Hyperhomocysteinemia Increases the Risk of Carotid Atherosclerotic Plaque in the High-Risk Group of Stroke: A Cross Sectional Study

Hyperhomocysteinemia Increases the Risk of Carotid Atherosclerotic Plaque in the High-Risk Group of Stroke: A Cross Sectional Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Hyperhomocysteinemia Increases the Risk of Carotid Atherosclerotic Plaque in the High-Risk Group of Stroke: A Cross Sectional Study Shenna Niu, Long Tian, Jie Zhang, Yan Gao, Huicong Xiao, Shumei Yao, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4152280/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Hyperhomocysteinemia (Hhcy) is associated with ischemic stroke. Controlling or reversing the progression of atherosclerotic plaque is essential to prevent ischemic stroke. The purpose of this study was to explore the relationship between hyperhomocysteinemia and the risk of carotid atherosclerotic plaque in the high-risk population of stroke in China. We included the high-risk population of stroke over 40 years old in East China for analysis, measured the plasma total homocysteine level, and evaluated the carotid atherosclerotic plaque by ultrasound. After adjusting for demographic and vascular risk factors, multiple machine models were applied to analyze the correlation between hyperhomocysteinemia and carotid atherosclerotic plaque. The logistic model achieved the best performance at AUROC (0.720), followed by Bayes (0.708), and KNN (0.665). SVM with random forest did not work well. The results showed that 17006 (76.6%) of 22192 subjects had carotid atherosclerotic plaque. Among the population ≧ 55 years old, HHcy was significantly associated with carotid atherosclerotic plaque. HHcy (OR = 1.17, P < 0.001) is a risk factor of carotid atherosclerotic plaque. We conclude that hyperhomocysteinemia is an independent risk factor for carotid atherosclerotic plaque in stroke high-risk population. Stroke Hyperhomcysteinemia Atherosclerotic Homcysteine Risk factors Figures Figure 1 1 Introduction With the aging of the Chinese population, the disease burden of stroke continues to increase, and has become the leading cause of death in recent years [ 1 ]. Although stroke can be quickly diagnosed, primary prevention is still challenging, especially for high-risk stroke population [ 2 ]. Rupture and bleeding of atherosclerotic plaque, plaque shedding, or secondary vascular stenosis are important causes of ischemic stroke [ 3 , 4 ]. Many major modifiable risk factors of atherosclerosis have been identified, and the causal correlation of several risk factors has been fully confirmed (including but not limited to smoking, hypertension, hyperlipidemia, and diabetes) [ 2 , 5 , 6 ]. Homocysteine was also significantly associated with ischemic stroke [ 7 ]. A prospective case-control study based on rural China found that total homocysteine (tHcy) level and systolic blood pressure (SBP) were the two most important risk factors, which had independent and cumulative effects on the risk of first stroke (tHcy: 1.06; SBP: 1.13; P-interaction, 0.889) [ 8 ]. In the study of the rat model of transient focal cerebral ischemia, it was also found that the increase of Hcy level would increase brain injury and neurotoxicity after cerebral ischemia/reperfusion, activate microglia and induce TNF- α and IL-6[ 9 ]. However, the accumulated research mainly focused on the relationship between homocysteine and vascular disease. So far, there are few studies on the relationship between homocysteine and atherosclerotic plaque, and they are usually in the general population. This study is a cross-sectional study carried out in the high-risk population of stroke in East China. The correlation between hyperhomocysteinemia and carotid atherosclerotic plaque was evaluated through multiple machine learning models. We hope that the results of this study can provide better prevention and management strategies for people at high risk of stroke. 2 Materials and Methods 2.1 Ethical Considerations This study has been approved by the ethics committee of the Provincial Hospital Affiliated to Shandong First Medical University. The research protocol is consistent with the Helsinki Declaration (2013, Brazil Revision). Written informed consent had been obtained from all individuals before they were included in the study. 2.2 Study Population This large-population based retrospective study was conducted by cluster sampling in 18 urban areas and 18 rural areas of Shandong Province, a representative province in eastern China (with a large population and rapid economic development), and identified 231289 (108230 men and 123059 women) permanent residents over the age of 40 (date of birth from January 1, 1937 to December 31, 1971). Residents living in the local area for ≥ 6 months can also be defined as the screening subjects. The number of people screened in each region should not be fewer than 6000. 2.3 Data Collection All data were collected through a national questionnaire which included: 1. basic information of the respondents, including age, gender, BMI, exercise status, community, education level, smoking history. 2. previous history of hypertension, atrial fibrillation, diabetes, dyslipidemia, transient ischemic attack (TIA), and stroke. 3. family history of stroke. The evaluation criteria for stroke risk factors are as follows: ① Hypertension: previous history of hypertension diagnosed by hospitals graded level 2 or above; or the current use of antihypertensives; or the screening measurement results showed that high blood pressure (systolic blood pressure ≥ 140 and / or diastolic blood pressure ≥ 90mmHg). ② Atrial fibrillation (AF): previous history of atrial fibrillation diagnosed by a hospital graded level 2 or above; or atrial fibrillation shown by electrocardiogram in this screening. ③ Dyslipidemia: previous history of dyslipidemia diagnosed by hospitals graded level 2 or above; or one or more abnormalities (total cholesterol ≥ 5.70mmol/l, triglyceride ≥ 1.70mmol/l, LDLC ≥ 3.10mmol/l) ④ Diabetes: previous history of diabetes diagnosed by hospitals graded level 2 or above; treatment with insulin or oral hypoglycemic medications; or a fasting blood glucose ≥ 126mg/dl or glycosylated hemoglobin ≥ 6.5% in this screening. ⑤ Smoking: continuous or cumulative smoking for more than 6 months. ⑥ Lack of sports: physical exercise＜3 times / week and each＜30min in duration (industrial and agricultural labor is regarded as physical exercise). ⑦ Obesity: body mass index≥25kg/m2. ⑧ Family history of stroke: self-reported family history of stroke. The above risk factors of all screening populations were assessed. Those individuals who have three or more stroke risk factors; or previous TIA, or previous stroke were defined as stroke high-risk groups. We selected 22192 participants of stroke high-risk groups as our subjects, accounting for 9.5% of all screening populations. 2.4 Ultrasonic examination All patients who were defined as stroke high risk group were screened by bilateral carotid artery ultrasound. The intimal thickness of carotid artery was observed and the number of plaques in bilateral carotid arteries was recorded. The local carotid intima thickness≥1.5mm is considered as the formation of carotid atherosclerotic plaque[10]. 2.5 Statistical analysis Kolmogorov-Smirnov test was used to estimate the normality of continuous variables. Continuous variables were reported as means±standard deviations and t-test was applied for statistical analysis. The continuous variables conforming to the skew distribution were expressed by the median (interquartile range) and statistically analyzed by Mann Whitney U test. Categorical variables were described by percentage and analyzed using the chi-square test. Binary logistic regression was used to evaluate relationships between each risk factor and carotid atherosclerotic plaque. All statistical tests were two-tailed, and the significance level was set to 0.05. All data were analyzed by R4.2.1. 