Association of the Atherogenic Index of Plasma and High-Sensitivity C-Reactive Protein with Incident Stroke Among Individuals Without Diabetes: A National Cohort Study

Association of the Atherogenic Index of Plasma and High-Sensitivity C-Reactive Protein with Incident Stroke Among Individuals Without Diabetes: A National Cohort Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Association of the Atherogenic Index of Plasma and High-Sensitivity C-Reactive Protein with Incident Stroke Among Individuals Without Diabetes: A National Cohort Study Xiaotong Yao, Liting Liang, Manting Yang, Zhuoji Liang, Ying Piao, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5831890/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Background Both the atherogenic index of plasma (AIP), a surrogate marker of insulin resistance, and high-sensitivity C-reactive protein (hsCRP) are predictors of stroke risk and clinical outcomes. However, most existing evidence is derived from studies involving diabetic patients, which may lead to the overestimation of the impact of the AIP and hsCRP on stroke due to the confounding effects of diabetes. This study aimed to assess the combined and interactive effects of the AIP and hsCRP on stroke events in individuals without diabetes. Methods A total of 8,909 participants from the China Health and Retirement Longitudinal Study (CHARLS) 2011 who were free of stroke and diabetes at baseline were included. The AIP was calculated as lg[total cholesterol (mmol/L)/high-density lipoprotein cholesterol (mmol/L)]. A subset of 5,954 participants was studied to investigate the relationship between cumulative AIP (CumAIP) and hsCRP (CumAIP) exposure and stroke incidence.The CumAIP and CumCRP were also calculated using the same algorithm.The primary outcome was physician-diagnosed stroke occurring before 2020. We employed adjusted Cox proportional hazards regression and mediation analysis to investigate the associations between the AIP, hsCRP, and stroke events. Results Over nine years of follow-up, 696 new stroke cases were recorded.Compared with individuals with low AIP (<0.302 [median level]) and hsCRP <1 mg/L, those with elevated levels of both the AIP and hsCRP had the highest overall risk of stroke (adjusted HR [aHR]: 1.69; 95% CI: 1.36–2.10). In a 5-year subset analysis, 497 participants suffered a stroke. Compared with individuals with low risk (CumAIP<1.29 [median level] and CumhsCRP < 4.02 mg/L [median level]), those with high risk had the highest overall risk of stroke (adjusted HR [aHR]: 1.41; 95% CI: 1.10-1.82). Moreover, hsCRP significantly mediated 5.61% of the association between the AIP and stroke, whereas the AIP mediated 1.86% of the association between hsCRP and stroke. Conclusions The AIP and hsCRP exhibit coexposure effects and mutual mediation in with regard to the risk of stroke. The combined assessment of the AIP and hsCRP should be promoted for residual risk stratification and primary prevention of stroke in individuals without diabetes, particularly among middle-aged populations. Atherogenic index of plasma (AIP) Insulin resistance Inflammation High-sensitivity C-reactive protein (hsCRP) Stroke Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Stroke represents a leading cause of mortality and disability worldwide, posing substantial challenges to ageing populations and placing considerable economic and resource pressures on healthcare systems[ 1 ]. Over the past three decades, China has experienced a significant epidemiological transition from infectious diseases to noncommunicable diseases (NCDs)[ 2 ]. As in many other ageing societies, the high prevalence and multimorbidity of NCDs, including stroke, present unique challenges to the healthcare system[ 3 ]. According to China Stroke Statistics[ 4 ], chronic diseases cause the majority of deaths in China, with stroke being a prominent contributor. Projections suggest that the lifetime risk of stroke among Chinese residents will reach 39.3% over the next 25 years, significantly surpassing the global average of approximately 25%[ 5 ]. While traditional risk factors—such as age, hypertension, diabetes, hyperlipidaemia, and smoking—are well recognized and commonly integrated into predictive models, a notable proportion of stroke patients do not exhibit these conventional risk factors[ 6 ]. This highlights the urgent need to identify novel biomarkers to enable more precise risk stratification and to advance effective prevention strategies. Insulin resistance (IR) is a pathological state characterized by reduced sensitivity of target organs or tissues to insulin, leading to impaired glucose utilization, and is widely recognized as a significant driver of cardiovascular diseases and mortality[ 7 , 8 ]. The exact biological mechanisms linking insulin resistance (IR) and stroke remain unclear, but several have been hypothesized, including metabolic dysregulation, oxidative stress, endothelial dysfunction, enhanced inflammatory responses, and abnormal activation of the renin‒angiotensin‒aldosterone system (RAAS)[ 9 , 10 ]. In recent years, the AIP, defined as a logarithmic transformation of the ratio of the molar triglyceride (TG) concentration and high-density lipoprotein cholesterol (HDL-c) level, has gained recognition as a simple and reliable surrogate marker of IR[ 11 ]. In addition to serving as an indicator of IR, the AIP is closely associated with atherogenic dyslipidaemia[ 12 ]. This marker was first introduced by Dobiásová et al[ 13 ]. An increasing number of studies have demonstrated that elevated AIP levels are significantly associated with increased risks of stroke, coronary artery disease, and all-cause mortality, reinforcing its role as an emerging predictor of cardiovascular and cerebrovascular diseases[ 14 – 16 ]. Additionally, C-reactive protein (CRP), particularly high-sensitivity CRP (hsCRP), is a well-established biomarker of systemic inflammation and an independent predictor of cardiovascular risk[ 17 ]. While existing evidence highlights the independent roles of the AIP and hsCRP in cardiovascular diseases, their combined or synergistic effects on the risk of stroke remain underexplored and warrant further investigation. Current research indicates that IR is associated with stroke recurrence and poor functional outcomes in nondiabetic ischaemic stroke patients[ 18 , 19 ]. Inflammation and atherogenic dyslipidaemia are intricately interconnected, potentially working synergistically to exacerbate vascular damage and increase the risk of stroke[ 20 ]. Systemic inflammation may amplify dyslipidaemia, leading to endothelial dysfunction and atherosclerosis, whereas dyslipidaemia may further intensify inflammatory responses. However, the relationship between the AIP and hsCRP in stroke and their combined predictive value have not been adequately studied. Most existing research has focused on diabetic populations, which might lead to the overestimation of confounding of the role of IR, given the strong direct influence of diabetes on the risk of stroke. Investigating nondiabetic individuals allows the isolation of the independent contributions of the AIP and CRP, avoiding the confounding effects of diabetes and providing clearer insights into their roles. Furthermore, since nondiabetic individuals constitute the majority of the population, studying this group enhances the generalizability of findings and identifies broader, population-wide risk factors. To address this gap, we utilized data from the China