Association of the endothelial activation and stress index with cognitive function in older adults: a cross-sectional study with machine learning

Association of the endothelial activation and stress index with cognitive function in older adults: a cross-sectional study with machine learning | 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 Research Article Association of the endothelial activation and stress index with cognitive function in older adults: a cross-sectional study with machine learning Zixu Wang, Yin Liu, Zhiliang Zhou, Hongwei Liu, Zhinan Ye This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7537562/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 06 Dec, 2025 Read the published version in European Journal of Medical Research → Version 1 posted 14 You are reading this latest preprint version Abstract Background Age-associated memory impairment (AAMI) is a predementia state linked to endothelial dysfunction. The endothelial activation and stress index (EASIX) quantifies endothelial injury, yet its association with cognitive function remains unvalidated in population studies. This study aimed to evaluate the relationship between EASIX and cognitive performance. Methods Data from adults aged ≥ 60 years in the NHANES 2011–2014 were analyzed. Multiple linear regression assessed associations between EASIX and cognitive function scores. LASSO regression selected variables, and six machine learning models (e.g., Random Forest, XGBoost) and two ensemble strategies were developed. SHAP values interpreted feature importance. Results Among 2,763 participants, EASIX showed a significant negative correlation with all cognitive scores (P < 0.05). The Random Forest model outperformed other models. SHAP analysis identified EASIX as one of the top five influential variables, with cognitive function levels demonstrating a declining trend as EASIX score increased, particularly among older adults. Conclusion EASIX is significantly negatively associated with cognitive function, especially in advanced age. It shows promise as a blood-based biomarker for early screening and risk assessment of cognitive decline, supporting its potential clinical utility. EASIX cognitive function cross-sectional study NHANES Figures Figure 1 Figure 2 Figure 3 1. Introduction With the acceleration of the aging of the global population [ 1 ] , age-associated memory impairment (AAMI) has become a significant public health issue [ 2 ] . In the absence of effective preventive and interventional measures, AAMI may progress to mild cognitive impairment (MCI) and ultimately develop into dementia [ 3 ] . Currently, approximately 50 million individuals worldwide live with dementia. This number is projected to rise to 74.7 million by 2030, with associated care costs potentially approaching $ 2 trillion [ 4 ] . As there are still no effective treatments for dementia, and the progression from cognitive decline to dementia is continuous and irreversible, early identification of relevant risk factors and implementation of interventions are crucial [ 5 ],[ 6 ] . Endothelial dysfunction, serving as an early biomarker of vascular pathology [ 7 ] , plays a key role in various cognitive impairment-related diseases, particularly in cerebral small vessel disease (cSVD) [ 8 ] and vascular dementia (VaD) [ 9 ] . Endothelial cells (ECs), which form the inner lining of blood vessels, are central regulators in maintaining vascular homeostasis [ 10 ] . In the brain, ECs not only constitute the major structure of the blood-brain barrier (BBB) but are also involved in neurovascular coupling (NVC), a process that ensures precise matching between regional cerebral blood flow (CBF) and neuronal activity [ 11 ] . Endothelial dysfunction can lead to the loss of these functions, severely disrupting cerebral physiology and serving as a core driver in the development and progression of cognitive impairment [ 12 ] . EASIX was initially developed to assess the degree of endothelial injury in patients after stem cell transplantation. It is calculated via three routine laboratory parameters: lactate dehydrogenase (LDH), creatinine, and platelet count (PLT) [ 13 ] . Multiple studies have demonstrated that EASIX is a reliable indicator for predicting endothelial dysfunction [ 14 ], [ 15 ], [ 16 ] . Given the close relationship between cognitive decline and endothelial dysfunction, we hypothesize that EASIX may serve as a predictive marker for cognitive deterioration. However, research exploring the association between EASIX and cognitive function remains limited. To address this gap, we conducted a cross-sectional study using data from the 2011–2014 survey cycles of the NHANES. 2. Methods 2.1 Study population Data were obtained from the 2011–2012 and 2013–2014 cycles of the National Health and Nutrition Examination Survey (NHANES), which included cognitive function assessments. The initial sample comprised 19,931 participants. Since cognitive evaluations in the NHANES were conducted only for individuals aged 60 years and above, the study population was restricted to this age group (n = 3,632). After excluding individuals with incomplete cognitive function scores (n = 698) and those lacking data on lactate dehydrogenase (LDH), platelet count (PLT), or creatinine (n = 171), a total of 2,763 participants were included in the final analysis. The participant selection process is illustrated in Supplementary Fig. 1 . 2.2 Endothelial Activation and Stress Index (EASIX) EASIX (Endothelial Activation and Stress Index) is a biomarker reflecting endothelial cell injury and systemic inflammatory status, calculated via the following formula: ​EASIX = [LDH (U/L) × Creatinine (mg/dL)] / PLT (10⁹/L)​. 2.3 Assessment of Cognitive Functions Cognitive function assessments in NHANES were only conducted during the 2011–2012 and 2013–2014 cycles. These tests were administered in Mobile Examination Centers (MECs) and included three core instruments the Consortium to Establish a Registry for Alzheimer’s Disease (CERAD) Word Learning Subtest, the Digit Symbol Substitution Test (DSST), and the Animal Fluency Test (AFT). The CERAD test assesses immediate and delayed recall of new verbal information. It consisted of three consecutive learning trials followed by a delayed recall test. In each trial, the examiner read a list of 10 words, and the participant was asked to recall as many words as possible immediately afterward. The delayed recall test was administered after a fill period involving other cognitive assessments. One point was awarded for each correctly recalled word. The total learning score ranged from 0 to 30, and the delayed recall score ranged from 0 to 10. The AFT measured categorical verbal fluency as part of executive function. Participants were instructed to name as many animals as possible within one minute. The DSST, adapted from the Wechsler Adult Intelligence Scale, evaluated processing speed, sustained attention, working memory, and associative learning. Participants were required to match symbols to numbers via a provided key within a two-minute period, with 133 items in total. Each correct match was scored one point, for a maximum possible score of 133. The cognitive score was defined as a comprehensive measure of the participants' cognitive abilities, calculated using the formula: Cognitive Score = \(\:{\sum\:}_{1}^{3}\left(x-Mean\right)/SD\) , x represents the raw score of each cognitive test, while Mean and SD denote the mean and standard deviation of the scores for the corresponding test, respectively. This standardization approach effectively reduces measurement bias that may arise from floor or ceiling effects by unifying the scales of different assessment tools and controlling for the influence of extreme values. 2.4 Covariates The analysis incorporated confounding variables that could influence the relationship between EASIX and cognitive performance, including ​age, ​sex, ​race/ethnicity​ (Mexican American, other Hispanic, non-Hispanic White, non-Hispanic Black, or additional racial groups including multiracial individuals), ​education level​ (less than high school, high school graduate or equivalent, or above high school), ​marital status​ (cohabitation or living alone), ​smoking status​ (individuals who smoked < 100 cigarettes during their lifetime were defined as never smokers; individuals who smoked ≥ 100 cigarettes were categorized as current smokers or former smokers based on current usage), ​alcohol intake​ (individuals who consumed < 12 alcoholic drinks in their lifetime were defined as never drinkers; individuals who consumed ≥ 12 alcoholic drinks in the past year were classified as current drinkers; others who consumed ≥ 12 drinks in their lifetime but not in the past year were classified as former drinkers), ​body mass index(BMI)​, the ratio of income to poverty(PIR)​, Self-reported diseases in the interviews included, hypertension, ​stroke, and ​diabetes. 2.5 Statistical analyses 2.5.1 Cross-sectional research design All statistical analyses in this study followed the recommendations of the Centers for Disease Control and Prevention (CDC) and appropriately applied NHANES sample weights. Categorical variables are presented as frequencies (percentages), whereas continuous variables are expressed as the means ± standard deviation (means ± SD). EASIX was analysed both as a continuous variable and as a quartiles. The Kruskal–Wallis test (for continuous variables) and chi-square test (for categorical variables) were used to compare baseline characteristics across EASIX quartile groups. Multivariable linear regression was employed to examine the associations between EASIX and cognitive function scores. Three progressively adjusted models were constructed: Model 1(unadjusted) was used for the preliminary assessment of the association between EASIX and cognitive function, Model 2 was further adjusted for demographic variables such as age, gender, and race; Model 3 (adjusted for all covariates) was employed to more comprehensively control for confounding bias and enhance the robustness of the results. To further investigate the heterogeneity of the relationship between EASIX and cognitive function across different populations, this study conducted subgroup analyses stratified by gender, educational level, PIR, hypertension, stroke, and diabetes. Interaction terms were included and tested within these subgroups. All the statistical analyses were performed via R software (version 4.5.0) and the EmpowerStats platform. 2.5.2 Machine learning This study utilized the glmnet package in the R programming environment to construct a LASSO regression model to identify significant predictors associated with the cognitive score. A total of 13 candidate predictors were included: age, sex, race, educational level, marital status, smoking status, alcohol intake, BMI, PIR, hypertension, stroke, diabetes, and EASIX. In this study, the LASSO regression model was implemented via the ​glmnet​ package in the R environment to identify significant predictors associated with the total cognitive function score. A total of 13 candidate predictors were included: age, sex, race, educational level, marital status, smoking status, alcohol consumption, body mass index (BMI), poverty-income ratio (PIR), hypertension, stroke, diabetes, and EASIX. The cognitive score was treated as a continuous dependent variable. During model training, 10-fold cross-validation was performed via the ​cv.glmnet()​​ function to identify the optimal regularization parameter λ that minimized the mean squared error (MSE). The model was refitted using this optimal λ value, and regression coefficients for all variables were extracted. LASSO regression was used for feature selection to identify core predictors significantly associated with the total cognitive function score, while effectively addressing multicollinearity and promoting sparsity by shrinking redundant variables. To further evaluate the predictive performance, six machine learning models were constructed on the basis of the features selected by LASSO, including Random Forest, LightGBM, XGBoost, CatBoost, Gradient Boosting, and ElasticNet, as well as two ensemble strategies: Stacking and Weighted Average Ensemble. All models underwent hyperparameter tuning via 10-fold cross-validation and were evaluated on an independent test set. The final optimal model was selected on the basis of comprehensive performance metrics for subsequent analysis. 