2.6 Machine learning modeling Five machine learning models were applied, including logistic regression, random forest (RF), support vector machine (SVM), K-nearest neighbours (KNN)，and Naive Bayes classifier. Logistic regression is a widely used statistical method used for analyzing the relationship between a dependent variable and one or more independent variables. It is commonly used in binary classification problems. The goal of logistic regression is to predict the probability of the dependent variable taking a particular value based on the values of the independent variables. The logistic function allows us to transform any linear combination of the independent variables into a probability value between 0 and 1, which can then be used to make a binary classification decision. Random forest is an ensemble learning method that builds a multitude of decision trees and aggregates their predictions to arrive at a final output. The algorithm works by randomly selecting a subset of features and data samples from the training set and building decision trees based on these subsets. This process is repeated multiple times, resulting in a "forest" of decision trees. Each decision tree in the forest is trained on a different subset of data and features, making them less prone to overfitting and improving the overall accuracy and stability of the model. Support Vector Machine (SVM) is a supervised learning method that constructs a hyperplane or set of hyperplanes in a high-dimensional space to separate data points into different classes. SVM algorithm finds the hyperplane that maximizes the margin between the closest points of each class, also known as the support vectors. The margin is the distance between the hyperplane and the support vectors, and maximizing it helps to increase the robustness and generalization of the model. k-Nearest Neighbor (k-NN) is a simple and popular machine learning algorithm used for both classification and regression tasks. It is a non-parametric and lazy learning algorithm that does not make any assumptions about the underlying data distribution. The k-NN algorithm works by finding the k closest training data points to a new data point and then using the labels or values of those points to make a prediction. In the case of classification, the algorithm assigns the most frequent class label among the k-nearest neighbors to the new data point. Naive Bayes is based on Bayes' theorem, which states that the probability of a hypothesis (a targeted class label) given the observed evidence (the features of a data point) is proportional to the probability of the evidence given the hypothesis, multiplied by the prior probability of the hypothesis. 3 Results 3.1 clinical and demographic characteristics of subjects 22792 participants participated in the screening, of which 17006 (76.6%) had carotid atherosclerotic plaque. The average age of the subjects was 62.9 ± 9.5 years old. In addition, 6199 (27.9%) did not suffer from hypertension, 10582 (51.5%) did not suffer from hyperhomocysteinemia (HHcy), 17606 (79.3%) did not suffer from diabetes, 18783 (84.6%) did not suffer from atrial fibrillation, 15877 (71.5%) did not have a history of smoking, 13403 (60.4%) did not lack physical exercise, and 11256 (50.7%) were not overweight. Table 1 lists the baseline characteristics of subjects with and without carotid atherosclerotic plaque. We found that the average age of participants with carotid atherosclerotic plaque was higher than those without carotid atherosclerotic plaque (63.9 ± 9.4 and 50.9 ± 9.3, respectively). As shown in Table 1 , the incidence rate of men, hypertension, smoking, diabetes, and HHcy (50.9%, 73.8%, 29.5%, 21.2%, and 53.9%, respectively) in subjects with carotid atherosclerotic plaque is also higher than that in subjects without carotid atherosclerotic plaque (43%, 66.2%, 25.1%, 18.8%, and 47.1%, respectively). However, the TG and LDL-C level in the subjects with carotid atheroscleroic plaque was significantly lower compared with that found in the subjects without carotid atherosclerotic plaques (1.8 ± 1.7 vs 1.8 ± 1.4; 3.1 ± 1.2 vs 3 ± 1.1, respectively). Table 1 ༎Baseline characteristics of the study cohort. Carotid plaques Overall(n = 22192) P-value Without(n = 5186) With(n = 17006) Age, years(SD) 59.9 (9.3) 63.9 (9.4) 62.9 (9.5) < 0.001 Gender(Male) 2,228 (43.0) 8,650 (50.9) 10,878 (49.0) < 0.001 Education 4,398 (84.8) 14,585 (85.8) 18,983 (85.5) 0.090 Hypertension(No) 1,751 (33.8) 4,448 (26.2) 6,199 (27.9) < 0.001 AF(No) 4,434 (85.5) 14,349 (84.4) 18,783 (84.6) 0.052 Smoking(No) 3,882 (74.9) 11,995 (70.5) 15,877 (71.5) < 0.001 Diabetes(No) 4,213 (81.2) 13,393 (78.8) 17,606 (79.3) < 0.001 SportsLack(No) 3,105 (59.9) 10,298 (60.6) 13,403 (60.4) 0.388 Obesity(No) 2,271 (43.8) 8,985 (52.8) 11,256 (50.7) < 0.001 StrokeFamily(No) 3,618 (69.8) 12,427 (73.1) 16,045 (72.3) < 0.001 Hhcy(No) 2,745 (56.5) 7,837 (49.9) 10,582 (51.5) < 0.001 TG(SD) 1.8 (1.7) 1.8 (1.4) 1.8 (1.5) 0.028 TC(SD) 5 (1.5) 5 (1.5) 5 (1.5) 0.499 LDLC(SD) 3.1 (1.2) 3 (1.1) 3 (1.1) 0.001 The chi-square test was used to compare the characteristics of different age groups of participants with and without carotid plaque (Table 2 ). Among subjects younger than 55 years old, there are significant differences between gender, hypertension, smoking, diabetes, overweight and carotid atherosclerotic plaque, but there are significant differences between gender, hypertension, smoking, overweight and HHcy when the age is older than 55 years old. Table 2 Comparison of patients with and without carotid atherosclerotic plaque at different ages groups Age<55 years(n = 4093) Age ≥ 55 years(n = 18099) X 2 P-value X 2 P-value Gender 30.947 < 0.001 91.531 < 0.001 Hypertension 28.079 < 0.001 55.582 < 0.001 Smoking 5.216 < 0.001 50.694 < 0.001 Diabetes 16.678 < 0.001 1.582 0.208 Overweight 13.427 0.022 85.509 < 0.001 HHcy 0.386 0.534 45.267 < 0.001 The input variables for the feature selected data included age, sex, hypertension, diabetes, smoking overweight, stroke history, family stroke history, hyperhomocysteinemia, LDLC, sports, TG, TC. Table 3 shows the potential variables that predict patients at risk of plaque. Table 3 Correlations between clinical characteristics and carotid atherosclerotic plaques in study cohort One-way logistic regression Variable Estimate P -value Age 0.045 < 0.001 Gender -0.334 < 0.001 Hypertension 0.367 < 0.001 Smoking 0.240 < 0.001 Diabetes 0.134 0.005 Overweight -0.386 < 0.001 StrokeFamily -0.180 < 0.001 HHcy 0.285 < 0.001 LDLC -0.056 0.001 AF 0.101 0.059 SportsLack -0.034 0.373 TG -0.017 0.172 TC -0.005 0.687 3.2 Evaluation of regression model Based on the baseline data of 22192 subjects, a Logistic regression machine learning model was established. 70% of the cases were selected to build the training database, and 30% of the cases were used as the validation database. The area under curve (AUC) of the receiver operating characteristic (ROC) of the final training set and the validation set were 0.713 and 0.720(Figure 1 ), respectively. As shown in Table 4 , multivariate logistic regression results show that hypertension (OR = 1.278 P < 0.001), diabetes (OR = 1.163 P = 0.004), HHcy (OR = 1.170 P < 0.001), smoking (OR = 1.163 P = 0.004) and age (OR = 1.050 P < 0.001) are risk factors for carotid plaque formation, and women (OR = 0.635 P < 0.001) and overweight (OR = 0.737 P < 0.001) are protective factors for carotid plaque. LDL-C statistics show no significant difference. Table 4 Multivariate logistic regression Multivariate logistic regression Variable Estimate P-value Age 0.050 < 0.001 Gender -0.365 < 0.001 Hypertension 0.278 < 0.001 Smoking 0.163 0.004 Diabetes 0.208 < 0.001 Overweight -0.263 < 0.001 StrokeFamily 0.002 0.972 HHcy 0.170 < 0.001 LDLC -0.025 0.156 The AUROCs for the test_set data for all machine learning techniques for predicting osteoporosis risk are shown in Table 5 . For the analysis of data containing 9 variables, the logistic model achieved the best performance at AUROC (0.720), followed by Bayes (0.708), and KNN (0.665). SVM with random forest did not work well. Table 5 Comparison of multiple machine learning models Logistic SVM KNN Bayes Random Forest cutoff 0.792 (0.765, 0.818) 0.500 (0.500, 0.500) 0.161 (0.078, 0.23) 0.168 (0.156, 0.217) 0.500 (0.500, 0.500) auc 0.720 (0.706, 0.734) 0.502 (0.500, 0.504) 0.665 (0.650, 0.681) 0.708 (0.693, 0.723) 0.542 (0.533, 0.550) spe 0.784 (0.709, 0.854) 0.005 (0.001, 0.009) 0.704 (0.604, 0.814) 0.834 (0.723, 0.866) 0.117 (0.101, 0.134) sen 0.552 (0.476, 0.626) 0.999 (0.999, 1.000) 0.561 (0.442, 0.652) 0.494 (0.459, 0.607) 0.966 (0.960, 0.970) npv 0.347 (0.329, 0.368) 0.714 (0.400, 1.000) 0.323 (0.303, 0.349) 0.333 (0.323, 0.356) 0.510 (0.457, 0.561) ppv 0.893 (0.875, 0.916) 0.768 (0.767, 0.769) 0.863 (0.843, 0.888) 0.907 (0.877, 0.921) 0.783 (0.780, 0.786) 4 Discussion In this cross-sectional study based on East China, we observed the association between hyperhomocysteinemia and carotid atherosclerotic plaque risk in 22192 high-risk stroke patients over 40 years old. In addition, this study shows that hyperhomocysteinemia is an independent indicator of the presence of carotid plaque in the high-risk population of stroke. Although many clinical studies have shown that hyperhomocysteinemia (Hhcy) is one of the main risk factors of cardiovascular disease (CVD), stroke and new-onset hypertension [ 7 , 11 ], It is even related to the adverse outcome of acute ischemic stroke[ 12 ], but few people have discussed the relationship between Hhcy and carotid plaque in the high-risk population of stroke. A cross-sectional study conducted by the Framingham Heart Research Center on 1041 subjects showed that subjects with the highest plasma homocysteine concentration (> or = 14.4 ummol/L) had a 2-fold increased risk of extracranial carotid artery stenosis compared with those with the lowest concentration (< or = 9.1 ummol/L)[ 13 ]. However, the subjects they studied were elderly people (67 to 96 years old) and non-Asians. A recent observational study of other