Health and Retirement Longitudinal Study (CHARLS), which has a nationally representative prospective cohort, to investigate the associations between the AIP, CRP, and incident stroke in nondiabetic individuals. This study was performed with the aim of evaluating the combined impact of the AIP and hsCRP on the risk of stroke, assessing their joint capacity to enable risk reclassification, and exploring potential mediating mechanisms linking dyslipidaemia and inflammation, as reflected by hsCRP, to stroke. By integrating markers of dyslipidaemia and inflammation, to the intention was for this study to deepen the understanding of stroke risk factors and identify novel targets for early prevention efforts and interventions. Methods Study design and population This study is based on the China Health and Retirement Longitudinal Study (CHARLS), a nationally representative cohort study ( http://charls.pku.edu.cn/ ) that has been tracking the health of Chinese adults since 2011.Health surveys are conducted every two years, with the fifth round expected to be completed by 2020. The study employs a multi-stage, stratified probability sampling strategy to recruit participants from both rural and urban areas across 150 counties or districts in 28 provinces of China. In each round of the survey, trained staff collected data on sociodemographic characteristics, medical history, and lifestyle behaviours through face‒to‒face interviews using standardized questionnaires. This study included participants from the baseline wave (2011–2012) who underwent follow-up in 2013, 2015, 2018, and 2020. Individuals missing basic sociodemographic data (age and sex), blood sample test results, or baseline information on stroke, diabetes or cancer history were excluded. In total, 8,909 participants were included in the final analysis ( Fig. 1 ). Data Collection and Definitions In the baseline survey of 2011, CHARLS successfully collected fasting blood samples from participants. Prior to sampling, participants were asked to fast overnight. Samples were stored at local hospitals and then transported to Peking University in Beijing, where they were preserved at -80°C for future analysis. Lipid levels, including triglycerides (TG), low-density lipoprotein cholesterol (LDL cholesterol), high-density lipoprotein cholesterol (HDL cholesterol), and total cholesterol (TC), were measured using enzymatic colorimetric methods, while high-sensitivity C-reactive protein (hsCRP) levels were determined using immunoturbidimetric method. Exposure and Outcome Determination The baseline exposure in this study was the AIP, which was calculated via the following formula: AIP = lg[total cholesterol (mmol/L)/high-density lipoprotein cholesterol (mmol/L)]. CumAIP = [(AIP2012 + AIP2015) / 2] × (2015 − 2012), CumCRP = [(hsCRP2012 + hsCRP2015) / 2] × (2015 − 2012).The primary outcome of interest was the occurrence of stroke. Consistent with standard procedures, stroke was determined on the basis of self-reported data, where participants confirmed whether they had received a formal diagnosis of stroke from a physician[ 21 , 22 ]. Trained personnel asked participants the following survey question: “Have you been told by a doctor that you have been diagnosed with a stroke?” Participants were followed from baseline (2011) until either the occurrence of stroke or the most recent survey (2020), with the event date determined by whichever occurred first. Participants who reported a stroke prior to 2011 were excluded from the analysis. The time of stroke occurrence was calculated under different conditions: for participants who did not report a stroke during the final follow-up, the event time was defined as the difference between the year of the last survey and the baseline survey year. For participants who reported a stroke, the event time was calculated as the difference between the year of the first reported stroke and the baseline year. Covariates This study collected a variety of covariate information. Demographic factors include age, gender, education level, residence, marital status, as well as health indicators such as history of hypertension, chronic kidney disease, smoking, and alcohol consumption. Anthropometric measurements include height, weight,all measured according to standardized protocols. Body mass index (BMI) was calculated by dividing weight (in kilograms) by the square of height (in meters, kg/m²). Based on the World Health Organization (WHO) guidelines for the Chinese population, participants were categorized as underweight/normal weight (BMI < 24 kg/m²), overweight (BMI ≥ 24 kg/m²) or obese(BMI ≥ 28 kg/m²). According to previous studies[ 23 ], education was categorized into three groups: primary school or below, middle school, and high school or above. Marital status was divided into two categories: married and others. Based on the response to the question "Are you taking medication to control dyslipidemia?" participants were classified into two groups: using lipid-lowering treatment and not using lipid-lowering treatment. A history of hypertension was determined by specific questionnaire responses, where participants were asked "Have you ever been diagnosed with hypertension by a doctor?" and "Are you currently using medication to control hypertension?" A positive response to either question was categorized as having a history of hypertension. Furthermore, individuals diagnosed with diabetes by a doctor, or those with fasting blood glucose levels ≥ 126 mg/dL or HbA1c levels ≥ 6.5%, were classified as having a history of diabetes. Statistical analysis Statistical analyses were conducted using RStudio version 4.3.2. A two-tailed P-value of less than 0.05 was deemed statistically significant. Continuous variables were summarized as either mean ± standard deviation (SD) or median (interquartile range), depending on the distribution of the data. Comparisons of baseline data for normally distributed and skewed data were performed using analysis of variance (ANOVA) and the Kruskal-Wallis H test, respectively. Categorical variables were presented as counts and percentages, with differences evaluated through chi-square tests. Trend tests were carried out based on the median values of the AIP quartiles.Multiple imputation to handle missing values was performed using the 'mice' package. To investigate the nonlinear relationship between the AIP and the risk of incident stroke, restricted cubic spline (RCS) regression was conducted. Cox proportional hazards regression models, implemented using the survival package, were employed to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for the associations among the AIP, CRP levels, and the risk of stroke. The AIP was analysed both as a continuous variable and categorized by quartiles or the median value (0.302), whereas CRP levels were classified according to predefined thresholds (< 1 mg/L and ≥ 1 mg/L, < 2 mg/L and ≥ 2 mg/L). The models were adjusted incrementally for potential confounders, including demographic factors, health indicators, and lifestyle behaviours. Subgroup analyses were performed to assess whether the relationship between the AIP and the risk of stroke varied across predefined subgroups. Stratified Cox regression models were employed to evaluate interactions between the AIP and key covariates. Mediation analysis, conducted using the “mediation” package, was used to examine the bidirectional mediating effects of the AIP and CRP on the risk of stroke, quantifying their respective