3. Results 3.1 Cross-sectional research design 3.1.1 Baseline characteristics A total of 2,763 participants were included in this study, with a mean age of 69.5 ± 6.8 years, and 49.1% were male. Non-Hispanic Whites accounted for 49.0%, 51% had education above high school level, and 58.0% were cohabiting. The mean scores for CERAD, AFT, and DSST were 24.9 ± 6.5, 16.6 ± 5.5, and 45.9 ± 17.3, respectively. When grouped by EASIX quartiles, most baseline characteristics were significantly different among the groups (p < 0.05). Compared with the Q1 group, the Q4 group had a greater proportion of males, non-Hispanic Blacks, and individuals with education levels below high school, a heavier burden of chronic diseases, and lower cognitive function scores. According to the EASIX quartiles, the baseline characteristics are presented in Table 1 . 3.1.2 Association between EASIX and cognitive function scores In the fully adjusted model (Model 3), a significant negative association was observed between EASIX and cognitive scores. For each unit increase in EASIX, the CERAD total score, AFT score, and DSST score decreased by 0.649, 0.729, and 2.171 units, respectively. Quartile analysis further demonstrated that compared with the Q1 group, the Q4 group presented significantly lower scores across all the cognitive function assessments ( p < 0.05) ( Table 2 ). 3.1.3 Relationships between EASIX and cognitive function scores Threshold effect analysis ( Supplementary Table 1 ) revealed a significant negative correlation between the EASIX and various cognitive function indicators. Smooth curve fitting ( Supplementary Figure 2 ) further confirmed monotonically decreasing relationships between the EASIX and all cognitive function scores, with higher estimation precision at elevated EASIX scores. 3.1.4 Subgroup analyses To assess the reliability of the relationship between EASIX and cognitive function, we conducted a stratified analysis based on the following subgroups: gender, educational level, PIR, hypertension, diabetes, and stroke.We also tested for interactions among these subgroups. The results showed that negative associations between EASIX and all cognitive function scores were consistently observed in all stratified subgroups ( Supplementary Table2 ). 3.2 Machine learning 3.2.1 Variable Selection and Regularization The regularization parameter λ was optimized through 10-fold cross-validation. The cross-validation deviation curve ( Supplementary Figure 3 ) shows that MSE initially decreased and then increased as -log(λ) increased. . Following the "minimum deviation + 1 standard error (1-SE)" rule (orange dashed line in the figure), the optimal λ value was selected as 0.139 (corresponding to log(λ) = -1.971). The variable shrinkage path ( Figure 1 ) showed that the coefficients of all 13 variables were retained (none compressed to zero), with education level and PIR exhibiting the largest absolute coefficient values, suggesting their strong predictive power for the total cognitive function score. 3.2.2 Model Performance Validation Six machine learning models (Random Forest, LightGBM, XGBoost, CatBoost, Gradient Boosting, and ElasticNet) and two ensemble models (Stacking ensemble, weighted average ensemble) were constructed on the basis of the 13 key predictors selected by LASSO. All the models underwent hyperparameter tuning on the training set and were evaluated on an independent test set. Comparative performance analysis ( Supplementary Table3 ) confirmed that the Random Forest model achieved the best performance; thus, subsequent feature importance analysis and EASIX effect exploration were conducted with this model. 3.2.3 Evaluation of the importance of variables SHAP value analysis was used to assess the contribution of each feature to the predictions of the Random Forest model. The SHAP summary plot ( Figure 2 ) showed the contributions of all features, ranked in descending order of mean absolute SHAP values. Education level had the highest predictive importance, followed by age, race, PIR, and EASIX. The feature importance ranking ( Supplementary Figure 4 ) further visually emphasized the order of feature significance on the basis of SHAP values. To further explore the relationship between the EASIX and cognitive function, a SHAP partial dependence plot ( Supplementary Figure 5 ) was generated. The results showed that as EASIX score increased, the predicted value of cognitive function exhibited a transition from initial fluctuations to a relatively stable decline. The heatmap of the interaction between EASIX and age ( Figure 3 ) further revealed that the association between elevated the EASIX score and cognitive decline was more pronounced in older individuals. Box plots ( Supplementary Figure 6 ) showed the distribution of cognitive function grouped by EASIX score, with the median decreasing as EASIX increases, providing population-level support for the aforementioned association. 4. Discussion In this retrospective study, we assessed the relationship between EASIX and cognitive function. After comprehensive adjustment for potential confounding factors that may influence cognitive performance, a significant negative correlation was observed between participants' EASIX score and cognitive function. Quartile analysis further supported these findings, with significantly lower cognitive function scores in the Q4 group than in the Q1 group. These findings suggest that EASIX may serve as an effective biomarker for assessing cognitive function. The findings were further validated and expanded through machine learning approaches. LASSO regression was used to screen 13 key variables, confirming the importance of EASIX in predicting cognitive function. Among the various machine learning models constructed on the basis of the screened features, Random Forest showed the best performance. SHAP analysis verified EASIX as a significant predictor, with cognitive function levels showing a declining trend as EASIX scores increased. Interaction effect analysis further indicated that the association between elevated EASIX and cognitive decline was more pronounced in older individuals. EASIX is calculated via LDH, creatinine, and PLT, aiming to quantify endothelial dysfunction and systemic inflammatory status [ 17 ] . Its core value lies in integrating information across three dimensions: endothelial cell injury, renal dysfunction, and coagulation system activation, providing a convenient and effective window for assessing systemic endothelial health [ 18 ],[ 19 ] . Its clinical application has extended beyond the original field of transplantation medicine and has been validated as a robust prognostic marker in cardiovascular diseases [ 20 ] , oncology [ 21 ] , critical care [ 22 ] , and other areas. In patients with chronic heart failure, an EASIX ≥ 2.32 was associated with a 2–3 times higher mortality risk within 5 years, and elevated EASIX consistently predicted reduced overall survival regardless of heart failure subtype or symptom severity [ 23 ] . A study on traumatic brain injury found that when EASIX > 2.12, the sensitivity for predicting 30-day mortality reached 88.64%, and it showed a negative correlation with platelet function (r = -0.58) [ 24 ] . Understanding the pathological significance of each component of EASIX helps elucidate its potential link with cognitive decline. The release of LDH serves as a key marker of NLRP3 inflammasome-mediated pyroptosis, and elevated LDH levels are directly correlated with neuroinflammation and cognitive impairment in epilepsy models [ 25 ] . In a study on COVID-19-related cognitive dysfunction, only LDH levels were negatively correlated with the neurocognitive composite score [ 26 ] . Elevated creatinine levels are a hallmark of chronic kidney disease (CKD) [ 27 ] , which is often accompanied by endothelial dysfunction [ 28 ] and cognitive decline [ 29 ] . Platelets are considered a "peripheral mirror" of Alzheimer's disease pathology and may directly contribute to cognitive impairment by promoting cerebrovascular amyloid-β deposition, neuroinflammation, and oxidative stress [ 30 ], [ 31 ] . The negative correlation between EASIX and cognitive function observed in this study may be attributed to the following underlying mechanisms: elevated EASIX levels indicate systemic endothelial dysfunction and inflammatory states, which influence the brain microenvironment through multiple intertwined pathways, ultimately leading to cognitive impairment. A direct consequence of systemic endothelial dysfunction is impaired neurovascular coupling (NVC) [ 32 ] .Dysfunctional endothelial cells release signaling molecules that cross the blood-brain barrier (BBB) into the brain parenchyma, causing localized cerebral microvascular endothelial dysfunction and directly compromising NVC function11. This reduces blood supply to regions of neuronal activity, resulting in functional ischemia that impairs synaptic activity and neuronal metabolism [ 33 ] . This mechanism aligns with the high sensitivity of DSST and AFT scores to EASIX changes observed in the present study. Systemic endothelial dysfunction is often accompanied by disruption of BBB integrity [ 34 ] , manifested by cleavage of tight junction proteins [ 35 ] . Increased BBB permeability allows peripheral inflammatory mediators (e.g., IL-6, and TNF-α) [ 36 ] and neurotoxic substances [ 37 ] to enter the brain parenchyma, activating microglia and triggering neuroinflammation [ 38 ] . Neuroinflammation accelerates cognitive decline by promoting synaptic phagocytosis [ 39 ] , neuronal damage [ 40 ] , and white matter lesions [ 41 ] . The decrease in PLT reflects widespread microcirculatory disturbances, leading to reduced vascular regulatory capacity and microvascular thrombosis [ 42 ] . This induces chronic cerebral hypoperfusion, exacerbating neuronal injury through tissue hypoxia, oxidative stress, and energy metabolism crises [ 43 ] . Based on the NHANES data, this study employed a complex sampling design and weighted analysis, ensuring that the results are well-representative of the older adult population in the United States. It is the first to validate the association between EASIX and cognitive function, providing new insights for early non-invasive screening of cognitive decline. The calculation of EASIX based on routine laboratory parameters is simple and facilitates clinical implementation. However, this study has certain limitations. As a cross-sectional analysis, it causal inference limitations. Although a significant association was observed between EASIX and cognitive function, the causal relationship between endothelial dysfunction and cognitive decline cannot be definitively established. Prospective cohort studies are needed to validate the predictive value of the EASIX for cognitive impairment. While the NHANES cognitive test battery covers core cognitive domains, its sensitivity to mild cognitive changes may be lower than that of comprehensive neuropsychological assessments. Furthermore, despite adjustment for multiple covariates, residual confounding factors may still exist. 