high-risk groups, such as patients with uremia, hypercholesterolemia, type 2 diabetes, insulin resistance or nervous system abnormalities, failed to prove the association between plasma homocysteine level and carotid intima media thickness (IMT) [ 14 ]. Therefore, our study confirmed the association between hyperhomocysteinemia and the risk of carotid atherosclerotic plaque in the high-risk population of stroke in China. The northern Manhattan study (NOMAS) based on a multi-ethnic cohort showed that the increase of Hcy was independently related to the increase of carotid plaque morphology and plaque area [ 15 ]. Similarly, this was also found in a cross-sectional study of Chinese adults [ 16 ]. HHcy-induced vascular injury and the subsequent CVD mechanism include endothelial injury, promoting the proliferation of vascular smooth muscle cells, leading to inflammation and prethrombotic state, and oxidative stress response[ 17 – 19 ]. These pathological changes caused by HHcy are manifested as impaired vasodilation mediated by blood flow, mainly due to the reduction of NO production and bioavailability. This systemic pathological state is defined as endothelial dysfunction, which is considered to be the core of atherosclerotic plaque formation and CVD process[ 20 ]. After translation, Hcy can down-regulate the activity of dimethylarginine dimethylaminohydrolase (the enzyme that degrades ADMA), resulting in the accumulation of asymmetric dimethylarginine (ADMA, the endogenous inhibitor of NO synthase), thus inhibiting NO synthesis [ 21 ]. Liang et al. found that the ROS/COX-2 dependent activation of endothelial ENaC (epithelial sodium channel, the ion channel recently found in endothelial cells) by Hcy through SGK-1/Nedd4-2 signal transduction led to endothelial dysfunction [ 22 ]. These studies describe the pathophysiological mechanism of HHcy impairing endothelium-mediated NO dependent vasodilation. The research of Framingham's descendants showed that there was a significant positive trend between the increase of plasma total homocysteine level and the IMT of the internal carotid artery/bulb in the population aged 58 and above, which suggested the relationship between age, plasma homocysteine level and the formation of carotid atherosclerotic plaque [ 23 ]. Our study also found that there was a significant correlation between HHcy and carotid atherosclerotic plaque in people ≥ 55 years old, but no such difference was found in people < 55 years old. Some studies have shown that Hcy can accelerate cell aging through a variety of mechanisms. Long-term exposure of endothelial cells to homocysteine increases the expression of two surface molecules related to vascular diseases through the redox pathway, namely, intracellular adhesion molecule-1 (ICAM-1) and plasminogen activator inhibitor-1 (PAI-1). These two factors are related to the pathogenesis of atherosclerosis [ 24 ]. Cultured endothelial progenitor cells (EPC) are exposed to Hcy, the precursor of mature endothelial cells, which reduces proliferation and increases EPC senescence through decreased telomerase activity and Akt phosphorylation [ 25 ]. Therefore, the effect of age on atherosclerosis is closely related to the change of Hcy metabolism. About 72% of the population in this study have hypertension, which is higher than that of the general population. Graham [ 26 ] and other multi-center case-control studies found that the risk of cardiovascular and cerebrovascular events in patients with hyperhomocysteinemia or hypertension was increased compared with the normal blood pressure population without increased Hcy; When these two risk factors exist at the same time, the risk of cardiovascular and cerebrovascular events increases significantly. Chen[ 27 ] and others also found that the risk of atherosclerotic plaque in patients with hypertension and hyperhomocysteinemia was 1.63 times higher than that in patients with hypertension alone. This shows that hypertension and hyperhomocysteinemia have a synergistic effect on carotid atherosclerosis [ 28 , 29 ]. It may be because high Hcy will affect the total antioxidant status of vascular endothelium and the number of endothelial progenitor cells (EPCs) in hypertensive patients, aggravate vascular endothelial dysfunction, and promote the thickening of carotid IMT and plaque formation[ 28 , 30 ]. Therefore, the screening of blood Hcy and hypertension and the intervention treatment can effectively prevent the risk of carotid plaque in the high-risk group of strokes, which is of great significance to effectively prevent the onset of stroke, and is conducive to improving the management level of the high-risk group of stroke. Previous studies have confirmed that LDL-C is an independent risk factor for carotid atherosclerotic plaque in the general population. However, our research data did not find this correlation. Even after adjusting for gender, age, hypertension, diabetes and other confounding factors. At the same time, we did not find any correlation between carotid atherosclerotic plaque and TC, TG. The reason for this difference may be that our study population is at high risk of stroke, which is different from the general population. This study found that overweight had a slight protective effect on carotid plaque (OR = 0.737, P < 0.001). This study used BMI to define overweight, which may be the reason for this difference. Previous studies have shown that waist/hip ratio (WHR) more accurately reflects visceral fat accumulation and central fat distribution than BMI[ 31 ], and is more closely related to vascular endothelial dysfunction [ 32 ]and adverse cardiovascular events [ 33 ]. Although our research object is a high-risk group of strokes in a province of China, it provides new insights into the relationship between Hhcy and the risk of carotid atherosclerotic plaque. At the same time, this study has several limitations worth commenting on. First, the particularity of the research object. 1. The residence is in northern China. 2. Middle-aged and elderly people older than 40. 3. Participants have three or more stroke risk factors or past TIA or past stroke history. The prevalence of carotid plaque and the level of HCY in this study cannot be extrapolated to the general population and other regions. Second, the level of folate and B vitamins in plasma will affect the concentration of homocysteine. No data of folate serum and vitamin b12 concentration were collected in this study. Third, we only consider the existence of carotid plaque, but not its size and nature. Fourth: Our research is observational, cross-sectional, and single-centered. Therefore, we cannot confirm the causal relationship between HCY and plaque. Early disease prevention is the core value of health management. For the high-risk population of stroke, once carotid plaque is found, no matter what type of plaque, the condition is stable or not, early intervention measures should be taken. 5 Conclusions We conclude that hyperhomocysteinemia is an independent risk factor for carotid atherosclerotic plaque in stroke high-risk population. Declarations Acknowledgements This research was funded by The Shandong Provincial Natural Science Foundation of China, grant number ZR2023MH339. Author Contributions Conceptualization, Shenna Niu and Long Tian; methodology, Jie Zhang and Yan Gao; software, Huicong Xiao; validation, Shumei Yao and Hong Chen; formal analysis, Shenna Niu and Long Tian; investigation, Yan Gao; writing--original draft preparation, Shenna Niu; writing--review and editing, Chuanqiang Qu; All authors have read and agreed to the published version of the manuscript.” Conflicts of interest The authors declare no conflict of interest. Ethical statements This study has been approved by the ethics committee of the Provincial Hospital Affiliated to Shandong First Medical University. The research protocol is consistent with the Helsinki Declaration (2013, Brazil Revision). Written informed consent had been obtained from all individuals before they were included in the study. Data Availability The simulation experiment data used to support the findings of this study are available from the corresponding author upon request. 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Poddar R, Sivasubramanian N, DiBello PM, Robinson K, Jacobsen DW. Homocysteine induces expression and secretion of monocyte chemoattractant protein-1 and interleukin-8 in human aortic endothelial cells: implications for vascular disease. Circulation. 2001;103(22):2717–23. Koklesova L, Mazurakova A, Samec M, Biringer K, Samuel SM, Büsselberg D, Kubatka P, Golubnitschaja O. Homocysteine metabolism as the target for predictive medical approach, disease prevention, prognosis, and treatments tailored to the person. EPMA J. 2021;12(4):477–505. Stühlinger MC, Tsao PS, Her JH, Kimoto M, Balint RF, Cooke JP. Homocysteine impairs the nitric oxide synthase pathway: role of asymmetric dimethylarginine. Circulation. 