contributions to the total effect. Time-dependent AUC Curve were established to assess the predictive value of AIP and hsCRP on incidence of stroke, and the C-statistic, was used to quantify. To further estimate additional the predictive power beyond the basic models, the net reclassification improvement (NRI) and integrated discrimination improvement (IDI) index were computed . Results Baseline characteristics of the study population Data are presented as the means ± standard deviations (SDs) for continuous variables and as frequencies with percentages for categorical variables.The analysis of baseline characteristics (Table S1 ) revealed significant differences in various demographic characteristics, health indicators, disease conditions, and lifestyle factors across the participants stratified by different quartiles of the AIP. As the AIP increased, body mass index, blood pressure, and the levels of uric acid, triglycerides, and C-reactive protein significantly increased, whereas the high-density lipoprotein cholesterol level significantly decreased. Furthermore, the incidences of dyslipidaemia, heart diseases, and stroke were significantly greater in the high AIP group (P < 0.001). Overall, the increase in AIP is closely associated with the deterioration of metabolic indicators and an increased incidence of stroke events, suggesting that the AIP may have potential value for stroke risk assessment. Associations of baseline AIP and hsCRP with incident stroke To further examine the relationship between AIP and the risk of stroke, we employed the RCS regression model, revealing a nonlinear relationship of AIP with stroke (P for non-linearity = 0.028, as depicted in Fig. 2 ). A multivariable Cox proportional hazards regression model was employed to assess the impact of the AIP and hsCRP levels on the risk of incident stroke events(Table S2). A total of 8,909 person-years were analyzed, documenting the occurrence of events alongside the corresponding AIP and CRP levels. AIP was categorized by quartiles and medians, while CRP was grouped according to predefined thresholds to evaluate their association with event risk.The results demonstrated that in the AIP quartile analysis, hazard ratios (HRs) increased progressively across higher quartiles (Q2 to Q4), with the highest quartile (Q4) showing an HR of 1.49 (95% CI: 1.18–1.88, p < 0.001). A significant trend was observed (P for trend < 0.001), indicating a positive association between AIP levels and event risk. Further analysis based on median categorization revealed that individuals with higher AIP levels had a significantly increased risk of events (HR 1.26, 95% CI: 1.08–1.47, p = 0.004).For CRP, groups with levels ≥ 1 showed a significantly higher risk (HR 1.43, 95% CI: 1.23–1.68, p < 0.001), and this association persisted in the group with CRP levels ≥ 2 (HR 1.22, 95% CI: 1.04–1.44, p = 0.017).These findings suggest that elevated levels of AIP and CRP are independent predictors of incident event risk. Associations of the AIP with new-onset stroke according to the hsCRP level The combined impact of the AIP, with a threshold defined by the median value (0.302), and hsCRP, with a cut-off of 1 mg/L, on the risk of stroke was assessed. The participants were divided into four groups: (1) AIP < median & hsCRP < 1 mg/L, (2) AIP < median & hsCRP ≥ 1 mg/L, (3) AIP ≥ median & hsCRP < 1 mg/L, and (4) AIP ≥ median & hsCRP ≥ 1 mg/L. Comparisons between groups were made using the χ² test for categorical variables and analysis of variance (ANOVA) for continuous variables. Owing to the lack of universally accepted clinical thresholds for the AIP, the median value from previous studies was applied, with AIP values above the median considered elevated. As illustrated in Fig. 4 , multivariable Cox proportional hazards regression analysis revealed that individuals with both elevated AIP and hsCRP levels had an adjusted hazard ratio (HR) of 1.69 (95% CI: 1.36–2.10) for stroke compared with those with lower levels of both markers. Similarly, individuals with low AIP and elevated hsCRP levels presented an adjusted HR of 1.40 (95% CI: 1.11–1.76) relative to the reference group (low AIP and low hsCRP). An association between elevated AIP and low hsCRP levels was observed, with a Model 2-adjusted HR of 1.29 (95% CI: 1.02–1.64). However, this association lost statistical significance after further adjustment in Model 3. The Kaplan–Meier curves of the cumulative incidence of stroke are shown in Supplementary file 1: Fig. 3 . Sensitivity analysis The sensitivity analyses corroborated our primary findings. Even when a cut-off of 2 mg/L was applied to define high hsCRP levels, following the grouping approach used in the Canakinumab Antiinflammatory Thrombosis Outcome Study (CANTOS)[ 24 ], the results remained largely unchanged (Supplementary file 1, Table S4). Compared with individuals with lower AIP and hsCRP levels, those with elevated levels of both AIP and hsCRP had a significantly greater risk of stroke, with a multivariable-adjusted hazard ratio (HR) of 1.40 (95% CI: 1.12–1.76). Moreover, the trend in stroke risk across these four groups consistently reinforced our primary conclusion. Subgroup analyses Stratified analyses were performed to investigate whether the associations between the AIP and hsCRP level and the incidence of stroke were influenced by predefined subgroups (sex, age, smoking status, and alcohol consumption). The study's findings revealed consistent associations across various subgroups. Specifically, the joint assessment of AIP and hsCRP levels demonstrated a stable relationship with the incidence of stroke events(Fig. 5 ). This consistency was observed when hsCRP levels were stratified at a threshold of 2 mg/L(Supplementary file 1: Fig. 6 )。 Mediation Analysis The mediating effects of the AIP and hsCRP on stroke risk were examined to explore the mechanisms underlying their interaction. The results revealed bidirectional mediation pathways between the AIP and hsCRP in relation to the risk of stroke (Fig. 7 ). Specifically, the AIP indirectly affects stroke risk through hsCRP, while hsCRP also indirectly influences stroke risk via the AIP. Specifically, the proportions of the indirect effect of the AIP on stroke risk mediated by hsCRP were 1.92% (p < 0.001) and 1.86% (p = 0.04), whereas the proportions of the effect of hsCRP mediated through the AIP accounted for 10.4% (p < 0.001) and 5.61% (p = 0.04) of the total effect. These findings suggest that the AIP plays a relatively stronger mediating role in the association between hsCRP and stroke risk than vice versa. Together, these results point to a complex interrelationship between the AIP and hsCRP in their impact on the risk of stroke, underscoring the potential for synergistic effects between the two in the pathophysiological mechanisms of stroke. Incremental predictive performance of the AIP and hsCRP in the incident Stroke In predictive model analysis(Supplementary file 1: Fig. 8 ), we compared the performance of a model using a single biomarker (AIP or CRP) versus a model combining AIP and CRP in predicting stroke risk. The results showed that the combined model of AIP and CRP significantly outperformed both the single biomarker model and traditional models at all follow-up time points, demonstrating the best performance under the receiver operating characteristic curve. Moreover,the NRI and IDI for stroke were significant(Supplementary file 1: Table S5).This suggests that incorporating both AIP and CRP