5. Conclusion Based on the NHANES data, this study systematically investigated the relationship between EASIX and cognitive function in middle-aged and older adults through a combination of cross-sectional analysis and machine learning model. The results revealed significant negative correlations between EASIX and multiple cognitive function scores, with stronger effects observed in older population subgroups. These findings suggest that EASIX may serve as an effective and readily available blood biomarker reflecting endothelial dysfunction, providing a novel tool for early identification and risk stratification of cognitive decline. These machine learning analyses strengthen the association between EASIX and cognitive decline from a predictive model perspective, facilitating the identification of high-risk individuals for early intervention. Declarations Funding This work was supported by a grant from the Medical Health Science and Technology Project of Zhejiang Provincial Health Commission (No. 2025KY459). Author Contributions Zixu Wang: conceptualization, investigation, software, formal analysis, data curation, writing - review and editing, writing - original draft, visualization. Yin Liu: conceptualization, writing – review and editing. Zhiliang Zhou: supervision. Hongwei Liu: supervision. Zhinan Ye: supervision. Acknowledgments We gratefully acknowledge the National Health and Nutrition Examination Survey (NHANES) for providing access to the research data. We extend our sincere appreciation to all study participants for their contributions. Ethics Statement The study protocol received approval from the National Center for Health Statistics Research Ethics Review Board to ensure adherence to ethical standards. Written informed consent was obtained from each participant prior to their enrollment in the research. Conflicts of Interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. References Nations U: World Population Ageing 2023. In. Edited by New York N, USA; 2023. Mir FA, Amanullah A, Jain BP, Hyderi Z, Gautam A: Neuroepigenetics of ageing and neurodegeneration-associated dementia: An updated review. Ageing Res Rev 2023, 91:102067. https://doi.org/10.1016/j.arr.2023.102067 Vega JN, Newhouse PA: Mild cognitive impairment: diagnosis, longitudinal course, and emerging treatments. Curr Psychiatry Rep 2014, 16(10):490. https://doi.org/10.1007/s11920-014-0490-8 Cummings J, Aisen PS, DuBois B, Frölich L, Jack CR, Jr., Jones RW, Morris JC, Raskin J, Dowsett SA, Scheltens P: Drug development in Alzheimer's disease: the path to 2025. Alzheimers Res Ther 2016, 8:39. https://doi.org/10.1186/s13195-016-0207-9 Vaz M, Silvestre S: Alzheimer's disease: Recent treatment strategies. Eur J Pharmacol 2020, 887:173554. https://doi.org/10.1016/j.ejphar.2020.173554 Morgan AE, Mc Auley MT: Vascular dementia: From pathobiology to emerging perspectives. Ageing Res Rev 2024, 96:102278. https://doi.org/10.1016/j.arr.2024.102278 Tarantini S, Valcarcel-Ares MN, Toth P, Yabluchanskiy A, Tucsek Z, Kiss T, Hertelendy P, Kinter M, Ballabh P, Süle Z et al : Nicotinamide mononucleotide (NMN) supplementation rescues cerebromicrovascular endothelial function and neurovascular coupling responses and improves cognitive function in aged mice. Redox Biol 2019, 24:101192. https://doi.org/10.1016/j.redox.2019.101192 Wu LY, Chai YL, Cheah IK, Chia RSL, Hilal S, Arumugam TV, Chen CP, Lai MKP: Blood-based biomarkers of cerebral small vessel disease. Ageing Res Rev 2024, 95:102247. https://doi.org/10.1016/j.arr.2024.102247 Goncharov NV, Popova PI, Kudryavtsev IV, Golovkin AS, Savitskaya IV, Avdonin PP, Korf EA, Voitenko NG, Belinskaia DA, Serebryakova MK et al : Immunological Profile and Markers of Endothelial Dysfunction in Elderly Patients with Cognitive Impairments. Int J Mol Sci 2024, 25(3). https://doi.org/10.3390/ijms25031888 Quick S, Moss J, Rajani RM, Williams A: A Vessel for Change: Endothelial Dysfunction in Cerebral Small Vessel Disease. Trends Neurosci 2021, 44(4):289–305. https://doi.org/10.1016/j.tins.2020.11.003 Smith BC, Tinkey RA, Shaw BC, Williams JL: Targetability of the neurovascular unit in inflammatory diseases of the central nervous system. Immunol Rev 2022, 311(1):39–49. https://doi.org/10.1111/imr.13121 Pantoni L: Cerebral small vessel disease: from pathogenesis and clinical characteristics to therapeutic challenges. Lancet Neurol 2010, 9(7):689–701. https://doi.org/10.1016/s1474-4422(10)70104-6 Luft T, Benner A, Jodele S, Dandoy CE, Storb R, Gooley T, Sandmaier BM, Becker N, Radujkovic A, Dreger P et al : EASIX in patients with acute graft-versus-host disease: a retrospective cohort analysis. Lancet Haematol 2017, 4(9):e414–e423. https://doi.org/10.1016/s2352-3026(17)30108-4 Park S, Go SI, Lee GW: The Endothelial Activation and Stress Index (EASIX) score is an independent prognostic factor in patients with diffuse large B-cell lymphoma. BMC Cancer 2022, 22(1):816. https://doi.org/10.1186/s12885-022-09915-4 Xu HB, Ye Y, Xue F, Wu J, Suo Z, Zhang H: Association Between Endothelial Activation and Stress Index and 28-Day Mortality in Septic ICU patients: a Retrospective Cohort Study. Int J Med Sci 2023, 20(9):1165–1173. https://doi.org/10.7150/ijms.85870 Schult D, Rasch S, Schmid RM, Lahmer T, Mayr U: EASIX Is an Accurate and Easily Available Prognostic Score in Critically Ill Patients with Advanced Liver Disease. J Clin Med 2023, 12(7). https://doi.org/10.3390/jcm12072553 Luft T, Wendtner CM, Kosely F, Radujkovic A, Benner A, Korell F, Kihm L, Bauer MF, Dreger P, Merle U: EASIX for Prediction of Outcome in Hospitalized SARS-CoV-2 Infected Patients. Front Immunol 2021, 12:634416. https://doi.org/10.3389/fimmu.2021.634416 Korell F, DeFilipp Z, Schreck N, Luft T: Validation of pre-conditioning EASIX for prediction of sepsis after allogeneic stem cell transplantation. Intensive Care Med 2023, 49(11):1408–1410. https://doi.org/10.1007/s00134-023-07193-7 Jiang S, Penack O, Terzer T, Schult D, Majer-Lauterbach J, Radujkovic A, Blau IW, Bullinger L, Müller-Tidow C, Dreger P et al : Predicting sinusoidal obstruction syndrome after allogeneic stem cell transplantation with the EASIX biomarker panel. Haematologica 2021, 106(2):446–453. https://doi.org/10.3324/haematol.2019.238790 Yin F, Wang K: Independent prognostic importance of endothelial activation and stress index (EASIX) in critically ill patients with heart failure: modulating role of inflammation. Front Med (Lausanne) 2025, 12:1560947. https://doi.org/10.3389/fmed.2025.1560947 Frenking JH, Zhou X, Wagner V, Hielscher T, Kauer J, Mai EK, Friedrich MJ, Michel CS, Hajiyianni M, Breitkreutz I et al : EASIX-guided risk stratification for complications and outcome after CAR T-cell therapy with ide-cel in relapsed/refractory multiple myeloma. J Immunother Cancer 2024, 12(10) .https://doi.org/10.1136/jitc-2024-009220 Wang J, Chen X, Qin C, Zeng X, Du X, Wang D: The endothelial activation and stress index is a potential prognostic indicator for patients with acute pancreatitis managed in the intensive care unit: a retrospective study. Front Med (Lausanne) 2024, 11:1498148. https://doi.org/10.3389/fmed.2024.1498148 Estler B, Fröhlich H, Täger T, Hund H, Frey N, Luft T, Frankenstein L: Endothelial dysfunction assessed by the endothelial activation and stress index (EASIX) predicts risk of mortality in chronic heart failure patients. Int J Cardiol 2025, 438:133566. https://doi.org/10.1016/j.ijcard.2025.133566 Băetu AE, Mirea LE, Cobilinschi C, Grințescu IC, Grințescu IM: Platelet Contribution and Endothelial Activation and Stress Index-Potential Mortality Predictors in Traumatic Brain Injury. Int J Mol Sci 2024, 25(14). https://doi.org/10.3390/ijms25147763 Wang L, Ding J, Zhu C, Guo B, Yang W, He W, Li X, Wang Y, Li W, Wang F et al : Semaglutide attenuates seizure severity and ameliorates cognitive dysfunction by blocking the NLR family pyrin domain containing 3 inflammasome in pentylenetetrazole–kindled mice. Int J Mol Med 2021, 48(6). https://doi.org/10.3892/ijmm.2021.5052 Galderisi S, Perrottelli A, Giuliani L, Pisaturo MA, Monteleone P, Pagliano P, Vita A, Muiesan ML, Amore M, Bassetti M et al : Cognitive impairment after recovery from COVID-19: Frequency, profile, and relationships with clinical and laboratory indices. Eur Neuropsychopharmacol 2024, 79:22–31. https://doi.org/10.1016/j.euroneuro.2023.11.001 Van Biesen W, Vanholder R, Veys N, Verbeke F, Delanghe J, De Bacquer D, Lameire N: The importance of standardization of creatinine in the implementation of guidelines and recommendations for CKD: implications for CKD management programmes. Nephrol Dial Transplant 2006, 21(1):77–83. https://doi.org/10.1093/ndt/gfi185 Cho ME, Brunt VE, Shiu YT, Bunsawat K: Endothelial dysfunction in chronic kidney disease: a clinical perspective. Am J Physiol Heart Circ Physiol 2025, 329(1):H135–h153. https://doi.org/10.1152/ajpheart.00908.2024 Viggiano D, Wagner CA, Martino G, Nedergaard M, Zoccali C, Unwin R, Capasso G: Mechanisms of cognitive dysfunction in CKD. Nat Rev Nephrol 2020, 16(8):452–469. https://doi.org/10.1038/s41581-020-0266-9 Talib LL, Joaquim HP, Forlenza OV: Platelet biomarkers in Alzheimer's disease. World J Psychiatry 2012, 2(6):95–101. https://doi.org/10.5498/wjp.v2.i6.95 Ramos-Cejudo J, Johnson AD, Beiser A, Seshadri S, Salinas J, Berger JS, Fillmore NR, Do N, Zheng C, Kovbasyuk Z et al : Platelet Function Is Associated With Dementia Risk in the Framingham Heart Study. J Am Heart Assoc 2022, 11(9):e023918 .https://doi.org/10.1161/jaha.121.023918 Harraz OF, Klug NR, Senatore AJ, Hill-Eubanks DC, Nelson MT: Piezo1 Is a Mechanosensor Channel in Central Nervous System Capillaries. Circ Res 2022, 130(10):1531–1546. https://doi.org/10.1161/circresaha.122.320827 Chen YX, Yang H, Wang DS, Chen TT, Qi XL, Tao L, Chen Y, Shen XC: Gastrodin alleviates mitochondrial dysfunction by regulating SIRT3-mediated TFAM acetylation in vascular dementia. Phytomedicine 2024, 128:155369. https://doi.org/10.1016/j.phymed.2024.155369 Ohtsuki S, Sato S, Yamaguchi H, Kamoi M, Asashima T, Terasaki T: Exogenous expression of claudin-5 induces barrier properties in cultured rat brain capillary endothelial cells. J Cell Physiol 2007, 210(1):81–86. https://doi.org/10.1002/jcp.20823 Liu J, Weaver J, Jin X, Zhang Y, Xu J, Liu KJ, Li W, Liu W: Nitric Oxide Interacts with Caveolin-1 to Facilitate Autophagy-Lysosome-Mediated Claudin-5 Degradation in Oxygen-Glucose Deprivation-Treated Endothelial Cells. Mol Neurobiol 2016, 53(9):5935–5947. https://doi.org/10.1007/s12035-015-9504-8 Camire RB, Beaulac HJ, Willis CL: Transitory loss of glia and the subsequent modulation in inflammatory cytokines/chemokines regulate paracellular claudin-5 expression in endothelial cells. J Neuroimmunol 2015, 284:57–66. https://doi.org/10.1016/j.jneuroim.2015.05.008 Alkhalifa AE, Al-Ghraiybah NF, Odum J, Shunnarah JG, Austin N, Kaddoumi A: Blood-Brain Barrier Breakdown in Alzheimer's Disease: Mechanisms and Targeted Strategies. Int J Mol Sci 2023, 24(22). https://doi.org/10.3390/ijms242216288 Nimmerjahn A, Kirchhoff F, Helmchen F: Resting microglial cells are highly dynamic surveillants of brain parenchyma in vivo. Science 2005, 308(5726):1314–1318. https://doi.org/10.1126/science.1110647 Caplan LR: Lacunar infarction and small vessel disease: pathology and pathophysiology. J Stroke 2015, 17(1):2–6 .https://doi.org/10.5853/jos.2015.17.1.2 Zlokovic BV: Neurovascular pathways to neurodegeneration in Alzheimer's disease and other disorders. Nat Rev Neurosci 2011, 12(12):723–738. https://doi.org/10.1038/nrn3114 Kan CN, Gyanwali B, Hilal S, Ng KP, Venketasubramanian N, Chen CL, Xu X: Neuropsychiatric Correlates of Small Vessel Disease Progression in Incident Cognitive Decline: Independent and Interactive Effects. J Alzheimers Dis 2020, 73(3):1053–1062. https://doi.org/10.3233/jad-190999 Nezu T, Hosomi N, Aoki S, Kubo S, Araki M, Mukai T, Takahashi T, Maruyama H, Higashi Y, Matsumoto M: Endothelial dysfunction is associated with the severity of cerebral small vessel disease. Hypertens Res 2015, 38(4):291–297. https://doi.org/10.1038/hr.2015.4 Tian Z, Ji X, Liu J: Neuroinflammation in Vascular Cognitive Impairment and Dementia: Current Evidence, Advances, and Prospects. Int J Mol Sci 2022, 23(11). https://doi.org/10.3390/ijms23116224 Tables Table 1 to 2 are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Table12.docx SupplementaryTable13.docx SupplementaryFigure16.docx Cite Share Download PDF Status: Published Journal Publication published 06 Dec, 2025 Read the published version in European Journal of Medical Research → Version 1 posted Editorial decision: Revision requested 07 Oct, 2025 Reviews received at journal 27 Sep, 2025 Reviews received at journal 26 Sep, 2025 Reviews received at journal 24 Sep, 2025 Reviewers agreed at journal 22 Sep, 2025 Reviewers agreed at journal 21 Sep, 2025 Reviewers agreed at journal 19 Sep, 2025 Reviews received at journal 19 Sep, 2025 Reviewers agreed at journal 19 Sep, 2025 Reviewers agreed at journal 19 Sep, 2025 Reviewers invited by journal 18 Sep, 2025 Editor