2001;104(21):2569–75. Liang C, Wang QS, Yang X, Zhu D, Sun Y, Niu N, Yao J, Dong BH, Jiang S, Tang LL, Lou J, Yu CJ, Shao Q, Wu MM, Zhang ZR. Homocysteine Causes Endothelial Dysfunction via Inflammatory Factor-Mediated Activation of Epithelial Sodium Channel (ENaC). Front cell Dev biology. 2021;9:672335. Dietrich M, Jacques PF, Polak JF, Keyes MJ, Pencina MJ, Evans JC, Wolf PA, Selhub J, Vasan RS, D'Agostino RB. Segment-specific association between plasma homocysteine level and carotid artery intima-media thickness in the Framingham Offspring Study. J stroke Cerebrovasc diseases: official J Natl Stroke Association. 2011;20(2):155–61. Xu D, Neville R, Finkel T. Homocysteine accelerates endothelial cell senescence. FEBS Lett. 2000;470(1):20–4. Zhu JH, Chen JZ, Wang XX, Xie XD, Sun J, Zhang FR. Homocysteine accelerates senescence and reduces proliferation of endothelial progenitor cells. J Mol Cell Cardiol. 2006;40(5):648–52. Graham IM, Daly LE, Refsum HM, Robinson K, Brattström LE, Ueland PM, Palma-Reis RJ, Boers GH, Sheahan RG, Israelsson B, Uiterwaal CS, Meleady R, McMaster D, Verhoef P, Witteman J, Rubba P, Bellet H, Wautrecht JC, de Valk HW, Sales Lúis AC, Parrot-Rouland FM, Tan KS, Higgins I, Garcon D, Andria G, et al. Plasma homocysteine as a risk factor for vascular disease. The European Concerted Action Project. JAMA. 1997;277(22):1775–81. Chen Z, Wang F, Zheng Y, Zeng Q, Liu H. H-type hypertension is an important risk factor of carotid atherosclerotic plaques. Clinical and experimental hypertension (New York, NY : 1993). 2016;38(5):424–428. Zhang Z, Fang X, Hua Y, Liu B, Ji X, Tang Z, Wang C, Guan S, Wu X, Liu H, Gu X. Combined Effect of Hyperhomocysteinemia and Hypertension on the Presence of Early Carotid Artery Atherosclerosis. J stroke Cerebrovasc diseases: official J Natl Stroke Association. 2016;25(5):1254–62. Zhou F, Hou D, Wang Y, Yu D. Evaluation of H-type hypertension prevalence and its influence on the risk of increased carotid intima-media thickness among a high-risk stroke population in Hainan Province, China. Medicine. 2020;99(35):e21953. Bogdanski P, Miller-Kasprzak E, Pupek-Musialik D, Jablecka A, Lacinski M, Jagodzinski PP, Jakubowski H. Plasma total homocysteine is a determinant of carotid intima-media thickness and circulating endothelial progenitor cells in patients with newly diagnosed hypertension. Clin Chem Lab Med. 2012;50(6):1107–13. Snijder MB, van Dam RM, Visser M, Seidell JC. What aspects of body fat are particularly hazardous and how do we measure them? Int J Epidemiol. 2006;35(1):83–92. Brook RD, Bard RL, Rubenfire M, Ridker PM, Rajagopalan S. Usefulness of visceral obesity (waist/hip ratio) in predicting vascular endothelial function in healthy overweight adults. Am J Cardiol. 2001;88(11):1264–9. Lo K, Liu Q, Allison M, Feng YQ, Chan K, Phillips L, Manson J, Liu S. Prospective Associations of Waist-to-Height Ratio With Cardiovascular Events in Postmenopausal Women: Results From the Women's Health Initiative. Diabetes Care. 2019;42(9):e148–9. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4152280","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":284513182,"identity":"9c8be034-3836-40aa-b8df-3a77dafac6ac","order_by":0,"name":"Shenna Niu","email":"","orcid":"","institution":"Shandong Provincial Hospital Affiliated to Shandong First Medical University","correspondingAuthor":false,"prefix":"","firstName":"Shenna","middleName":"","lastName":"Niu","suffix":""},{"id":284513183,"identity":"c1325f3b-07ad-4450-83d5-34d11e73a715","order_by":1,"name":"Long Tian","email":"","orcid":"","institution":"Heze Municipal Hospital","correspondingAuthor":false,"prefix":"","firstName":"Long","middleName":"","lastName":"Tian","suffix":""},{"id":284513184,"identity":"33ea85e3-590a-4789-a9c5-9d273c294c6f","order_by":2,"name":"Jie Zhang","email":"","orcid":"","institution":"Zouping People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jie","middleName":"","lastName":"Zhang","suffix":""},{"id":284513186,"identity":"dec625d0-0509-40c1-bc04-7a65d84383a1","order_by":3,"name":"Yan Gao","email":"","orcid":"","institution":"Shandong Provincial Hospital Affiliated to Shandong First Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yan","middleName":"","lastName":"Gao","suffix":""},{"id":284513187,"identity":"60ec8c6f-350a-4f26-bbab-5ee5f4f609ff","order_by":4,"name":"Huicong Xiao","email":"","orcid":"","institution":"Shandong Provincial Hospital Affiliated to Shandong First Medical University","correspondingAuthor":false,"prefix":"","firstName":"Huicong","middleName":"","lastName":"Xiao","suffix":""},{"id":284513189,"identity":"c384afff-6aca-42f1-8ecb-bdf9d672f91e","order_by":5,"name":"Shumei Yao","email":"","orcid":"","institution":"Shandong Provincial Hospital Affiliated to Shandong First Medical University","correspondingAuthor":false,"prefix":"","firstName":"Shumei","middleName":"","lastName":"Yao","suffix":""},{"id":284513191,"identity":"3a09b247-1431-4b36-9c58-5bd00043937c","order_by":6,"name":"Chuanqiang Qu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7ElEQVRIiWNgGAWjYDCCA0DM2MAAI20Y2EjVkkaSFjA4TNhdfLcPb5P4ueOwvDl7cuPngl/n7fmkmx8w/KjYhlOL5Lm0MsneM4cNd/Y8bJae2Xc7sU3mmAFjz5nbOLUYnOExk2ZsO8y44UZigzRvz+0ENokEA2bGNsJa7IFamn/z9pyzZ5NI/0CUlkSgljZpnh8HGNskcvDbInmGrdiyty09ecOZh23WvA3JiUAtBQfx+YXvDPPGGz/brG03HE9/fJvnj529/Iz0jQ9+VODWAnIblE4ARlAbhHkAn3pULQx/CKgdBaNgFIyCEQkAP8VclTDLMS0AAAAASUVORK5CYII=","orcid":"","institution":"Shandong Provincial Hospital Affiliated to Shandong First Medical University","correspondingAuthor":true,"prefix":"","firstName":"Chuanqiang","middleName":"","lastName":"Qu","suffix":""},{"id":284513193,"identity":"bc44385c-0c8f-4469-8788-223585cdf3dd","order_by":7,"name":"Hong Chen","email":"","orcid":"","institution":"Shandong Provincial Hospital Affiliated to Shandong First Medical University","correspondingAuthor":false,"prefix":"","firstName":"Hong","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2024-03-23 00:59:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4152280/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4152280/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":53850217,"identity":"321f3f23-509d-46c5-ade7-572420f45021","added_by":"auto","created_at":"2024-04-01 09:49:15","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":232021,"visible":true,"origin":"","legend":"\u003cp\u003eThe area under curve (AUC) of the receiver operating characteristic (ROC) of the final training set and the validation set.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4152280/v1/4a25feb9b6103ddae82782c7.jpeg"},{"id":53986612,"identity":"1da59ca4-cb3e-4a11-a2c1-1aac7998964c","added_by":"auto","created_at":"2024-04-03 04:22:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":379842,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4152280/v1/00f8311a-6202-405e-8274-8a9ce116ca26.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Hyperhomocysteinemia Increases the Risk of Carotid Atherosclerotic Plaque in the High-Risk Group of Stroke: A Cross Sectional Study","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eWith the aging of the Chinese population, the disease burden of stroke continues to increase, and has become the leading cause of death in recent years [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Although stroke can be quickly diagnosed, primary prevention is still challenging, especially for high-risk stroke population [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Rupture and bleeding of atherosclerotic plaque, plaque shedding, or secondary vascular stenosis are important causes of ischemic stroke [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Many major modifiable risk factors of atherosclerosis have been identified, and the causal correlation of several risk factors has been fully confirmed (including but not limited to smoking, hypertension, hyperlipidemia, and diabetes) [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHomocysteine was also significantly associated with ischemic stroke [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. A prospective case-control study based on rural China found that total homocysteine (tHcy) level and systolic blood pressure (SBP) were the two most important risk factors, which had independent and cumulative effects on the risk of first stroke (tHcy: 1.06; SBP: 1.13; P-interaction, 0.889) [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In the study of the rat model of transient focal cerebral ischemia, it was also found that the increase of Hcy level would increase brain injury and neurotoxicity after cerebral ischemia/reperfusion, activate microglia and induce TNF- α and IL-6[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. However, the accumulated research mainly focused on the relationship between homocysteine and vascular disease. So far, there are few studies on the relationship between homocysteine and atherosclerotic plaque, and they are usually in the general population. This study is a cross-sectional study carried out in the high-risk population of stroke in East China. The correlation between hyperhomocysteinemia and carotid atherosclerotic plaque was evaluated through multiple machine learning models. We hope that the results of this study can provide better prevention and management strategies for people at high risk of stroke.