into the prediction model substantially improves stroke risk prediction accuracy, particularly in non-diabetic individuals. This combined model may become a more accurate tool for stroke risk stratification and cardiovascular health management, especially for early screening and risk monitoring. Associations of baseline CumAIP and CumhsCRP with incident stroke In our analysis of a subset from CHARLS spanning 2011 to 2020, we investigated the potential association between cumulative exposure to the AIP and hsCRP with the incidence of stroke, as detailed in Table S6. The study encompassed 5,954 participants, out of whom 497 experienced a stroke during the 5-year follow-up period. The study divided cumulative AIP levels into tertiles (Q1 to Q3), with Q1 as the reference group. In the Q2 group, the stroke risk was 44% higher than in Q1 (HR: 1.44, 95% CI: 1.14–1.81, P = 0.002), and this increase was also significant in Models 2 and 3. In the Q3 group, the stroke risk was 75% higher than in Q1 (HR: 1.75, 95% CI: 1.40–2.19, P < 0.001), with significant findings in all models. The trend test showed a significant increase in stroke risk with higher AIP levels (P for trend < 0.001).When AIP was categorized by the median, participants with AIP at or above the median had a 38% higher stroke risk (HR: 1.38, 95% CI: 1.15–1.65, P < 0.001), with significant findings in Models 2 and 3.For cumulative hsCRP, each unit increase was associated with a 2% higher stroke risk (HR: 1.02, 95% CI: 1.01–1.03, P = 0.001). When stratified by median, those with hsCRP at or above the median had a 38% higher stroke risk (HR: 1.38, 95% CI: 1.16–1.66, P < 0.001), with persistent significance in subsequent models. Table S7 primarily discusses the association between CumAIP combined with CumhsCRP levels and stroke incidence. When both CumAIP and CumhsCRP levels are at or above the median, participants have a significantly higher risk of stroke. This association remains significant across all three models, indicating the robustness of the results. Discussion This study, which utilized data from the CHARLS, investigated the relationship between the AIP and the risk of stroke in nondiabetic individuals, with a particular focus on varying levels of hsCRP. Elevated AIP levels were significantly associated with an increased stroke risk, emphasizing the synergistic role of lipid metabolism and inflammation in the pathogenesis of stroke. Notably, individuals with high levels of both the AIP and hsCRP had the greatest risk of stroke, highlighting the collaborative impact of lipid dysregulation and inflammation in promoting atherosclerosis and ultimately increasing the risk of stroke. The focus on nondiabetic individuals is particularly important, as diabetes itself is a well-established independent risk factor for stroke, which could confound the relationships among the AIP, inflammation, and the risk of stroke. By excluding diabetic patients, this study provides a clearer understanding of how the AIP and hsCRP independently contribute to the risk of stroke in this specific population. Moreover, the findings suggest a potential bidirectional relationship between the AIP and hsCRP with regard to the risk of stroke among nondiabetic individuals. The AIP may influence the risk of stroke indirectly by driving inflammation via lipid metabolism, whereas hsCRP could exacerbate the risk of stroke by promoting chronic inflammation that accelerates atherosclerosis driven by the AIP. These results underscore the clinical importance of combining assessments of the AIP and hsCRP to refine the stratification of the risk of stroke in nondiabetic populations. Identifying high-risk individuals allows the implementation of more precise interventions and personalized management strategies, particularly for those with concurrent lipid dysregulation and systemic inflammation. Furthermore, incorporating lipid metabolism and inflammatory markers into clinical risk prediction models could increase the accuracy of the assessment of the risk of stroke and optimize targeted prevention strategies. Future research should further explore the combined predictive and therapeutic potential of the AIP and hsCRP in diverse nondiabetic populations. An elevated AIP has been reported to correlate with an increased risk of ischaemic stroke [ 25 ], serving as a composite biomarker that reflects the balance between TG and HDL-C levels. Higher AIP values are typically associated with elevated TG and reduced HDL-C levels, both of which are recognized as significant cardiovascular risk factors. Impaired TG metabolism, considered a residual risk factor beyond LDL-C levels, contributes to stroke development and adverse clinical outcomes. Conversely, HDL-C possesses antioxidant and anti-inflammatory properties, mitigating inflammatory responses by inhibiting macrophage activation and reducing cytokine secretion [ 26 , 27 ]. Additionally, HDL-C plays a key role in reverse cholesterol transport, providing protective effects against atherosclerosis and reducing the risk of cardiovascular diseases and related conditions. In line with these mechanisms, our study revealed a clear dose‒response relationship between the AIP and the risk of stroke, with individuals in the highest AIP quartile exhibiting a significantly elevated HR for stroke. This strong correlation persisted even after excluding diabetic individuals, highlighting the AIP as an independent predictor of the risk of stroke in nondiabetic populations. These findings underscore the clinical relevance of the AIP as a valuable marker of the risk of atherosclerosis. Moreover, chronic inflammation is another important cardiovascular risk factor. Numerous reports have suggested that elevated inflammation levels synergistically increase the risk of arterial stiffness[ 20 , 28 , 29 ]. Among various inflammatory markers, hsCRP has garnered the most attention because of its significant role in risk assessment and reclassification for cardiovascular and cerebrovascular diseases[ 30 , 31 ]. Increased inflammation, as indicated by elevated hsCRP levels, may be associated with the progression of arterial stiffness induced by lipid abnormalities[ 32 ]. The role of inflammation in the propagation of atherosclerosis and susceptibility to cardiovascular events has been well established. We observed that hsCRP was also positively associated with the risk of stroke, and the combination of the AIP and hsCRP significantly improved the predictive accuracy. This combined effect highlights the critical interplay between lipid metabolism and inflammation in the pathogenesis of stroke. Individuals with elevated levels of both the AIP and hsCRP had the highest stroke risk, reinforcing the hypothesis that lipid dysregulation and inflammation synergistically promote atherosclerosis, thereby increasing the likelihood of stroke [ 28 , 33 , 34 ]. Sensitivity analyses further confirmed the robustness of these results, even when a higher hsCRP threshold of 2 mg/L was applied. Several potential mechanisms may underlie the complex relationships among the AIP, inflammation levels, and arterial stiffness or vascular lesions. Dyslipidaemia, metabolic syndrome, vascular stiffness, and IR are closely related. IR is characterized by endothelial dysfunction and a proinflammatory state, which play crucial roles in the pathophysiology of atherosclerosis, whereas dyslipidaemia, a traditional risk marker for IR and atherosclerosis, has long been involved in chronic inflammation-related