assigned by journal 12 Sep, 2025 Submission checks completed at journal 11 Sep, 2025 First submitted to journal 04 Sep, 2025 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-7537562","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":521516277,"identity":"e88a8d8f-91e9-4f3a-b4b7-4eb7b6410795","order_by":0,"name":"Zixu Wang","email":"","orcid":"","institution":"Tianjin University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Zixu","middleName":"","lastName":"Wang","suffix":""},{"id":521516278,"identity":"fe3a465e-7ec6-42c5-bddb-55aca0d79e91","order_by":1,"name":"Yin Liu","email":"","orcid":"","institution":"Tianjin University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yin","middleName":"","lastName":"Liu","suffix":""},{"id":521516279,"identity":"a953328d-fa61-43c4-a3e2-8cc836d986a2","order_by":2,"name":"Zhiliang Zhou","email":"","orcid":"","institution":"University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Zhiliang","middleName":"","lastName":"Zhou","suffix":""},{"id":521516280,"identity":"08d0fbaf-2894-4d32-8dcd-53aa2db72b65","order_by":3,"name":"Hongwei Liu","email":"","orcid":"","institution":"Taiyuan City Central Hospital, The Ninth Clinical Medical College of Shanxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Hongwei","middleName":"","lastName":"Liu","suffix":""},{"id":521516281,"identity":"bfe951ae-b429-482e-a4cb-16b95d8a8811","order_by":4,"name":"Zhinan Ye","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5klEQVRIie3QsQqCQBjA8e8QbJFcTwJ9hRMhGnqYk9aIoMVB6guhqXadegV9AyWwxfYbk54gGmpo6KqhTR0b7g8Hx8GPu/sAVKo/zImNIQVGbbMX5d+jvIUw8SbByLO2pY/dCNUnDKrAZ2LqdiVacSYbyi2c3utHCHZfcHKbN/6FYC7JzIRThrQEzxJcG8RN14gvWVjrXYbyaX4quK4ZHYifHowa5QBW3QhUkpQGwQKBszbCPiSgcsi6m2BJ3aSqo0ETcfaFJGxpm87lfH2EY6d/nBS3xofJtOdvT+Ui2AJUKpVK1dYL1aFQrpaWUrQAAAAASUVORK5CYII=","orcid":"","institution":"Taizhou Municipal Hospital (Taizhou University Affiliated Municipal Hospital), Taizhou University","correspondingAuthor":true,"prefix":"","firstName":"Zhinan","middleName":"","lastName":"Ye","suffix":""}],"badges":[],"createdAt":"2025-09-04 15:23:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7537562/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7537562/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s40001-025-03512-4","type":"published","date":"2025-12-06T15:58:23+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":92478172,"identity":"3f5e8078-6e90-48d5-bfd5-cde9aa93bcea","added_by":"auto","created_at":"2025-09-30 07:27:35","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":110042,"visible":true,"origin":"","legend":"","description":"","filename":"AssociationoftheEASIXwithcognitivefunctioninolderadults.docx","url":"https://assets-eu.researchsquare.com/files/rs-7537562/v1/1c8bf1a981c4da246670fb59.docx"},{"id":92478173,"identity":"18ce051f-af52-4cfc-afce-c618a93adb78","added_by":"auto","created_at":"2025-09-30 07:27:35","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":13293502,"visible":true,"origin":"","legend":"","description":"","filename":"Figure13.docx","url":"https://assets-eu.researchsquare.com/files/rs-7537562/v1/2f421e160ef6a0e1c1c4dfa7.docx"},{"id":92478158,"identity":"4e8e565f-cf9d-4dd9-9283-65b08ad8d98b","added_by":"auto","created_at":"2025-09-30 07:27:35","extension":"json","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":6620,"visible":true,"origin":"","legend":"","description":"","filename":"90aa3d7eec13489da3e87b18fad18639.json","url":"https://assets-eu.researchsquare.com/files/rs-7537562/v1/dc0418fcc7e61a3268810838.json"},{"id":92478171,"identity":"524f8813-08a1-4ffb-a6fe-b6dcecf82a5a","added_by":"auto","created_at":"2025-09-30 07:27:35","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":29469974,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure16.docx","url":"https://assets-eu.researchsquare.com/files/rs-7537562/v1/65a94fda3d795a04460fb0dd.docx"},{"id":92478865,"identity":"f5e941af-cbcd-4cc1-aa3b-d898882e7492","added_by":"auto","created_at":"2025-09-30 07:35:35","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":28691,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable13.docx","url":"https://assets-eu.researchsquare.com/files/rs-7537562/v1/a0da3dbd6a38f1de0b2c5db3.docx"},{"id":92478868,"identity":"dbb6001f-a6a6-46ab-aa85-f399d30fba90","added_by":"auto","created_at":"2025-09-30 07:35:35","extension":"xml","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":166189,"visible":true,"origin":"","legend":"","description":"","filename":"90aa3d7eec13489da3e87b18fad186391enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7537562/v1/0a000a2de14b45781616c735.xml"},{"id":92478867,"identity":"7e015290-cebf-4f0d-808d-77232fd0a968","added_by":"auto","created_at":"2025-09-30 07:35:35","extension":"jpeg","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":3203880,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7537562/v1/e1e40141aca121214380ffa7.jpeg"},{"id":92478869,"identity":"00d87b5c-67a3-441b-a340-40e58d04103b","added_by":"auto","created_at":"2025-09-30 07:35:35","extension":"jpeg","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":7332044,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7537562/v1/bab94f30003121523a8e72f1.jpeg"},{"id":92478167,"identity":"e4db4549-91d1-4337-a8b7-34857fb0e7ed","added_by":"auto","created_at":"2025-09-30 07:27:35","extension":"jpeg","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":2739216,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7537562/v1/5d76205e0c33695e7a5c2f89.jpeg"},{"id":92478165,"identity":"c37fa710-f6e7-4f26-bdc7-6b9d8b77d4b9","added_by":"auto","created_at":"2025-09-30 07:27:35","extension":"xml","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":166389,"visible":true,"origin":"","legend":"","description":"","filename":"90aa3d7eec13489da3e87b18fad186391structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7537562/v1/59cb33a3ca210c6fcb2bdac4.xml"},{"id":92478169,"identity":"5c450ad2-9bcb-424b-a85f-c73d65395c50","added_by":"auto","created_at":"2025-09-30 07:27:35","extension":"html","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":177800,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7537562/v1/dfaa35934f3ad68c5e528f33.html"},{"id":92478864,"identity":"cf758f81-3945-40db-863b-fef8b162bfdf","added_by":"auto","created_at":"2025-09-30 07:35:35","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":580050,"visible":true,"origin":"","legend":"\u003cp\u003eLASSO Coefficient Shrinkage Path.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations:\u003c/strong\u003e EASIX, endothelial activation and stress index. BMI, ​body mass index; PIR, the ratio of income to poverty.\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7537562/v1/e642a222e41fcc4accdc9fc0.png"},{"id":92478163,"identity":"24644fe3-c8e0-439c-9fe9-d100ea390edc","added_by":"auto","created_at":"2025-09-30 07:27:35","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":292419,"visible":true,"origin":"","legend":"\u003cp\u003eLASSO Coefficient Shrinkage Path.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations:\u003c/strong\u003e EASIX, endothelial activation and stress index. BMI, ​body mass index; PIR, the ratio of income to poverty.\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7537562/v1/883609b9e4d2b8c9a2a43cc1.png"},{"id":92480778,"identity":"96e3f18a-b631-4edd-ab3d-903042033ff9","added_by":"auto","created_at":"2025-09-30 07:43:35","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":389773,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmap of the interaction between EASIX and age.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations:\u003c/strong\u003e EASIX, endothelial activation and stress index.\u003c/p\u003e","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7537562/v1/08f0d50edd1c1d7e54233c9c.png"},{"id":97724321,"identity":"f7ca8b32-4e3e-43da-9e3c-9d5b36df82c5","added_by":"auto","created_at":"2025-12-08 16:12:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1644281,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7537562/v1/fb44ba3b-dd9c-4263-a8d0-019d97f805b6.pdf"},{"id":92478164,"identity":"ecd85e17-8b1c-43c9-ae15-b17281cc07fc","added_by":"auto","created_at":"2025-09-30 07:27:35","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":38612,"visible":true,"origin":"","legend":"","description":"","filename":"Table12.docx","url":"https://assets-eu.researchsquare.com/files/rs-7537562/v1/7401075a5bb8ca5951995aee.docx"},{"id":92478159,"identity":"3a3f5b12-bb6c-432b-8d7e-e445c98cc4e0","added_by":"auto","created_at":"2025-09-30 07:27:35","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":28691,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable13.docx","url":"https://assets-eu.researchsquare.com/files/rs-7537562/v1/f78c0a7dfb9de4c79b4edb14.docx"},{"id":92478175,"identity":"17a10f6d-2c8d-479d-b6db-e395f34786c1","added_by":"auto","created_at":"2025-09-30 07:27:36","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":29469974,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure16.docx","url":"https://assets-eu.researchsquare.com/files/rs-7537562/v1/0f095d202a198f5a2ea59203.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association of the endothelial activation and stress index with cognitive function in older adults: a cross-sectional study with machine learning","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eWith the acceleration of the aging of the global population\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e, age-associated memory impairment (AAMI) has become a significant public health issue\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. In the absence of effective preventive and interventional measures, AAMI may progress to mild cognitive impairment (MCI) and ultimately develop into dementia\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Currently, approximately 50\u0026nbsp;million individuals worldwide live with dementia. This number is projected to rise to 74.7\u0026nbsp;million by 2030, with associated care costs potentially approaching \u003cspan\u003e$\u003c/span\u003e2 trillion \u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. As there are still no effective treatments for dementia, and the progression from cognitive decline to dementia is continuous and irreversible, early identification of relevant risk factors and implementation of interventions are crucial\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e],[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eEndothelial dysfunction, serving as an early biomarker of vascular pathology\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e, plays a key role in various cognitive impairment-related diseases, particularly in cerebral small vessel disease (cSVD) \u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e and vascular dementia (VaD) \u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. Endothelial cells (ECs), which form the inner lining of blood vessels, are central regulators in maintaining vascular homeostasis\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. In the brain, ECs not only constitute the major structure of the blood-brain barrier (BBB) but are also involved in neurovascular coupling (NVC), a process that ensures precise matching between regional cerebral blood flow (CBF) and neuronal activity\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. Endothelial dysfunction can lead to the loss of these functions, severely disrupting cerebral physiology and serving as a core driver in the development and progression of cognitive impairment\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. EASIX was initially developed to assess the degree of endothelial injury in patients after stem cell transplantation. It is calculated via three routine laboratory parameters: lactate dehydrogenase (LDH), creatinine, and platelet count (PLT) \u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. Multiple studies have demonstrated that EASIX is a reliable indicator for predicting endothelial dysfunction\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. Given the close relationship between cognitive decline and endothelial dysfunction, we hypothesize that EASIX may serve as a predictive marker for cognitive deterioration. However, research exploring the association between EASIX and cognitive function remains limited. To address this gap, we conducted a cross-sectional study using data from the 2011\u0026ndash;2014 survey cycles of the NHANES.