\u003c/p\u003e"},{"header":"2 Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003e2.1 \u0026nbsp; Ethical Considerations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study has been approved by the ethics committee of the Provincial Hospital Affiliated to Shandong First Medical University. The research protocol is consistent with the Helsinki Declaration (2013, Brazil Revision). Written informed consent had been obtained from all individuals before they were included in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Study Population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis large-population based retrospective study was conducted by cluster sampling in 18 urban areas and 18 rural areas of Shandong Province, a representative province in eastern China (with a large population and rapid economic development), and identified 231289 (108230 men and 123059 women) permanent residents over the age of 40 (date of birth from January 1, 1937 to December 31, 1971). Residents living in the local area for\u0026nbsp;\u0026ge;\u0026nbsp;6 months can also be defined as the screening subjects. The number of people screened in each region should not be fewer than 6000.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 \u0026nbsp;Data Collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data were collected through a national questionnaire which included: 1. basic information of the respondents, including age, gender, BMI, exercise status, community, education level, smoking history. 2. previous history of hypertension, atrial fibrillation, diabetes, dyslipidemia, transient ischemic attack (TIA), and stroke. 3. family history of stroke. The evaluation criteria for stroke risk factors are as follows:\u0026nbsp;①\u0026nbsp;Hypertension: previous history of hypertension diagnosed by hospitals graded level 2 or above; or the current use of antihypertensives; or the screening measurement results showed that high blood pressure (systolic blood pressure\u0026nbsp;\u0026ge;\u0026nbsp;140 and / or diastolic blood pressure\u0026nbsp;\u0026ge;\u0026nbsp;90mmHg).\u0026nbsp;②\u0026nbsp;Atrial fibrillation (AF): previous history of atrial fibrillation diagnosed by a hospital graded level 2 or above; or atrial fibrillation shown by electrocardiogram in this screening.\u0026nbsp;③\u0026nbsp;Dyslipidemia: previous history of dyslipidemia diagnosed by hospitals graded level 2 or above; or one or more abnormalities (total cholesterol\u0026nbsp;\u0026ge;\u0026nbsp;5.70mmol/l, triglyceride\u0026nbsp;\u0026ge;\u0026nbsp;1.70mmol/l, LDLC\u0026nbsp;\u0026ge;\u0026nbsp;3.10mmol/l)\u0026nbsp;④\u0026nbsp;Diabetes: previous history of diabetes diagnosed by hospitals graded level 2 or above; treatment with insulin or oral hypoglycemic medications; or a fasting blood glucose\u0026nbsp;\u0026ge;\u0026nbsp;126mg/dl or glycosylated hemoglobin\u0026nbsp;\u0026ge;\u0026nbsp;6.5% in this screening.\u0026nbsp;⑤\u0026nbsp;Smoking: continuous or cumulative smoking for more than 6 months.\u0026nbsp;⑥\u0026nbsp;Lack of sports: \u0026nbsp;physical exercise＜3 times / week and each＜30min in duration (industrial and agricultural labor is regarded as physical exercise).\u0026nbsp;⑦\u0026nbsp;Obesity: body mass index\u0026ge;25kg/m2.\u0026nbsp;⑧\u0026nbsp;Family history of stroke: self-reported family history of stroke.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe above risk factors of all screening populations were assessed. Those individuals who have three or more stroke risk factors; or previous TIA, or previous stroke were defined as stroke high-risk groups. We selected 22192 participants of stroke high-risk groups as our subjects, accounting for 9.5% of all screening populations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4 Ultrasonic examination\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll patients who were defined as stroke high risk group were screened by bilateral carotid artery ultrasound. The intimal thickness of carotid artery was observed and the number of plaques in bilateral carotid arteries was recorded. The local carotid intima thickness\u0026ge;1.5mm is considered as the formation of carotid atherosclerotic plaque[10].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5 Statistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKolmogorov-Smirnov test was used to estimate the normality of continuous variables. Continuous variables were reported as means\u0026plusmn;standard deviations and t-test was applied for statistical analysis. The continuous variables conforming to the skew distribution were expressed by the median (interquartile range) and statistically analyzed by Mann Whitney U test. Categorical variables were described by percentage and analyzed using the chi-square test. Binary logistic regression was used to evaluate relationships between each risk factor and carotid atherosclerotic plaque. All statistical tests were two-tailed, and the significance level was set to 0.05. All data were analyzed by R4.2.1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.6 Machine learning modeling\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFive machine learning models were applied, including logistic regression, random forest (RF), support vector machine (SVM), K-nearest neighbours (KNN)，and Naive Bayes classifier. Logistic regression is a widely used statistical method used for analyzing the relationship between a dependent variable and one or more independent variables. It is commonly used in binary classification problems. The goal of logistic regression is to predict the probability of the dependent variable taking a particular value based on the values of the independent variables. The logistic function allows us to transform any linear combination of the independent variables into a probability value between 0 and 1, which can then be used to make a binary classification decision. Random forest is an ensemble learning method that builds a multitude of decision trees and aggregates their predictions to arrive at a final output. The algorithm works by randomly selecting a subset of features and data samples from the training set and building decision trees based on these subsets. This process is repeated multiple times, resulting in a \u0026quot;forest\u0026quot; of decision trees. Each decision tree in the forest is trained on a different subset of data and features, making them less prone to overfitting and improving the overall accuracy and stability of the model. Support Vector Machine (SVM) is a supervised learning method that constructs a hyperplane or set of hyperplanes in a high-dimensional space to separate data points into different classes. SVM algorithm finds the hyperplane that maximizes the margin between the closest points of each class, also known as the support vectors. The margin is the distance between the hyperplane and the support vectors, and maximizing it helps to increase the robustness and generalization of the model. k-Nearest Neighbor (k-NN) is a simple and popular machine learning algorithm used for both classification and regression tasks. It is a non-parametric and lazy learning algorithm that does not make any assumptions about the underlying data distribution. The k-NN algorithm works by finding the k closest training data points to a new data point and then using the labels or values of those points to make a prediction. In the case of classification, the algorithm assigns the most frequent class label among the k-nearest neighbors to the new data point. Naive Bayes is based on Bayes\u0026apos; theorem, which states that the probability of a hypothesis (a targeted class label) given the observed evidence (the features of a data point) is proportional to the probability of the evidence given the hypothesis, multiplied by the prior probability of the hypothesis.\u003c/p\u003e"},{"header":"3 Results","content":"\u003cp\u003e \u003cb\u003e3.1 clinical and demographic characteristics of subjects\u003c/b\u003e \u003c/p\u003e \u003cp\u003e22792 participants participated in the screening, of which 17006 (76.6%) had carotid atherosclerotic plaque. The average age of the subjects was 62.9\u0026thinsp;\u0026plusmn;\u0026thinsp;9.5 years old. In addition, 6199 (27.9%) did not suffer from hypertension, 10582 (51.5%) did not suffer from hyperhomocysteinemia (HHcy), 17606 (79.3%) did not suffer from diabetes, 18783 (84.6%) did not suffer from atrial fibrillation, 15877 (71.5%) did not have a history of smoking, 13403 (60.4%) did not lack physical exercise, and 11256 (50.7%) were not overweight.