pathological processes. In this context, the AIP, as a comprehensive marker, is not only closely associated with IR but also reflects the role of dyslipidaemia in promoting atherosclerosis. Elevated inflammation levels, particularly increased hsCRP, may exacerbate this process. Inflammation may promote the development of IR through mechanisms involving the RAAS, sympathetic nervous system activation, and gut insulin-modulating factors (such as DPP-4), which further promote IR progression[ 35 , 36 ]. The association between hsCRP and lipid particles, along with its ability to activate inflammatory complement[ 37 – 39 ], suggests that hsCRP may be involved in the pathogenesis of stroke events. Building on these studies, it is clear that alongside lipid-lowering strategies, addressing cardiovascular risk by reducing inflammation is critically important. Currently, numerous anti-inflammatory therapies, including low-dose colchicine and interleukin-6 inhibitors, are undergoing clinical investigation[ 40 , 41 ]. PCSK9 inhibitors, which are widely employed in clinical practice, not only exert lipid-lowering effects but also demonstrate potential anti-inflammatory properties and protective effects against arterial and venous thrombosis[ 42 ]. Sodium‒glucose cotransporter 2 inhibitors and glucagon-like peptide-1 receptor agonists have been demonstrated to modulate inflammation and reduce the incidence of vascular events[ 43 , 44 ]. Accordingly, the assessment of the AIP and hsCRP levels may provide clinicians with critical insights to guide the selection of appropriate therapeutic strategies for the long-term prevention of atherosclerosis. Limitations This study has the limitations inherent in an observational design, which means that a causal relationship could not be determined. Although we found associations of the AIP and hsCRP with the risk of stroke, the nature of the study design did not allow us to rule out the potential impact of confounding factors on the results. Moreover, some information in the CHARLS data is based on self-reports, such as smoking and drinking habits, which may introduce recall bias or inaccurate reporting, thereby affecting the assessment of stroke risk factors. Finally, although this study proposed a stroke risk prediction model based on the AIP and CRP, this model has not yet been externally validated in other independent cohorts, so its applicability and clinical utility in different populations still need further verification. Conclusions This study demonstrated that elevated AIP and hsCRP levels are significantly associated with the risk of stroke in individuals without diabetes and that the combination of the AIP with hsCRP enhances stroke risk prediction capabilities. The results validate the potential use of the AIP and hsCRP as effective tools for cardiovascular risk assessment and highlight their clinical value in enhancing risk stratification in nondiabetic populations. By facilitating the identification of high-risk individuals among those without diabetes, particularly those with concurrent lipid dysregulation and systemic inflammation, the AIP provides a foundation for developing more precise and personalized treatment strategies. Abbreviations CHARLS China Health and Retirement Longitudinal Study；AIP Atherogenic Index of Plasma；hsCRP High-sensitivity C-reactive protein；BMI Body mass index；BUN Blood urea nitrogen；Cr creatinine；CI Confidence interval；FBG Fasting blood glucose；HbA1c Glycosylated hemoglobin A1c；HDLC High-density lipoprotein cholesterol；HR Hazard ratio；hsCRP High-sensitivity C-reactive protein；LDLC Low-density lipoprotein cholesterol；RAAS Renin–angiotensin–aldosterone system；RCS Restricted cubic spline；TC Total cholesterol；TG Triglyceride；UA Uric acid ROC Receiver operating characteristic；AUC Area under the curve. Declarations Conflict of interest: The authors declare that they have no conflicts of interest. Ethics approval and consent to participate: The CHARLS study was approved by the Institutional Review Board of Peking University (IRB00001052-11015) and conducted according to the Declaration of Helsinki. Written informed consent was obtained from all participants. Consent for publication: All the authors listed have approved the manuscript that is enclosed. Availability of data and materials: The data that support the findings of this study are available for request. Competing interests: The authors declare that they have no competing interests. Funding: This study was supported by grants from National Natural Science Foundation of China (82001568), Guangdong Basic and Applied Basic Research Foundation (2019A1515110102), China Postdoctoral Science Foundation (2020M682675), PhD Start-up Fund of the Third Affiliated Hospital of Guangzhou Medical University (2019B08) and Science and Technology Projects in Guangzhou (202201020167). The funders played no role in the study design, data collection or analysis, decision to publish, or preparation of the manuscript. Authors' contributions: Xiaotong Yao: conception and design of the study, acquisition, analysis, and interpretation of data, drafting a significant portion of the manuscript or figures.Liting Liang, MM: acquisition, analysis, and interpretation of data. Jia Chen, MD PhD : acquisition, analysis, and interpretation of data. Liting Liang, MM, Zhuoji Liang, Ying Piao , MM, and Manting Yang, MM: analysis and interpretation of data.Yanling Liang, MD PhD: conception and design of the study, acquisition, analysis. Xiaobo Fang,, MD PhD: conception and design of the study, interpretation of data, drafting a significant portion of the manuscript. Acknowledgements: This study used data from China Health and Retirement Longitudinal Study (CHARLS). We would like to thank the CHARLS research team for the time and effort into the CHARLS project. 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European heart journal. Cardiovascular pharmacotherapy 2019, 5 (4):237-245. Neves JS, Borges-Canha M, Vasques-Nóvoa F, Green JB, Leiter LA, Granger CB, Carvalho D, Leite-Moreira A, Hernandez AF, Del Prato S et al : GLP-1 Receptor Agonist Therapy With and Without SGLT2 Inhibitors in Patients With Type 2 Diabetes. J AM COLL CARDIOL 2023, 82 (6):517-525. Patorno E, Htoo PT, Glynn RJ, Schneeweiss S, Wexler DJ, Pawar A, Bessette LG, Chin K, Everett BM, Kim SC: Sodium-Glucose Cotransporter-2 Inhibitors Versus Glucagon-like Peptide-1 Receptor Agonists and the Risk for Cardiovascular Outcomes in Routine Care Patients With Diabetes Across Categories of Cardiovascular Disease. ANN INTERN MED 2021, 174 (11):1528-1541. Tables Table 1: Baseline characteristics of the study population across AIP quartiles. Characteristics Overall Quartile p Q1 Q2 Q3 Q4 Participants,No. 8909 2228 2227 2227 2227 Age,years 58.56(9.60) 59.26(9.86) 58.71 (9.92) 58.42(9.35) 57.85(9.20) <0.001 Gender female 4784(53.7) 1097(49.2) 1213 (54.5) 1226 (55.1) 1248 (56.0) <0.001 male 4125(46.3) 1131(50.8) 1014 (45.5) 1001(44.9) 979(44.0) Education level high 139 ( 1.6) 29 ( 1.3) 30 ( 1.3) 35 ( 1.6) 45 ( 2.0) <0.001 middle 2643 (29.7) 591 (26.5) 643 (28.9) 685 (30.8) 724 (32.5) primary 6127(68.8) 1608(72.2) 1554 (69.8) 1507 (67.7) 1458 (65.5) Residence Rural 7365(82.7) 1927(86.5) 1873 (84.1) 1829 (82.1) 1736 (78.0) <0.001 Urban 1544(17.3) 301(13.5) 354 (15.9) 398 (17.9) 491 (22.0) Marital status married 7914(88.8) 1963(88.1) 1974 (88.6) 1980 (88.9) 1997 (89.7) 0.413 others 995 (11.2) 265(11.9) 253 (11.4) 247 (11.1) 230 (10.3) Smoking no 5620(63.1) 