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Study population\u003c/h2\u003e\u003cp\u003eData were obtained from the 2011\u0026ndash;2012 and 2013\u0026ndash;2014 cycles of the National Health and Nutrition Examination Survey (NHANES), which included cognitive function assessments. The initial sample comprised 19,931 participants. Since cognitive evaluations in the NHANES were conducted only for individuals aged 60 years and above, the study population was restricted to this age group (n\u0026thinsp;=\u0026thinsp;3,632). After excluding individuals with incomplete cognitive function scores (n\u0026thinsp;=\u0026thinsp;698) and those lacking data on lactate dehydrogenase (LDH), platelet count (PLT), or creatinine (n\u0026thinsp;=\u0026thinsp;171), a total of 2,763 participants were included in the final analysis. The participant selection process is illustrated in \u003cb\u003eSupplementary Fig.\u0026nbsp;1\u003c/b\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Endothelial Activation and Stress Index (EASIX)\u003c/h2\u003e\u003cp\u003eEASIX (Endothelial Activation and Stress Index) is a biomarker reflecting endothelial cell injury and systemic inflammatory status, calculated via the following formula:\u003c/p\u003e\u003cp\u003e​EASIX = [LDH (U/L) \u0026times; Creatinine (mg/dL)] / PLT (10⁹/L)​.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Assessment of Cognitive Functions\u003c/h2\u003e\u003cp\u003eCognitive function assessments in NHANES were only conducted during the 2011\u0026ndash;2012 and 2013\u0026ndash;2014 cycles. These tests were administered in Mobile Examination Centers (MECs) and included three core instruments the Consortium to Establish a Registry for Alzheimer\u0026rsquo;s Disease (CERAD) Word Learning Subtest, the Digit Symbol Substitution Test (DSST), and the Animal Fluency Test (AFT). The CERAD test assesses immediate and delayed recall of new verbal information. It consisted of three consecutive learning trials followed by a delayed recall test. In each trial, the examiner read a list of 10 words, and the participant was asked to recall as many words as possible immediately afterward. The delayed recall test was administered after a fill period involving other cognitive assessments. One point was awarded for each correctly recalled word. The total learning score ranged from 0 to 30, and the delayed recall score ranged from 0 to 10. The AFT measured categorical verbal fluency as part of executive function. Participants were instructed to name as many animals as possible within one minute. The DSST, adapted from the Wechsler Adult Intelligence Scale, evaluated processing speed, sustained attention, working memory, and associative learning. Participants were required to match symbols to numbers via a provided key within a two-minute period, with 133 items in total. Each correct match was scored one point, for a maximum possible score of 133.\u003c/p\u003e\u003cp\u003eThe cognitive score was defined as a comprehensive measure of the participants' cognitive abilities, calculated using the formula: Cognitive Score = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\sum\\:}_{1}^{3}\\left(x-Mean\\right)/SD\\)\u003c/span\u003e\u003c/span\u003e, x represents the raw score of each cognitive test, while Mean and SD denote the mean and standard deviation of the scores for the corresponding test, respectively. This standardization approach effectively reduces measurement bias that may arise from floor or ceiling effects by unifying the scales of different assessment tools and controlling for the influence of extreme values.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Covariates\u003c/h2\u003e\u003cp\u003eThe analysis incorporated confounding variables that could influence the relationship between EASIX and cognitive performance, including ​age, ​sex, ​race/ethnicity​ (Mexican American, other Hispanic, non-Hispanic White, non-Hispanic Black, or additional racial groups including multiracial individuals), ​education level​ (less than high school, high school graduate or equivalent, or above high school), ​marital status​ (cohabitation or living alone), ​smoking status​ (individuals who smoked\u0026thinsp;\u0026lt;\u0026thinsp;100 cigarettes during their lifetime were defined as never smokers; individuals who smoked\u0026thinsp;\u0026ge;\u0026thinsp;100 cigarettes were categorized as current smokers or former smokers based on current usage), ​alcohol intake​ (individuals who consumed\u0026thinsp;\u0026lt;\u0026thinsp;12 alcoholic drinks in their lifetime were defined as never drinkers; individuals who consumed\u0026thinsp;\u0026ge;\u0026thinsp;12 alcoholic drinks in the past year were classified as current drinkers; others who consumed\u0026thinsp;\u0026ge;\u0026thinsp;12 drinks in their lifetime but not in the past year were classified as former drinkers), ​body mass index(BMI)​, the ratio of income to poverty(PIR)​, Self-reported diseases in the interviews included, hypertension, ​stroke, and ​diabetes.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Statistical analyses\u003c/h2\u003e\u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\u003ch2\u003e2.5.1 Cross-sectional research design\u003c/h2\u003e\u003cp\u003e All statistical analyses in this study followed the recommendations of the Centers for Disease Control and Prevention (CDC) and appropriately applied NHANES sample weights. Categorical variables are presented as frequencies (percentages), whereas continuous variables are expressed as the means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (means\u0026thinsp;\u0026plusmn;\u0026thinsp;SD). EASIX was analysed both as a continuous variable and as a quartiles. The Kruskal\u0026ndash;Wallis test (for continuous variables) and chi-square test (for categorical variables) were used to compare baseline characteristics across EASIX quartile groups. Multivariable linear regression was employed to examine the associations between EASIX and cognitive function scores. Three progressively adjusted models were constructed: Model 1(unadjusted) was used for the preliminary assessment of the association between EASIX and cognitive function, Model 2 was further adjusted for demographic variables such as age, gender, and race; Model 3 (adjusted for all covariates) was employed to more comprehensively control for confounding bias and enhance the robustness of the results. To further investigate the heterogeneity of the relationship between EASIX and cognitive function across different populations, this study conducted subgroup analyses stratified by gender, educational level, PIR, hypertension, stroke, and diabetes. Interaction terms were included and tested within these subgroups. All the statistical analyses were performed via R software (version 4.5.0) and the EmpowerStats platform.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\u003ch2\u003e2.5.2 Machine learning\u003c/h2\u003e\u003cp\u003eThis study utilized the glmnet package in the R programming environment to construct a LASSO regression model to identify significant predictors associated with the cognitive score. A total of 13 candidate predictors were included: age, sex, race, educational level, marital status, smoking status, alcohol intake, BMI, PIR, hypertension, stroke, diabetes, and EASIX. In this study, the LASSO regression model was implemented via the ​glmnet​ package in the R environment to identify significant predictors associated with the total cognitive function score. A total of 13 candidate predictors were included: age, sex, race, educational level, marital status, smoking status, alcohol consumption, body mass index (BMI), poverty-income ratio (PIR), hypertension, stroke, diabetes, and EASIX. The cognitive score was treated as a continuous dependent variable. During model training, 10-fold cross-validation was performed via the ​cv.glmnet()​​ function to identify the optimal regularization parameter λ that minimized the mean squared error (MSE). The model was refitted using this optimal λ value, and regression coefficients for all variables were extracted. LASSO regression was used for feature selection to identify core predictors significantly associated with the total cognitive function score, while effectively addressing multicollinearity and promoting sparsity by shrinking redundant variables. To further evaluate the predictive performance, six machine learning models were constructed on the basis of the features selected by LASSO, including Random Forest, LightGBM, XGBoost, CatBoost, Gradient Boosting, and ElasticNet, as well as two ensemble strategies: Stacking and Weighted Average Ensemble. All models underwent hyperparameter tuning via 10-fold cross-validation and were evaluated on an independent test set. The final optimal model was selected on the basis of comprehensive performance metrics for subsequent analysis.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cstrong\u003e3.1 Cross-sectional research design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.1.1 Baseline characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 2,763 participants were included in this study, with a mean age of 69.5 \u0026plusmn; 6.8 years, and 49.1% were male. Non-Hispanic Whites accounted for 49.0%, 51% had education above high school level, and 58.0% were cohabiting. The mean scores for CERAD, AFT, and DSST were 24.9 \u0026plusmn; 6.5, 16.6 \u0026plusmn; 5.5, and 45.9 \u0026plusmn; 17.3, respectively. When grouped by EASIX quartiles, most baseline characteristics were significantly different among the groups (p \u0026lt; 0.05). Compared with the Q1 group, the Q4 group had a greater proportion of males, non-Hispanic Blacks, and individuals with education levels below high school, a heavier burden of chronic diseases, and lower cognitive function scores. According to the EASIX quartiles, the baseline characteristics are presented in \u003cstrong\u003eTable 1\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.1.2 Association between EASIX and cognitive function scores\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the fully adjusted model (Model 3), a significant negative association was observed between EASIX and cognitive scores. For each unit increase in EASIX, the CERAD total score, AFT score, and DSST score decreased by 0.649, 0.729, and 2.171 units, respectively. Quartile analysis further demonstrated that compared with the Q1 group, the Q4 group presented significantly lower scores across all the cognitive function assessments (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05) (\u003cstrong\u003eTable 2\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.1.3 Relationships between EASIX and cognitive function scores\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThreshold effect analysis (\u003cstrong\u003eSupplementary Table 1\u003c/strong\u003e) revealed a significant negative correlation between the EASIX and various cognitive function indicators.\u0026nbsp;Smooth curve fitting (\u003cstrong\u003eSupplementary Figure\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e2\u003c/strong\u003e) further confirmed monotonically decreasing relationships between the EASIX and all cognitive function scores, with higher estimation precision at elevated EASIX scores.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.1.4 Subgroup analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo assess the reliability of the relationship between EASIX and cognitive function, we conducted a stratified analysis based on the following subgroups: gender, educational level, PIR, hypertension, diabetes, and stroke.We also tested for interactions among these subgroups. The results showed\u0026nbsp;that negative associations between EASIX and all cognitive function scores were consistently observed in all stratified subgroups (\u003cstrong\u003eSupplementary Table2\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Machine learning\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2.1 Variable Selection and Regularization\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe regularization parameter \u0026lambda; was optimized through 10-fold cross-validation.