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e lists the baseline characteristics of subjects with and without carotid atherosclerotic plaque. We found that the average age of participants with carotid atherosclerotic plaque was higher than those without carotid atherosclerotic plaque (63.9\u0026thinsp;\u0026plusmn;\u0026thinsp;9.4 and 50.9\u0026thinsp;\u0026plusmn;\u0026thinsp;9.3, respectively). As shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the incidence rate of men, hypertension, smoking, diabetes, and HHcy (50.9%, 73.8%, 29.5%, 21.2%, and 53.9%, respectively) in subjects with carotid atherosclerotic plaque is also higher than that in subjects without carotid atherosclerotic plaque (43%, 66.2%, 25.1%, 18.8%, and 47.1%, respectively). However, the TG and LDL-C level in the subjects with carotid atheroscleroic plaque was significantly lower compared with that found in the subjects without carotid atherosclerotic plaques (1.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7 vs 1.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4; 3.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2 vs 3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1, respectively).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e༎Baseline characteristics of the study cohort.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eCarotid plaques\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOverall(n\u0026thinsp;=\u0026thinsp;22192)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWithout(n\u0026thinsp;=\u0026thinsp;5186)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWith(n\u0026thinsp;=\u0026thinsp;17006)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years(SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e59.9 (9.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e63.9 (9.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e62.9 (9.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender(Male)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,228 (43.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8,650 (50.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10,878 (49.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4,398 (84.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14,585 (85.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18,983 (85.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.090\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension(No)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,751 (33.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4,448 (26.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6,199 (27.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAF(No)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4,434 (85.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14,349 (84.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18,783 (84.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking(No)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,882 (74.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11,995 (70.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15,877 (71.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes(No)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4,213 (81.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13,393 (78.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17,606 (79.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSportsLack(No)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,105 (59.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10,298 (60.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13,403 (60.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.388\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObesity(No)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,271 (43.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8,985 (52.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11,256 (50.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStrokeFamily(No)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,618 (69.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12,427 (73.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16,045 (72.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHhcy(No)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,745 (56.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7,837 (49.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10,582 (51.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG(SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.8 (1.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.8 (1.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.8 (1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC(SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5 (1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5 (1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5 (1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.499\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDLC(SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.1 (1.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3 (1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3 (1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe chi-square test was used to compare the characteristics of different age groups of participants with and without carotid plaque (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Among subjects younger than 55 years old, there are significant differences between gender, hypertension, smoking, diabetes, overweight and carotid atherosclerotic plaque, but there are significant differences between gender, hypertension, smoking, overweight and HHcy when the age is older than 55 years old.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of patients with and without carotid atherosclerotic plaque at different ages groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eAge\u0026lt;55 years(n\u0026thinsp;=\u0026thinsp;4093)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eAge\u0026thinsp;\u0026ge;\u0026thinsp;55 years(n\u0026thinsp;=\u0026thinsp;18099)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eX\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eX\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30.947\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e91.531\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e55.582\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e50.694\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16.678\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.582\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.208\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13.427\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e85.509\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHHcy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.386\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.534\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e45.267\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe input variables for the feature selected data included age, sex, hypertension, diabetes, smoking overweight, stroke history, family stroke history, hyperhomocysteinemia, LDLC, sports, TG, TC. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the potential variables that predict patients at risk of plaque.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelations between clinical characteristics and carotid atherosclerotic plaques in study cohort\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eOne-way logistic regression\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.367\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.386\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStrokeFamily\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHHcy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.285\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSportsLack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.373\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.172\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.687\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e3.2 Evaluation of regression model\u003c/b\u003e \u003c/p\u003e \u003cp\u003eBased on the baseline data of 22192 subjects, a Logistic regression machine learning model was established. 70% of the cases were selected to build the training database, and 30% of the cases were used as the validation database. The area under curve (AUC) of the receiver operating characteristic (ROC) of the final training set and the validation set were 0.713 and 0.720(Figure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), respectively.\u003c/p\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, multivariate logistic regression results show that hypertension (OR\u0026thinsp;=\u0026thinsp;1.278 P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), diabetes (OR\u0026thinsp;=\u0026thinsp;1.163 P\u0026thinsp;=\u0026thinsp;0.004), HHcy (OR\u0026thinsp;=\u0026thinsp;1.170 P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), smoking (OR\u0026thinsp;=\u0026thinsp;1.163 P\u0026thinsp;=\u0026thinsp;0.004) and age (OR\u0026thinsp;=\u0026thinsp;1.050 P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) are risk factors for carotid plaque formation, and women (OR\u0026thinsp;=\u0026thinsp;0.635 P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and overweight (OR\u0026thinsp;=\u0026thinsp;0.737 P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) are protective factors for carotid plaque. LDL-C statistics show no significant difference.