1353(60.7) 1403 (63.0) 1438 (64.6) 1426 (64.0) 0.040 yes 3289(36.9) 875(39.3) 824 (37.0) 789 (35.4) 801 (36.0) Drinking no 5937 (66.6) 1316 (59.1) 1524 (68.4) 1555 (69.8) 1542 (69.2) <0.001 yes 2972 (33.4) 912 (40.9) 703 (31.6) 672 (30.2) 685 (30.8) BMI Continuous 21.80 (4.97) 20.71 (4.17) 21.45 (4.79) 22.10 (5.10) 22.93 (5.46) <0.001 <23.9 4652 (52.2) 1521 (68.3) 1282 (57.6) 1047 (47.0) 802 (36.0) <0.001 24-27.9 2131 (23.9) 342 (15.4) 494 (22.2) 598 (26.9) 697 (31.3) ≥28 2126 (23.9) 365 (16.4) 451 (20.3) 582 (26.1) 728 (32.7) SBP 123.64 (24.77) 121.94 (23.55) 122.70 (24.05) 124.60 (25.59) 125.31 (25.67) <0.001 DBP 73.90 (16.68) 72.59 (16.19) 73.21 (16.36) 74.38 (16.91) 75.42 (17.14) <0.001 BUN 15.69 (4.55) 16.50 (4.76) 15.89 (4.68) 15.28 (4.36) 15.11 (4.27) <0.001 UA 4.42 (1.23) 4.23 (1.14) 4.29 (1.19) 4.41 (1.24) 4.75 (1.29) <0.001 TG 121.82(75.49) 61.50 (14.42) 88.55 (17.76) 121.74 (24.94) 215.51 (90.18) <0.001 TC 191.79(37.37) 187.41(34.01) 189.15 (36.82) 192.54 (36.87) 198.05 (40.61) <0.001 HDLC 52.02 (14.97) 67.49 (13.81) 54.80 (10.15) 47.55 (8.69) 38.21 (8.34) <0.001 LDLC 116.58(33.78) 110.23(29.06) 119.04 (32.97) 122.39 (33.46) 114.68 (37.85) <0.001 HbA1c 5.10 (0.40) 5.07 (0.39) 5.09 (0.39) 5.10 (0.41) 5.13 (0.42) <0.001 hsCRP 2.06 (3.35) 1.89 (3.45) 1.99 (3.42) 2.11 (3.34) 2.25 (3.16) 0.002 FBG 99.92(12.05) 97.77(12.69) 98.54 (11.63) 99.95 (11.28) 103.44 (11.78) <0.001 Cr 0.78 (0.23) 0.77 (0.29) 0.77 (0.23) 0.78 (0.18) 0.79 (0.19) <0.001 Comorbidities Dyslipidemia 654 ( 7.3) 101 ( 4.5) 129 ( 5.8) 163 ( 7.3) 261 (11.7) <0.001 Hypertension 1954 (21.9) 353 (15.8) 410 (18.4) 552 (24.8) 639 (28.7) <0.001 Heart disease 1321 (14.8) 310 (13.9) 302 (13.6) 372 (16.7) 337 (15.1) 0.014 Stroke 696 ( 7.8) 123 ( 5.5) 171 ( 7.7) 197 ( 8.8) 205 ( 9.2) <0.001 History of medication use Dyslipidemia medications 348 ( 3.9) 43 ( 1.9) 70 ( 3.1) 85 ( 3.8) 150 ( 6.7) <0.001 Hypertension medications 1411 (15.8) 235 (10.5) 286 (12.8) 401 (18.0) 489 (22.0) <0.001 P value was based on χ2 or analysis of variance or Kruskal-Wallis rank sum test where appropriate. AIP: Q1 0.522 . BMI body mass index, BUN blood urea nitrogen, DBP diastolic blood pressure, DM diabetes mellitus, FBG fasting blood glucose, HbA1c glycosylated hemoglobin A1c, HDLC high density lipoprotein cholesterol, hsCRP high-sensitivity C-reactive protein, LDLC low density lipoprotein cholesterol, SBP systolic blood pressure, TC total cholesterol, TG triglycerides, UA uric acid,AIP atherogenic index of plasma. Table 2 : Cox Proportional Hazards Model Analysis of AIP and hsCRP Levels with Stroke Event Ris k Variable No. of events (Incident ratea) Model1 Model2 Model3 HR (95% CI) P value HR (95% CI) P value HR (95% CI) P value AIP Quartiles Q1 123 Ref. Ref. Ref. Q2 171 1.44(1.14-1.82) 0.002 1.43(1.13-1.80) 0.003 1.39(1.10-1.76) 0.005 Q3 197 1.69(1.35-2.12) <0.001 1.67(1.33-2.10) <0.001 1.48(1.18-1.86) <0.001 Q4 205 1.79(1.43-2.25) <0.001 1.75(1.40-2.20) <0.001 1.49(1.18-1.88) <0.001 P for trend <0.001 <0.001 0.002 sensitivity AIP < median 294 Ref. Ref. Ref. AIP≥median 402 1.43(1.23-1.66) <0.001 1.41(1.34-1.82) <0.001 1.26(1.08-1.47) 0.004 hsCRP hsCRP <1 275 Ref. Ref. Ref. hsCRP≥1 421 1.58(1.36-1.84) <0.001 1.56(1.11-1.57) 0.002 1.43(1.23-1.68) <0.001 sensitivity hsCRP < 2 480 Ref. Ref. Ref. hsCRP≥2 216 1.30(1.10-1.52) 0.002 1.28(1.08-1.50) 0.003 1.18(1.01-1.39) 0.042 Abbreviations: CI, confidence interval HR, hazard ratio. Mode1: Adjusted for age, gender. Mode2: Adjusted for age, gender, bmi level, smoking status, and drinking status. Mode3: Adjusted for age, gender, bmi level, marital status,education level, smoking status, drinking status, hypertension, heart disease, dyslipidaemia, and history of medication use for dyslipidaemia、hypertension. Table 3 : Risk of stroke upon coexposure stratified by the AIP and hsCRP Model1 p Model 2 p Model 3 p AIP < median & CRP < 1 Ref. Ref Ref AIP < median & crp ≥ 1 1.48 (1.17-1.86) <0.001 1.45 (1.15-1.83) 0.001 1.40(1.11-1.76) 0.004 AIP ≥ median & CRP < 1 1.32 (1.04-1.67) 0.023 1.29 (1.02-1.64) 0.034 1.21 (0.96-1.54) 0.111 AIP ≥ median & CRP ≥ 1 2.04 (1.66-2.51) <0.001 1.95 (1.59-2.41) <0.001 1.69 (1.36-2.10) <0.001 P for trend <0.001 <0.001 <0.001 Table 4 : Sensitivity analysis of AIP and hsCRP with new-onset stroke. Model1 p Model 2 p Model 3 p AIP < median & CRP < 2 Ref. Ref Ref AIP < median & CRP ≥ 2 1.30 (1.00-1.68) 0.046 1.28 (0.99-1.65) 0.065 1.23(0.95-1.59) 0.122 AIP ≥ median & CRP < 2 1.43 (1.19-1.71) <0.001 1.39 (1.16-1.67) <0.001 1.28(1.07-1.54) 0.008 AIP ≥ median & CRP ≥ 2 1.74 (1.41-2.16) <0.001 1.66 (1.34-2.07) <0.001 1.40(1.12-1.76) 0.003 P for trend <0.001 <0.001 0.001 Table 5 : Incremental predictive value of hsCRP and AIP beyond traditional risk factors. Models AUC(95% CI) P AUC NRI (95% CI) P NRI IDI (95% CI) P IDI Traditional model a 0.673(0.652-0.693) Ref. Ref. Traditional model +hs CRP + AIP 0.680(0.659-0.700) 0.028 0.106(0.028-0.183) 0.007 0.002(0.001-0.003) 0.003 Abbreviations: AUC, area under curve; BMI, body mass index; CI, confidence interval; NRI,net reclassification index;IDI,integrated discrimination. a Traditional model based on gender, sex, marital status, residence, education level, smoking status, drinking status,hypertension, heart disease, dyslipidaemia, and history of medication use for hypertension, dyslipidaemia. Table 6 : Cox Proportional Hazards Model Analysis of Cum AIP and Cum hsCRP Levels with Stroke Event Ris k Variable No. of events (Incident ratea) Model1 Model2 Model3 HR (95% CI) P value HR (95% CI) P value HR (95% CI) P value CumAIP Tertiles Q1 124 Ref. Ref. Ref. Q2 171 1.44(1.14-1.81) 0.002 1.37(1.09-1.73) 0.008 1.35(1.07-1.71) 0.012 Q3 202 1.75(1.40-2.19) <0.001 1.57(1.25-1.98) <0.001 1.49(1.18-1.89) <0.001 P for trend <0.001 <0.001 0.002 sensitivity CumAIP < median 217 Ref. Ref. Ref. CumAIP≥median 280 1.38(1.15-1.65) <0.001 1.27(1.06-1.52) 0.011 1.21(1.01-1.46) 0.044 CumhsCRP Continuous 497 1.02(1.01-1.03) 0.001 1.01(1.01-1.02) 0.002 1.01(1.00-1.02) 0.08 CumhsCRP <median 204 Ref. Ref. Ref. CumhsCRP≥median 293 1.38(1.16-1.66) <0.001 1.29(1.08-1.55) 0.006 1.20(1.01-1.45) 0.049 Abbreviations: CI, confidence interval HR, hazard ratio. Mode1: Adjusted for age, gender. Mode2: Adjusted for age, gender, bmi level, smoking status, and drinking status. Mode3: Adjusted for age, gender, bmi level, marital status,education level, smoking status, drinking status, hypertension, heart disease, dyslipidaemia, and history of medication use for dyslipidaemia、hypertension. Table 7 : Risk of stroke upon coexposure stratified by the Cum AIP and Cum hsCRP Model1 p Model 2 p Model 3 p Low Risk Ref. Ref Ref Moderate Risk 1.30(1.03-1.65) 0.026 1.27(01.01-1.61) 0.043 1.18(0.931-1.49) 0.172 High Risk 1.76 (1.38-2.23) <0.001 1.63(1.27-2.08) <0.001 1.41(1.10-1.82) 0.007 P for trend <0.001 <0.001 0.032 Low Risk:CumAIP < median & CumhsCRP < median; Moderate Risk:CumAIP ≥ median & CumhsCRP < median , CumAIP < median & CumhsCRP ≥ median;,High Risk:CumAIP ≥ median & CumhsCRP ≥ median Model1: Adjusted for age, gender. Model2: Adjusted for age, gender, bmi level, smoking status, and drinking status. Model3:Adjusted for age, gender, marital status,residence, education level, smoking status, drinking status, hypertension, diabetes, heart disease, dyslipidaemia, and history of medication use for hypertension, and dyslipidaemia. Additional Declarations No competing interests reported. 