\u0026nbsp;The cross-validation deviation curve (\u003cstrong\u003eSupplementary\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Figure 3\u003c/strong\u003e)\u0026nbsp;shows that MSE initially decreased and then increased as -log(\u0026lambda;) increased.\u0026nbsp;. Following the \u0026quot;minimum deviation + 1 standard error (1-SE)\u0026quot; rule (orange dashed line in the figure), the optimal \u0026lambda; value was selected as 0.139 (corresponding to log(\u0026lambda;) = -1.971).\u0026nbsp;The variable shrinkage path (\u003cstrong\u003eFigure 1\u003c/strong\u003e) showed that the coefficients of all 13 variables were retained (none compressed to zero), with education level and PIR exhibiting the largest absolute coefficient values, suggesting their strong predictive power for the total cognitive function score.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2.2 Model Performance Validation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSix machine learning models (Random Forest, LightGBM, XGBoost, CatBoost, Gradient Boosting, and ElasticNet) and two ensemble models (Stacking ensemble, weighted average ensemble) were constructed on the basis of the 13 key predictors selected by LASSO.\u0026nbsp;All the models underwent hyperparameter tuning on the training set and were evaluated on an independent test set.\u0026nbsp;Comparative performance analysis (\u003cstrong\u003eSupplementary Table3\u003c/strong\u003e) confirmed that the Random Forest model achieved the best performance; thus, subsequent feature importance analysis and EASIX effect exploration were conducted with this model.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2.3 Evaluation of the importance of variables\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSHAP value analysis was used to assess the contribution of each feature to the predictions of the Random Forest model.\u0026nbsp;The SHAP summary plot (\u003cstrong\u003eFigure 2\u003c/strong\u003e) showed the contributions of all features, ranked in descending order of mean absolute SHAP values.\u0026nbsp;Education level had the highest predictive importance, followed by age, race, PIR, and EASIX.\u0026nbsp;The feature importance ranking (\u003cstrong\u003eSupplementary Figure 4\u003c/strong\u003e) further visually emphasized the order of feature significance on the basis of SHAP values.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo further explore the relationship between the EASIX and cognitive function,\u0026nbsp;a SHAP partial dependence plot (\u003cstrong\u003eSupplementary Figure 5\u003c/strong\u003e) was generated.\u0026nbsp;The results showed that as EASIX score increased, the predicted value of cognitive function exhibited a transition from initial fluctuations to a relatively stable decline.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe heatmap of the interaction between EASIX and age (\u003cstrong\u003eFigure 3\u003c/strong\u003e) further revealed that the association between elevated the EASIX score and cognitive decline was more pronounced in older individuals.\u003c/p\u003e\n\u003cp\u003eBox plots (\u003cstrong\u003eSupplementary Figure 6\u003c/strong\u003e) showed the distribution of cognitive function grouped by EASIX score, with the median decreasing as EASIX increases, providing population-level support for the aforementioned association.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn this retrospective study, we assessed the relationship between EASIX and cognitive function. After comprehensive adjustment for potential confounding factors that may influence cognitive performance, a significant negative correlation was observed between participants' EASIX score and cognitive function. Quartile analysis further supported these findings, with significantly lower cognitive function scores in the Q4 group than in the Q1 group. These findings suggest that EASIX may serve as an effective biomarker for assessing cognitive function.\u003c/p\u003e\u003cp\u003eThe findings were further validated and expanded through machine learning approaches. LASSO regression was used to screen 13 key variables, confirming the importance of EASIX in predicting cognitive function. Among the various machine learning models constructed on the basis of the screened features, Random Forest showed the best performance. SHAP analysis verified EASIX as a significant predictor, with cognitive function levels showing a declining trend as EASIX scores increased. Interaction effect analysis further indicated that the association between elevated EASIX and cognitive decline was more pronounced in older individuals.\u003c/p\u003e\u003cp\u003eEASIX is calculated via LDH, creatinine, and PLT, aiming to quantify endothelial dysfunction and systemic inflammatory status\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. Its core value lies in integrating information across three dimensions: endothelial cell injury, renal dysfunction, and coagulation system activation, providing a convenient and effective window for assessing systemic endothelial health\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e],[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. Its clinical application has extended beyond the original field of transplantation medicine and has been validated as a robust prognostic marker in cardiovascular diseases\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e, oncology\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e, critical care\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e, and other areas. In patients with chronic heart failure, an EASIX\u0026thinsp;\u0026ge;\u0026thinsp;2.32 was associated with a 2\u0026ndash;3 times higher mortality risk within 5 years, and elevated EASIX consistently predicted reduced overall survival regardless of heart failure subtype or symptom severity\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. A study on traumatic brain injury found that when EASIX\u0026thinsp;\u0026gt;\u0026thinsp;2.12, the sensitivity for predicting 30-day mortality reached 88.64%, and it showed a negative correlation with platelet function (r = -0.58) \u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eUnderstanding the pathological significance of each component of EASIX helps elucidate its potential link with cognitive decline. The release of LDH serves as a key marker of NLRP3 inflammasome-mediated pyroptosis, and elevated LDH levels are directly correlated with neuroinflammation and cognitive impairment in epilepsy models\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. In a study on COVID-19-related cognitive dysfunction, only LDH levels were negatively correlated with the neurocognitive composite score\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. Elevated creatinine levels are a hallmark of chronic kidney disease (CKD) \u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e, which is often accompanied by endothelial dysfunction\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e and cognitive decline\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e. Platelets are considered a \"peripheral mirror\" of Alzheimer's disease pathology and may directly contribute to cognitive impairment by promoting cerebrovascular amyloid-β deposition, neuroinflammation, and oxidative stress\u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe negative correlation between EASIX and cognitive function observed in this study may be attributed to the following underlying mechanisms: elevated EASIX levels indicate systemic endothelial dysfunction and inflammatory states, which influence the brain microenvironment through multiple intertwined pathways, ultimately leading to cognitive impairment. A direct consequence of systemic endothelial dysfunction is impaired neurovascular coupling (NVC) \u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e.Dysfunctional endothelial cells release signaling molecules that cross the blood-brain barrier (BBB) into the brain parenchyma, causing localized cerebral microvascular endothelial dysfunction and directly compromising NVC function11. This reduces blood supply to regions of neuronal activity, resulting in functional ischemia that impairs synaptic activity and neuronal metabolism\u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e. This mechanism aligns with the high sensitivity of DSST and AFT scores to EASIX changes observed in the present study. Systemic endothelial dysfunction is often accompanied by disruption of BBB integrity\u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e, manifested by cleavage of tight junction proteins\u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e. Increased BBB permeability allows peripheral inflammatory mediators (e.g., IL-6, and TNF-α) \u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e and neurotoxic substances\u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e to enter the brain parenchyma, activating microglia and triggering neuroinflammation\u003csup\u003e[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e. Neuroinflammation accelerates cognitive decline by promoting synaptic phagocytosis\u003csup\u003e[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e, neuronal damage\u003csup\u003e[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/sup\u003e, and white matter lesions\u003csup\u003e[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/sup\u003e. The decrease in PLT reflects widespread microcirculatory disturbances, leading to reduced vascular regulatory capacity and microvascular thrombosis\u003csup\u003e[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]\u003c/sup\u003e. This induces chronic cerebral hypoperfusion, exacerbating neuronal injury through tissue hypoxia, oxidative stress, and energy metabolism crises\u003csup\u003e[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eBased on the NHANES data, this study employed a complex sampling design and weighted analysis, ensuring that the results are well-representative of the older adult population in the United States. It is the first to validate the association between EASIX and cognitive function, providing new insights for early non-invasive screening of cognitive decline. The calculation of EASIX based on routine laboratory parameters is simple and facilitates clinical implementation.\u003c/p\u003e\u003cp\u003eHowever, this study has certain limitations. As a cross-sectional analysis, it causal inference limitations. Although a significant association was observed between EASIX and cognitive function, the causal relationship between endothelial dysfunction and cognitive decline cannot be definitively established. Prospective cohort studies are needed to validate the predictive value of the EASIX for cognitive impairment. While the NHANES cognitive test battery covers core cognitive domains, its sensitivity to mild cognitive changes may be lower than that of comprehensive neuropsychological assessments. Furthermore, despite adjustment for multiple covariates, residual confounding factors may still exist.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eBased on the NHANES data, this study systematically investigated the relationship between EASIX and cognitive function in middle-aged and older adults through a combination of cross-sectional analysis and machine learning model. The results revealed significant negative correlations between EASIX and multiple cognitive function scores, with stronger effects observed in older population subgroups. These findings suggest that EASIX may serve as an effective and readily available blood biomarker reflecting endothelial dysfunction, providing a novel tool for early identification and risk stratification of cognitive decline. These machine learning analyses strengthen the association between EASIX and cognitive decline from a predictive model perspective, facilitating the identification of high-risk individuals for early intervention.