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariate logistic regression\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eMultivariate logistic regression\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.365\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.263\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStrokeFamily\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.972\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHHcy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.156\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe AUROCs for the test_set data for all machine learning techniques for predicting osteoporosis risk are shown in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. For the analysis of data containing 9 variables, the logistic model achieved the best performance at AUROC (0.720), followed by Bayes (0.708), and KNN (0.665). SVM with random forest did not work well.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of multiple machine learning models\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLogistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSVM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eKNN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBayes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRandom Forest\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecutoff\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.792 (0.765, 0.818)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.500 (0.500, 0.500)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.161 (0.078, 0.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.168 (0.156, 0.217)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.500 (0.500, 0.500)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eauc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.720 (0.706, 0.734)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.502 (0.500, 0.504)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.665 (0.650, 0.681)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.708 (0.693, 0.723)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.542 (0.533, 0.550)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003espe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.784 (0.709, 0.854)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.005 (0.001, 0.009)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.704 (0.604, 0.814)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.834 (0.723, 0.866)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.117 (0.101, 0.134)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.552 (0.476, 0.626)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.999 (0.999, 1.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.561 (0.442, 0.652)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.494 (0.459, 0.607)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.966 (0.960, 0.970)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enpv\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.347 (0.329, 0.368)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.714 (0.400, 1.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.323 (0.303, 0.349)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.333 (0.323, 0.356)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.510 (0.457, 0.561)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eppv\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.893 (0.875, 0.916)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.768 (0.767, 0.769)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.863 (0.843, 0.888)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.907 (0.877, 0.921)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.783 (0.780, 0.786)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eIn this cross-sectional study based on East China, we observed the association between hyperhomocysteinemia and carotid atherosclerotic plaque risk in 22192 high-risk stroke patients over 40 years old. In addition, this study shows that hyperhomocysteinemia is an independent indicator of the presence of carotid plaque in the high-risk population of stroke. Although many clinical studies have shown that hyperhomocysteinemia (Hhcy) is one of the main risk factors of cardiovascular disease (CVD), stroke and new-onset hypertension [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], It is even related to the adverse outcome of acute ischemic stroke[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], but few people have discussed the relationship between Hhcy and carotid plaque in the high-risk population of stroke. A cross-sectional study conducted by the Framingham Heart Research Center on 1041 subjects showed that subjects with the highest plasma homocysteine concentration (\u0026gt;\u0026thinsp;or =\u0026thinsp;14.4 ummol/L) had a 2-fold increased risk of extracranial carotid artery stenosis compared with those with the lowest concentration (\u0026lt;\u0026thinsp;or =\u0026thinsp;9.1 ummol/L)[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. However, the subjects they studied were elderly people (67 to 96 years old) and non-Asians. A recent observational study of other high-risk groups, such as patients with uremia, hypercholesterolemia, type 2 diabetes, insulin resistance or nervous system abnormalities, failed to prove the association between plasma homocysteine level and carotid intima media thickness (IMT) [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Therefore, our study confirmed the association between hyperhomocysteinemia and the risk of carotid atherosclerotic plaque in the high-risk population of stroke in China.\u003c/p\u003e \u003cp\u003eThe northern Manhattan study (NOMAS) based on a multi-ethnic cohort showed that the increase of Hcy was independently related to the increase of carotid plaque morphology and plaque area [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Similarly, this was also found in a cross-sectional study of Chinese adults [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. HHcy-induced vascular injury and the subsequent CVD mechanism include endothelial injury, promoting the proliferation of vascular smooth muscle cells, leading to inflammation and prethrombotic state, and oxidative stress response[\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. These pathological changes caused by HHcy are manifested as impaired vasodilation mediated by blood flow, mainly due to the reduction of NO production and bioavailability. This systemic pathological state is defined as endothelial dysfunction, which is considered to be the core of atherosclerotic plaque formation and CVD process[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. After translation, Hcy can down-regulate the activity of dimethylarginine dimethylaminohydrolase (the enzyme that degrades ADMA), resulting in the accumulation of asymmetric dimethylarginine (ADMA, the endogenous inhibitor of NO synthase), thus inhibiting NO synthesis [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Liang et al. found that the ROS/COX-2 dependent activation of endothelial ENaC (epithelial sodium channel, the ion channel recently found in endothelial cells) by Hcy through SGK-1/Nedd4-2 signal transduction led to endothelial dysfunction [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. These studies describe the pathophysiological mechanism of HHcy impairing endothelium-mediated NO dependent vasodilation.\u003c/p\u003e \u003cp\u003eThe research of Framingham's descendants showed that there was a significant positive trend between the increase of plasma total homocysteine level and the IMT of the internal carotid artery/bulb in the population aged 58 and above, which suggested the relationship between age, plasma homocysteine level and the formation of carotid atherosclerotic plaque [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Our study also found that there was a significant correlation between HHcy and carotid atherosclerotic plaque in people\u0026thinsp;\u0026ge;\u0026thinsp;55 years old, but no such difference was found in people\u0026thinsp;\u0026lt;\u0026thinsp;55 years old. Some studies have shown that Hcy can accelerate cell aging through a variety of mechanisms. Long-term exposure of endothelial cells to homocysteine increases the expression of two surface molecules related to vascular diseases through the redox pathway, namely, intracellular adhesion molecule-1 (ICAM-1) and plasminogen activator inhibitor-1 (PAI-1). These two factors are related to the pathogenesis of atherosclerosis [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Cultured endothelial progenitor cells (EPC) are exposed to Hcy, the precursor of mature endothelial cells, which reduces proliferation and increases EPC senescence through decreased telomerase activity and Akt phosphorylation [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Therefore, the effect of age on atherosclerosis is closely related to the change of Hcy metabolism.