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Fang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4UlEQVRIiWNgGAWjYDACZjA6wMDPwMAGETlArBbJBqK1MEC1GBwgVovBceaHjwsq7sgZn1/+7NHNNgY5vhsJjJ8L8GiRbGYzNp5x5pmx2Y0H6ca5bQzGkjcSmKVn4NHCz8xgJs3bdjhx240Dx6SBWhI33EhgY+bBo4WNmf0bWMvmGQfbQFrqCWrhZ+aB2LKBv5kNpCXBgJAWyWaeYrBfJG6wsUnnnJMwnHnmYbM0Pi0G549vBIcYf//xZ9I5ZTbyfMeTD37GpwUBJBLAJBAzNhClAeirA0QqHAWjYBSMghEHAAX4Sp98IHcvAAAAAElFTkSuQmCC","orcid":"","institution":"Third Affiliated Hospital of Guangzhou Medical University","correspondingAuthor":true,"prefix":"","firstName":"Xiaobo","middleName":"","lastName":"Fang","suffix":""}],"badges":[],"createdAt":"2025-01-15 06:53:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5831890/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5831890/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":74243282,"identity":"9861d509-b50f-4ec4-8430-b8131c077f10","added_by":"auto","created_at":"2025-01-20 09:40:39","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":87673,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of the study population.\u003c/p\u003e","description":"","filename":"Fig.1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5831890/v1/ddd1387bf417450584acff8a.jpg"},{"id":74241399,"identity":"3d598051-980d-49fb-8931-f13e796a89f1","added_by":"auto","created_at":"2025-01-20 09:32:39","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":147458,"visible":true,"origin":"","legend":"\u003cp\u003eNonlinear association between AIP and Stroke.\u003c/p\u003e","description":"","filename":"Fig.2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5831890/v1/47f234347a8149e0b970e814.jpg"},{"id":74241397,"identity":"895fffb2-6a4e-4ca4-aecf-add26e219452","added_by":"auto","created_at":"2025-01-20 09:32:39","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":125988,"visible":true,"origin":"","legend":"\u003cp\u003eK-M plot of \u0026nbsp;stroke by AIP and hsCRP level\u003c/p\u003e","description":"","filename":"Fig.3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5831890/v1/1c6a842c6a3d85819e9654fb.jpg"},{"id":74243284,"identity":"e7ed5070-8968-4269-b3e2-9eefd4db60d5","added_by":"auto","created_at":"2025-01-20 09:40:39","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":258566,"visible":true,"origin":"","legend":"\u003cp\u003eStroke risks in different groups according to the AIP and hsCRP levels.\u003c/p\u003e","description":"","filename":"Fig.4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5831890/v1/6b52d7446e2f26056345c657.jpg"},{"id":74241416,"identity":"b8f952d3-182d-43f0-b853-68e3f1c1fddb","added_by":"auto","created_at":"2025-01-20 09:32:40","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":145524,"visible":true,"origin":"","legend":"\u003cp\u003eSubgroup and interaction analyses among the quartile 1−4 and stroke across various subgroups(hsCRP was divided into two groups at 1 mg/L).\u003c/p\u003e","description":"","filename":"Fig.5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5831890/v1/f5b6480e579a067dfd0a96b4.jpg"},{"id":74241403,"identity":"1eeb8b11-3b19-4e6a-8f54-62939bec6ec9","added_by":"auto","created_at":"2025-01-20 09:32:39","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":124314,"visible":true,"origin":"","legend":"\u003cp\u003eSubgroup and interaction analyses among the quartile 1−4 and stroke across various subgroups(hsCRP was divided into two groups at 2 mg/L).\u003c/p\u003e","description":"","filename":"Fig.6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5831890/v1/f49063dbb96761c13750b973.jpg"},{"id":74243285,"identity":"769fbae6-d300-4d4d-bd38-03a5bfcc5280","added_by":"auto","created_at":"2025-01-20 09:40:39","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":72840,"visible":true,"origin":"","legend":"\u003cp\u003eMutual mediation effects ofAIP index and hsCRP on stroke\u003c/p\u003e","description":"","filename":"Fig.7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5831890/v1/969cfd92c4ff06095d401392.jpg"},{"id":74241411,"identity":"c8a81c4f-b4a5-4d94-8f27-8b44249f45dc","added_by":"auto","created_at":"2025-01-20 09:32:39","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":66583,"visible":true,"origin":"","legend":"\u003cp\u003ePredictive capacity of AIP and hsCRP on the cardiovascular risk\u003c/p\u003e","description":"","filename":"Fig.8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5831890/v1/01ed15cd81087a5c9c5c7791.jpg"},{"id":74246849,"identity":"f5289ee5-d1d1-4df6-97ed-466ca1eac69a","added_by":"auto","created_at":"2025-01-20 09:56:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4588586,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5831890/v1/a2fdd5e4-9008-4a53-91f9-2d590cdcb1c3.pdf"},{"id":74241407,"identity":"ca7b3cd7-5432-4659-b4ff-d32d1d04b0f0","added_by":"auto","created_at":"2025-01-20 09:32:39","extension":"docx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":1555958,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfile1.docx","url":"https://assets-eu.researchsquare.com/files/rs-5831890/v1/688d3180c7406950b3681d80.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association of the Atherogenic Index of Plasma and High-Sensitivity C-Reactive Protein with Incident Stroke Among Individuals Without Diabetes: A National Cohort Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eStroke represents a leading cause of mortality and disability worldwide, posing substantial challenges to ageing populations and placing considerable economic and resource pressures on healthcare systems[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Over the past three decades, China has experienced a significant epidemiological transition from infectious diseases to noncommunicable diseases (NCDs)[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. As in many other ageing societies, the high prevalence and multimorbidity of NCDs, including stroke, present unique challenges to the healthcare system[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. According to China Stroke Statistics[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], chronic diseases cause the majority of deaths in China, with stroke being a prominent contributor. Projections suggest that the lifetime risk of stroke among Chinese residents will reach 39.3% over the next 25 years, significantly surpassing the global average of approximately 25%[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. While traditional risk factors\u0026mdash;such as age, hypertension, diabetes, hyperlipidaemia, and smoking\u0026mdash;are well recognized and commonly integrated into predictive models, a notable proportion of stroke patients do not exhibit these conventional risk factors[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. This highlights the urgent need to identify novel biomarkers to enable more precise risk stratification and to advance effective prevention strategies.