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by a grant from the Medical Health Science and Technology Project of Zhejiang Provincial Health Commission (No. 2025KY459).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eZixu Wang:\u0026nbsp;conceptualization, investigation, software, formal analysis, data curation, writing - review and editing, writing - original draft, visualization. Yin Liu: conceptualization, writing \u0026ndash; review and editing. Zhiliang Zhou: supervision. Hongwei Liu: supervision. Zhinan Ye: supervision.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe gratefully acknowledge the National Health and Nutrition Examination Survey (NHANES) for providing access to the research data. We extend our sincere appreciation to all study participants for their contributions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study protocol received approval from the National Center for Health Statistics Research Ethics Review Board to ensure adherence to ethical standards. Written informed consent was obtained from each participant prior to their enrollment in the research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eNations U: World Population Ageing 2023. In. Edited by New York N, USA; 2023.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMir FA, Amanullah A, Jain BP, Hyderi Z, Gautam A: Neuroepigenetics of ageing and neurodegeneration-associated dementia: An updated review. \u003cem\u003eAgeing Res Rev\u003c/em\u003e 2023, 91:102067.\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.arr.2023.102067\u003c/span\u003e\u003cspan address=\"10.1016/j.arr.2023.102067\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVega JN, Newhouse PA: Mild cognitive impairment: diagnosis, longitudinal course, and emerging treatments. \u003cem\u003eCurr Psychiatry Rep\u003c/em\u003e 2014, 16(10):490.\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11920-014-0490-8\u003c/span\u003e\u003cspan address=\"10.1007/s11920-014-0490-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCummings J, Aisen PS, DuBois B, Fr\u0026ouml;lich L, Jack CR, Jr., Jones RW, Morris JC, Raskin J, Dowsett SA, Scheltens P: Drug development in Alzheimer's disease: the path to 2025. \u003cem\u003eAlzheimers Res Ther\u003c/em\u003e 2016, 8:39.\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s13195-016-0207-9\u003c/span\u003e\u003cspan address=\"10.1186/s13195-016-0207-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVaz M, Silvestre S: Alzheimer's disease: Recent treatment strategies. \u003cem\u003eEur J Pharmacol\u003c/em\u003e 2020, 887:173554.\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ejphar.2020.173554\u003c/span\u003e\u003cspan address=\"10.1016/j.ejphar.2020.173554\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMorgan AE, Mc Auley MT: Vascular dementia: From pathobiology to emerging perspectives. \u003cem\u003eAgeing Res Rev\u003c/em\u003e 2024, 96:102278.\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.arr.2024.102278\u003c/span\u003e\u003cspan address=\"10.1016/j.arr.2024.102278\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTarantini S, Valcarcel-Ares MN, Toth P, Yabluchanskiy A, Tucsek Z, Kiss T, Hertelendy P, Kinter M, Ballabh P, S\u0026uuml;le Z \u003cem\u003eet al\u003c/em\u003e: Nicotinamide mononucleotide (NMN) supplementation rescues cerebromicrovascular endothelial function and neurovascular coupling responses and improves cognitive function in aged mice. \u003cem\u003eRedox Biol\u003c/em\u003e 2019, 24:101192.\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.redox.2019.101192\u003c/span\u003e\u003cspan address=\"10.1016/j.redox.2019.101192\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWu LY, Chai YL, Cheah IK, Chia RSL, Hilal S, Arumugam TV, Chen CP, Lai MKP: Blood-based biomarkers of cerebral small vessel disease. \u003cem\u003eAgeing Res Rev\u003c/em\u003e 2024, 95:102247.\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.arr.2024.102247\u003c/span\u003e\u003cspan address=\"10.1016/j.arr.2024.102247\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGoncharov NV, Popova PI, Kudryavtsev IV, Golovkin AS, Savitskaya IV, Avdonin PP, Korf EA, Voitenko NG, Belinskaia DA, Serebryakova MK \u003cem\u003eet al\u003c/em\u003e: Immunological Profile and Markers of Endothelial Dysfunction in Elderly Patients with Cognitive Impairments. \u003cem\u003eInt J Mol Sci\u003c/em\u003e 2024, 25(3).\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/ijms25031888\u003c/span\u003e\u003cspan address=\"10.3390/ijms25031888\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eQuick S, Moss J, Rajani RM, Williams A: A Vessel for Change: Endothelial Dysfunction in Cerebral Small Vessel Disease. \u003cem\u003eTrends Neurosci\u003c/em\u003e 2021, 44(4):289\u0026ndash;305.\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.tins.2020.11.003\u003c/span\u003e\u003cspan address=\"10.1016/j.tins.2020.11.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSmith BC, Tinkey RA, Shaw BC, Williams JL: Targetability of the neurovascular unit in inflammatory diseases of the central nervous system. \u003cem\u003eImmunol Rev\u003c/em\u003e 2022, 311(1):39\u0026ndash;49.\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/imr.13121\u003c/span\u003e\u003cspan address=\"10.1111/imr.13121\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePantoni L: Cerebral small vessel disease: from pathogenesis and clinical characteristics to therapeutic challenges. \u003cem\u003eLancet Neurol\u003c/em\u003e 2010, 9(7):689\u0026ndash;701.\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/s1474-4422(10)70104-6\u003c/span\u003e\u003cspan address=\"10.1016/s1474-4422(10)70104-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLuft T, Benner A, Jodele S, Dandoy CE, Storb R, Gooley T, Sandmaier BM, Becker N, Radujkovic A, Dreger P \u003cem\u003eet al\u003c/em\u003e: EASIX in patients with acute graft-versus-host disease: a retrospective cohort analysis. \u003cem\u003eLancet Haematol\u003c/em\u003e 2017, 4(9):e414\u0026ndash;e423.\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/s2352-3026(17)30108-4\u003c/span\u003e\u003cspan address=\"10.1016/s2352-3026(17)30108-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePark S, Go SI, Lee GW: The Endothelial Activation and Stress Index (EASIX) score is an independent prognostic factor in patients with diffuse large B-cell lymphoma. \u003cem\u003eBMC Cancer\u003c/em\u003e 2022, 22(1):816.\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12885-022-09915-4\u003c/span\u003e\u003cspan address=\"10.1186/s12885-022-09915-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eXu HB, Ye Y, Xue F, Wu J, Suo Z, Zhang H: Association Between Endothelial Activation and Stress Index and 28-Day Mortality in Septic ICU patients: a Retrospective Cohort Study. \u003cem\u003eInt J Med Sci\u003c/em\u003e 2023, 20(9):1165\u0026ndash;1173.\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.7150/ijms.85870\u003c/span\u003e\u003cspan address=\"10.7150/ijms.85870\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSchult D, Rasch S, Schmid RM, Lahmer T, Mayr U: EASIX Is an Accurate and Easily Available Prognostic Score in Critically Ill Patients with Advanced Liver Disease. \u003cem\u003eJ Clin Med\u003c/em\u003e 2023, 12(7).\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/jcm12072553\u003c/span\u003e\u003cspan address=\"10.3390/jcm12072553\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLuft T, Wendtner CM, Kosely F, Radujkovic A, Benner A, Korell F, Kihm L, Bauer MF, Dreger P, Merle U: EASIX for Prediction of Outcome in Hospitalized SARS-CoV-2 Infected Patients. \u003cem\u003eFront Immunol\u003c/em\u003e 2021, 12:634416.\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fimmu.2021.634416\u003c/span\u003e\u003cspan address=\"10.3389/fimmu.2021.634416\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKorell F, DeFilipp Z, Schreck N, Luft T: Validation of pre-conditioning EASIX for prediction of sepsis after allogeneic stem cell transplantation. \u003cem\u003eIntensive Care Med\u003c/em\u003e 2023, 49(11):1408\u0026ndash;1410.\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00134-023-07193-7\u003c/span\u003e\u003cspan address=\"10.1007/s00134-023-07193-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJiang S, Penack O, Terzer T, Schult D, Majer-Lauterbach J, Radujkovic A, Blau IW, Bullinger L, M\u0026uuml;ller-Tidow C, Dreger P \u003cem\u003eet al\u003c/em\u003e: Predicting sinusoidal obstruction syndrome after allogeneic stem cell transplantation with the EASIX biomarker panel. \u003cem\u003eHaematologica\u003c/em\u003e 2021, 106(2):446\u0026ndash;453.\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3324/haematol.2019.238790\u003c/span\u003e\u003cspan address=\"10.3324/haematol.2019.238790\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYin F, Wang K: Independent prognostic importance of endothelial activation and stress index (EASIX) in critically ill patients with heart failure: modulating role of inflammation. \u003cem\u003eFront Med (Lausanne)\u003c/em\u003e 2025, 12:1560947.\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fmed.2025.1560947\u003c/span\u003e\u003cspan address=\"10.3389/fmed.2025.1560947\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFrenking JH, Zhou X, Wagner V, Hielscher T, Kauer J, Mai EK, Friedrich MJ, Michel CS, Hajiyianni M, Breitkreutz I \u003cem\u003eet al\u003c/em\u003e: EASIX-guided risk stratification for complications and outcome after CAR T-cell therapy with ide-cel in relapsed/refractory multiple myeloma. \u003cem\u003eJ Immunother Cancer\u003c/em\u003e 2024, 12(10)\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e.https://doi.org/10.1136/jitc-2024-009220\u003c/span\u003e\u003cspan address=\".10.1136/jitc-2024-009220\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang J, Chen X, Qin C, Zeng X, Du X, Wang D: The endothelial activation and stress index is a potential prognostic indicator for patients with acute pancreatitis managed in the intensive care unit: a retrospective study. \u003cem\u003eFront Med (Lausanne)\u003c/em\u003e 2024, 11:1498148.\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fmed.2024.1498148\u003c/span\u003e\u003cspan address=\"10.3389/fmed.2024.1498148\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEstler B, Fr\u0026ouml;hlich H, T\u0026auml;ger T, Hund H, Frey N, Luft T, Frankenstein L: Endothelial dysfunction assessed by the endothelial activation and stress index (EASIX) predicts risk of mortality in chronic heart failure patients. \u003cem\u003eInt J Cardiol\u003c/em\u003e 2025, 438:133566.\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ijcard.2025.133566\u003c/span\u003e\u003cspan address=\"10.1016/j.ijcard.2025.133566\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBăetu AE, Mirea LE, Cobilinschi C, Grințescu IC, Grințescu IM: Platelet Contribution and Endothelial Activation and Stress Index-Potential Mortality Predictors in Traumatic Brain Injury. \u003cem\u003eInt J Mol Sci\u003c/em\u003e 2024, 25(14).\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/ijms25147763\u003c/span\u003e\u003cspan address=\"10.3390/ijms25147763\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang L, Ding J, Zhu C, Guo B, Yang W, He W, Li X, Wang Y, Li W, Wang F \u003cem\u003eet al\u003c/em\u003e: Semaglutide attenuates seizure severity and ameliorates cognitive dysfunction by blocking the NLR family pyrin domain containing 3 inflammasome in pentylenetetrazole\u0026ndash;kindled mice. \u003cem\u003eInt J Mol Med\u003c/em\u003e 2021, 48(6).\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3892/ijmm.2021.5052\u003c/span\u003e\u003cspan address=\"10.3892/ijmm.2021.5052\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGalderisi S, Perrottelli A, Giuliani L, Pisaturo MA, Monteleone P, Pagliano P, Vita A, Muiesan ML, Amore M, Bassetti M \u003cem\u003eet al\u003c/em\u003e: Cognitive impairment after recovery from COVID-19: Frequency, profile, and relationships with clinical and laboratory indices. \u003cem\u003eEur Neuropsychopharmacol\u003c/em\u003e 2024, 79:22\u0026ndash;31.\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.euroneuro.2023.11.001\u003c/span\u003e\u003cspan address=\"10.1016/j.euroneuro.2023.11.