\u003c/p\u003e \u003cp\u003eAbout 72% of the population in this study have hypertension, which is higher than that of the general population. Graham [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] and other multi-center case-control studies found that the risk of cardiovascular and cerebrovascular events in patients with hyperhomocysteinemia or hypertension was increased compared with the normal blood pressure population without increased Hcy; When these two risk factors exist at the same time, the risk of cardiovascular and cerebrovascular events increases significantly. Chen[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] and others also found that the risk of atherosclerotic plaque in patients with hypertension and hyperhomocysteinemia was 1.63 times higher than that in patients with hypertension alone. This shows that hypertension and hyperhomocysteinemia have a synergistic effect on carotid atherosclerosis [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. It may be because high Hcy will affect the total antioxidant status of vascular endothelium and the number of endothelial progenitor cells (EPCs) in hypertensive patients, aggravate vascular endothelial dysfunction, and promote the thickening of carotid IMT and plaque formation[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Therefore, the screening of blood Hcy and hypertension and the intervention treatment can effectively prevent the risk of carotid plaque in the high-risk group of strokes, which is of great significance to effectively prevent the onset of stroke, and is conducive to improving the management level of the high-risk group of stroke.\u003c/p\u003e \u003cp\u003ePrevious studies have confirmed that LDL-C is an independent risk factor for carotid atherosclerotic plaque in the general population. However, our research data did not find this correlation. Even after adjusting for gender, age, hypertension, diabetes and other confounding factors. At the same time, we did not find any correlation between carotid atherosclerotic plaque and TC, TG. The reason for this difference may be that our study population is at high risk of stroke, which is different from the general population. This study found that overweight had a slight protective effect on carotid plaque (OR\u0026thinsp;=\u0026thinsp;0.737, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This study used BMI to define overweight, which may be the reason for this difference. Previous studies have shown that waist/hip ratio (WHR) more accurately reflects visceral fat accumulation and central fat distribution than BMI[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], and is more closely related to vascular endothelial dysfunction [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]and adverse cardiovascular events [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAlthough our research object is a high-risk group of strokes in a province of China, it provides new insights into the relationship between Hhcy and the risk of carotid atherosclerotic plaque. At the same time, this study has several limitations worth commenting on. First, the particularity of the research object. 1. The residence is in northern China. 2. Middle-aged and elderly people older than 40. 3. Participants have three or more stroke risk factors or past TIA or past stroke history. The prevalence of carotid plaque and the level of HCY in this study cannot be extrapolated to the general population and other regions. Second, the level of folate and B vitamins in plasma will affect the concentration of homocysteine. No data of folate serum and vitamin b12 concentration were collected in this study. Third, we only consider the existence of carotid plaque, but not its size and nature. Fourth: Our research is observational, cross-sectional, and single-centered. Therefore, we cannot confirm the causal relationship between HCY and plaque. Early disease prevention is the core value of health management. For the high-risk population of stroke, once carotid plaque is found, no matter what type of plaque, the condition is stable or not, early intervention measures should be taken.\u003c/p\u003e"},{"header":"5 Conclusions","content":"\u003cp\u003eWe conclude that hyperhomocysteinemia is an independent risk factor for carotid atherosclerotic plaque in stroke high-risk population.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was funded by The Shandong Provincial Natural Science Foundation\u0026nbsp;of\u0026nbsp;China,\u0026nbsp;grant number ZR2023MH339.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization, Shenna Niu\u0026nbsp;and\u0026nbsp;Long Tian; methodology, Jie Zhang\u0026nbsp;and\u0026nbsp;Yan Gao; software, Huicong Xiao; validation, Shumei Yao\u0026nbsp;and\u0026nbsp;Hong Chen; formal analysis, Shenna Niu\u0026nbsp;and\u0026nbsp;Long Tian; investigation, Yan Gao; writing--original draft preparation, Shenna Niu; writing--review and editing, Chuanqiang Qu; All authors have read and agreed to the published version of the manuscript.\u0026rdquo;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical statements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study has been approved by the ethics committee of the Provincial Hospital Affiliated to Shandong First Medical University. The research protocol is consistent with the Helsinki Declaration (2013, Brazil Revision). Written informed consent had been obtained from all individuals before they were included in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe simulation experiment data used to support the findings of this study are available from the corresponding author upon request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors agree with the publication of the research study titled \u0026ldquo;Hyperhomocysteinemia Increases the Risk of Carotid Atherosclerotic Plaque in the High-Risk Group of Stroke: A Cross Sectional Study in BMC Neuroscience.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWu S, Wu B, Liu M, Chen Z, Wang W, Anderson CS, Sandercock P, Wang Y, Huang Y, Cui L, Pu C, Jia J, Zhang T, Liu X, Zhang S, Xie P, Fan D, Ji X, Wong KL, Wang L. 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Diabetes Care. 2019;42(9):e148\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Stroke, Hyperhomcysteinemia, Atherosclerotic, Homcysteine, Risk factors","lastPublishedDoi":"10.21203/rs.3.rs-4152280/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4152280/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHyperhomocysteinemia (Hhcy) is associated with ischemic stroke. Controlling or reversing the progression of atherosclerotic plaque is essential to prevent ischemic stroke. The purpose of this study was to explore the relationship between hyperhomocysteinemia and the risk of carotid atherosclerotic plaque in the high-risk population of stroke in China. We included the high-risk population of stroke over 40 years old in East China for analysis, measured the plasma total homocysteine level, and evaluated the carotid atherosclerotic plaque by ultrasound. After adjusting for demographic and vascular risk factors, multiple machine models were applied to analyze the correlation between hyperhomocysteinemia and carotid atherosclerotic plaque. The logistic model achieved the best performance at AUROC (0.720), followed by Bayes (0.708), and KNN (0.665). SVM with random forest did not work well. The results showed that 17006 (76.6%) of 22192 subjects had carotid atherosclerotic plaque. Among the population\u0026thinsp;≧\u0026thinsp;55 years old, HHcy was significantly associated with carotid atherosclerotic plaque. HHcy (OR\u0026thinsp;=\u0026thinsp;1.17, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) is a risk factor of carotid atherosclerotic plaque. We conclude that hyperhomocysteinemia is an independent risk factor for carotid atherosclerotic plaque in stroke high-risk population.\u003c/p\u003e","manuscriptTitle":"Hyperhomocysteinemia Increases the Risk of Carotid Atherosclerotic Plaque in the High-Risk Group of Stroke: A Cross Sectional Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-01 09:49:10","doi":"10.21203/rs.3.rs-4152280/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"60af463b-e1f3-4755-b175-c4059080f341","owner":[],"postedDate":"April 1st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-04-03T04:06:01+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-01 09:49:10","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4152280","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4152280","identity":"rs-4152280","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}