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVan Biesen W, Vanholder R, Veys N, Verbeke F, Delanghe J, De Bacquer D, Lameire N: The importance of standardization of creatinine in the implementation of guidelines and recommendations for CKD: implications for CKD management programmes. \u003cem\u003eNephrol Dial Transplant\u003c/em\u003e 2006, 21(1):77\u0026ndash;83.\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/ndt/gfi185\u003c/span\u003e\u003cspan address=\"10.1093/ndt/gfi185\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCho ME, Brunt VE, Shiu YT, Bunsawat K: Endothelial dysfunction in chronic kidney disease: a clinical perspective. \u003cem\u003eAm J Physiol Heart Circ Physiol\u003c/em\u003e 2025, 329(1):H135\u0026ndash;h153.\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1152/ajpheart.00908.2024\u003c/span\u003e\u003cspan address=\"10.1152/ajpheart.00908.2024\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eViggiano D, Wagner CA, Martino G, Nedergaard M, Zoccali C, Unwin R, Capasso G: Mechanisms of cognitive dysfunction in CKD. \u003cem\u003eNat Rev Nephrol\u003c/em\u003e 2020, 16(8):452\u0026ndash;469.\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41581-020-0266-9\u003c/span\u003e\u003cspan address=\"10.1038/s41581-020-0266-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTalib LL, Joaquim HP, Forlenza OV: Platelet biomarkers in Alzheimer's disease. \u003cem\u003eWorld J Psychiatry\u003c/em\u003e 2012, 2(6):95\u0026ndash;101.\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5498/wjp.v2.i6.95\u003c/span\u003e\u003cspan address=\"10.5498/wjp.v2.i6.95\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRamos-Cejudo J, Johnson AD, Beiser A, Seshadri S, Salinas J, Berger JS, Fillmore NR, Do N, Zheng C, Kovbasyuk Z \u003cem\u003eet al\u003c/em\u003e: Platelet Function Is Associated With Dementia Risk in the Framingham Heart Study. \u003cem\u003eJ Am Heart Assoc\u003c/em\u003e 2022, 11(9):e023918\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e.https://doi.org/10.1161/jaha.121.023918\u003c/span\u003e\u003cspan address=\".10.1161/jaha.121.023918\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHarraz OF, Klug NR, Senatore AJ, Hill-Eubanks DC, Nelson MT: Piezo1 Is a Mechanosensor Channel in Central Nervous System Capillaries. \u003cem\u003eCirc Res\u003c/em\u003e 2022, 130(10):1531\u0026ndash;1546.\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1161/circresaha.122.320827\u003c/span\u003e\u003cspan address=\"10.1161/circresaha.122.320827\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChen YX, Yang H, Wang DS, Chen TT, Qi XL, Tao L, Chen Y, Shen XC: Gastrodin alleviates mitochondrial dysfunction by regulating SIRT3-mediated TFAM acetylation in vascular dementia. \u003cem\u003ePhytomedicine\u003c/em\u003e 2024, 128:155369.\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.phymed.2024.155369\u003c/span\u003e\u003cspan address=\"10.1016/j.phymed.2024.155369\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOhtsuki S, Sato S, Yamaguchi H, Kamoi M, Asashima T, Terasaki T: Exogenous expression of claudin-5 induces barrier properties in cultured rat brain capillary endothelial cells. \u003cem\u003eJ Cell Physiol\u003c/em\u003e 2007, 210(1):81\u0026ndash;86.\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/jcp.20823\u003c/span\u003e\u003cspan address=\"10.1002/jcp.20823\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu J, Weaver J, Jin X, Zhang Y, Xu J, Liu KJ, Li W, Liu W: Nitric Oxide Interacts with Caveolin-1 to Facilitate Autophagy-Lysosome-Mediated Claudin-5 Degradation in Oxygen-Glucose Deprivation-Treated Endothelial Cells. \u003cem\u003eMol Neurobiol\u003c/em\u003e 2016, 53(9):5935\u0026ndash;5947.\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s12035-015-9504-8\u003c/span\u003e\u003cspan address=\"10.1007/s12035-015-9504-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCamire RB, Beaulac HJ, Willis CL: Transitory loss of glia and the subsequent modulation in inflammatory cytokines/chemokines regulate paracellular claudin-5 expression in endothelial cells. \u003cem\u003eJ Neuroimmunol\u003c/em\u003e 2015, 284:57\u0026ndash;66.\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jneuroim.2015.05.008\u003c/span\u003e\u003cspan address=\"10.1016/j.jneuroim.2015.05.008\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAlkhalifa AE, Al-Ghraiybah NF, Odum J, Shunnarah JG, Austin N, Kaddoumi A: Blood-Brain Barrier Breakdown in Alzheimer's Disease: Mechanisms and Targeted Strategies. \u003cem\u003eInt J Mol Sci\u003c/em\u003e 2023, 24(22).\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/ijms242216288\u003c/span\u003e\u003cspan address=\"10.3390/ijms242216288\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNimmerjahn A, Kirchhoff F, Helmchen F: Resting microglial cells are highly dynamic surveillants of brain parenchyma in vivo. \u003cem\u003eScience\u003c/em\u003e 2005, 308(5726):1314\u0026ndash;1318.\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1126/science.1110647\u003c/span\u003e\u003cspan address=\"10.1126/science.1110647\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCaplan LR: Lacunar infarction and small vessel disease: pathology and pathophysiology. \u003cem\u003eJ Stroke\u003c/em\u003e 2015, 17(1):2\u0026ndash;6\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e.https://doi.org/10.5853/jos.2015.17.1.2\u003c/span\u003e\u003cspan address=\".10.5853/jos.2015.17.1.2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZlokovic BV: Neurovascular pathways to neurodegeneration in Alzheimer's disease and other disorders. \u003cem\u003eNat Rev Neurosci\u003c/em\u003e 2011, 12(12):723\u0026ndash;738.\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/nrn3114\u003c/span\u003e\u003cspan address=\"10.1038/nrn3114\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKan CN, Gyanwali B, Hilal S, Ng KP, Venketasubramanian N, Chen CL, Xu X: Neuropsychiatric Correlates of Small Vessel Disease Progression in Incident Cognitive Decline: Independent and Interactive Effects. \u003cem\u003eJ Alzheimers Dis\u003c/em\u003e 2020, 73(3):1053\u0026ndash;1062.\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3233/jad-190999\u003c/span\u003e\u003cspan address=\"10.3233/jad-190999\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNezu T, Hosomi N, Aoki S, Kubo S, Araki M, Mukai T, Takahashi T, Maruyama H, Higashi Y, Matsumoto M: Endothelial dysfunction is associated with the severity of cerebral small vessel disease. \u003cem\u003eHypertens Res\u003c/em\u003e 2015, 38(4):291\u0026ndash;297.\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/hr.2015.4\u003c/span\u003e\u003cspan address=\"10.1038/hr.2015.4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTian Z, Ji X, Liu J: Neuroinflammation in Vascular Cognitive Impairment and Dementia: Current Evidence, Advances, and Prospects. \u003cem\u003eInt J Mol Sci\u003c/em\u003e 2022, 23(11).\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/ijms23116224\u003c/span\u003e\u003cspan address=\"10.3390/ijms23116224\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 to 2 are available in the Supplementary Files section.\u003c/p\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":"european-journal-of-medical-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejmr","sideBox":"Learn more about [European Journal of Medical Research](http://eurjmedres.biomedcentral.com)","snPcode":"40001","submissionUrl":"https://submission.nature.com/new-submission/40001/3","title":"European Journal of Medical Research","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"EASIX, cognitive function, cross-sectional study, NHANES","lastPublishedDoi":"10.21203/rs.3.rs-7537562/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7537562/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAge-associated memory impairment (AAMI) is a predementia state linked to endothelial dysfunction. The endothelial activation and stress index (EASIX) quantifies endothelial injury, yet its association with cognitive function remains unvalidated in population studies. This study aimed to evaluate the relationship between EASIX and cognitive performance.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e\u003cp\u003eData from adults aged\u0026thinsp;\u0026ge;\u0026thinsp;60 years in the NHANES 2011\u0026ndash;2014 were analyzed. Multiple linear regression assessed associations between EASIX and cognitive function scores. LASSO regression selected variables, and six machine learning models (e.g., Random Forest, XGBoost) and two ensemble strategies were developed. SHAP values interpreted feature importance.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAmong 2,763 participants, EASIX showed a significant negative correlation with all cognitive scores (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The Random Forest model outperformed other models. SHAP analysis identified EASIX as one of the top five influential variables, with cognitive function levels demonstrating a declining trend as EASIX score increased, particularly among older adults.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusion\u003c/b\u003e\u003c/p\u003e\u003cp\u003eEASIX is significantly negatively associated with cognitive function, especially in advanced age. It shows promise as a blood-based biomarker for early screening and risk assessment of cognitive decline, supporting its potential clinical utility.\u003c/p\u003e","manuscriptTitle":"Association of the endothelial activation and stress index with cognitive function in older adults: a cross-sectional study with machine learning","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-30 07:27:30","doi":"10.21203/rs.3.rs-7537562/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-07T13:03:01+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-27T13:44:00+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-27T00:25:38+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-24T10:02:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"326238231396397427244545518799057893559","date":"2025-09-22T08:27:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"267276776752933798636081241251772755066","date":"2025-09-22T01:03:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"321715678110387386767031698113665651396","date":"2025-09-19T16:06:14+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-19T15:06:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"284985168225918694568273903341428051427","date":"2025-09-19T14:52:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"240530964380550431865111633307579369717","date":"2025-09-19T14:16:02+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-18T13:19:29+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-12T16:02:09+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-11T10:32:41+00:00","index":"","fulltext":""},{"type":"submitted","content":"European Journal of Medical Research","date":"2025-09-04T15:14:49+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"european-journal-of-medical-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejmr","sideBox":"Learn more about [European Journal of Medical Research](http://eurjmedres.biomedcentral.com)","snPcode":"40001","submissionUrl":"https://submission.nature.com/new-submission/40001/3","title":"European Journal of Medical Research","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"137b53c5-159e-406d-935c-1c885273f80c","owner":[],"postedDate":"September 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-12-08T16:08:14+00:00","versionOfRecord":{"articleIdentity":"rs-7537562","link":"https://doi.org/10.1186/s40001-025-03512-4","journal":{"identity":"european-journal-of-medical-research","isVorOnly":false,"title":"European Journal of Medical Research"},"publishedOn":"2025-12-06 15:58:23","publishedOnDateReadable":"December 6th, 2025"},"versionCreatedAt":"2025-09-30 07:27:30","video":"","vorDoi":"10.1186/s40001-025-03512-4","vorDoiUrl":"https://doi.org/10.1186/s40001-025-03512-4","workflowStages":[]},"version":"v1","identity":"rs-7537562","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7537562","identity":"rs-7537562","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}