\u003c/p\u003e \u003cp\u003eInsulin resistance (IR) is a pathological state characterized by reduced sensitivity of target organs or tissues to insulin, leading to impaired glucose utilization, and is widely recognized as a significant driver of cardiovascular diseases and mortality[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The exact biological mechanisms linking insulin resistance (IR) and stroke remain unclear, but several have been hypothesized, including metabolic dysregulation, oxidative stress, endothelial dysfunction, enhanced inflammatory responses, and abnormal activation of the renin‒angiotensin‒aldosterone system (RAAS)[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. In recent years, the AIP, defined as a logarithmic transformation of the ratio of the molar triglyceride (TG) concentration and high-density lipoprotein cholesterol (HDL-c) level, has gained recognition as a simple and reliable surrogate marker of IR[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. In addition to serving as an indicator of IR, the AIP is closely associated with atherogenic dyslipidaemia[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. This marker was first introduced by Dobi\u0026aacute;sov\u0026aacute; et al[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. An increasing number of studies have demonstrated that elevated AIP levels are significantly associated with increased risks of stroke, coronary artery disease, and all-cause mortality, reinforcing its role as an emerging predictor of cardiovascular and cerebrovascular diseases[\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Additionally, C-reactive protein (CRP), particularly high-sensitivity CRP (hsCRP), is a well-established biomarker of systemic inflammation and an independent predictor of cardiovascular risk[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. While existing evidence highlights the independent roles of the AIP and hsCRP in cardiovascular diseases, their combined or synergistic effects on the risk of stroke remain underexplored and warrant further investigation.\u003c/p\u003e \u003cp\u003eCurrent research indicates that IR is associated with stroke recurrence and poor functional outcomes in nondiabetic ischaemic stroke patients[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Inflammation and atherogenic dyslipidaemia are intricately interconnected, potentially working synergistically to exacerbate vascular damage and increase the risk of stroke[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Systemic inflammation may amplify dyslipidaemia, leading to endothelial dysfunction and atherosclerosis, whereas dyslipidaemia may further intensify inflammatory responses. However, the relationship between the AIP and hsCRP in stroke and their combined predictive value have not been adequately studied. Most existing research has focused on diabetic populations, which might lead to the overestimation of confounding of the role of IR, given the strong direct influence of diabetes on the risk of stroke. Investigating nondiabetic individuals allows the isolation of the independent contributions of the AIP and CRP, avoiding the confounding effects of diabetes and providing clearer insights into their roles. Furthermore, since nondiabetic individuals constitute the majority of the population, studying this group enhances the generalizability of findings and identifies broader, population-wide risk factors. To address this gap, we utilized data from the China Health and Retirement Longitudinal Study (CHARLS), which has a nationally representative prospective cohort, to investigate the associations between the AIP, CRP, and incident stroke in nondiabetic individuals.\u003c/p\u003e \u003cp\u003eThis study was performed with the aim of evaluating the combined impact of the AIP and hsCRP on the risk of stroke, assessing their joint capacity to enable risk reclassification, and exploring potential mediating mechanisms linking dyslipidaemia and inflammation, as reflected by hsCRP, to stroke. By integrating markers of dyslipidaemia and inflammation, to the intention was for this study to deepen the understanding of stroke risk factors and identify novel targets for early prevention efforts and interventions.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and population\u003c/h2\u003e \u003cp\u003eThis study is based on the China Health and Retirement Longitudinal Study (CHARLS), a nationally representative cohort study (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://charls.pku.edu.cn/\u003c/span\u003e\u003cspan address=\"http://charls.pku.edu.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) that has been tracking the health of Chinese adults since 2011.Health surveys are conducted every two years, with the fifth round expected to be completed by 2020. The study employs a multi-stage, stratified probability sampling strategy to recruit participants from both rural and urban areas across 150 counties or districts in 28 provinces of China.\u003c/p\u003e \u003cp\u003eIn each round of the survey, trained staff collected data on sociodemographic characteristics, medical history, and lifestyle behaviours through face‒to‒face interviews using standardized questionnaires. This study included participants from the baseline wave (2011\u0026ndash;2012) who underwent follow-up in 2013, 2015, 2018, and 2020. Individuals missing basic sociodemographic data (age and sex), blood sample test results, or baseline information on stroke, diabetes or cancer history were excluded. In total, 8,909 participants were included in the final analysis ( Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData Collection and Definitions\u003c/h3\u003e\n\u003cp\u003eIn the baseline survey of 2011, CHARLS successfully collected fasting blood samples from participants. Prior to sampling, participants were asked to fast overnight. Samples were stored at local hospitals and then transported to Peking University in Beijing, where they were preserved at -80\u0026deg;C for future analysis. Lipid levels, including triglycerides (TG), low-density lipoprotein cholesterol (LDL cholesterol), high-density lipoprotein cholesterol (HDL cholesterol), and total cholesterol (TC), were measured using enzymatic colorimetric methods, while high-sensitivity C-reactive protein (hsCRP) levels were determined using immunoturbidimetric method.\u003c/p\u003e\n\u003ch3\u003eExposure and Outcome Determination\u003c/h3\u003e\n\u003cp\u003eThe baseline exposure in this study was the AIP, which was calculated via the following formula: AIP\u0026thinsp;=\u0026thinsp;lg[total cholesterol (mmol/L)/high-density lipoprotein cholesterol (mmol/L)]. CumAIP = [(AIP2012\u0026thinsp;+\u0026thinsp;AIP2015) / 2] \u0026times; (2015\u0026thinsp;\u0026minus;\u0026thinsp;2012), CumCRP = [(hsCRP2012\u0026thinsp;+\u0026thinsp;hsCRP2015) / 2] \u0026times; (2015\u0026thinsp;\u0026minus;\u0026thinsp;2012).The primary outcome of interest was the occurrence of stroke. Consistent with standard procedures, stroke was determined on the basis of self-reported data, where participants confirmed whether they had received a formal diagnosis of stroke from a physician[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Trained personnel asked participants the following survey question: \u0026ldquo;Have you been told by a doctor that you have been diagnosed with a stroke?\u0026rdquo; Participants were followed from baseline (2011) until either the occurrence of stroke or the most recent survey (2020), with the event date determined by whichever occurred first. Participants who reported a stroke prior to 2011 were excluded from the analysis. The time of stroke occurrence was calculated under different conditions: for participants who did not report a stroke during the final follow-up, the event time was defined as the difference between the year of the last survey and the baseline survey year. For participants who reported a stroke, the event time was calculated as the difference between the year of the first reported stroke and the baseline year.\u003c/p\u003e\n\u003ch3\u003eCovariates\u003c/h3\u003e\n\u003cp\u003eThis study collected a variety of covariate information. Demographic factors include age, gender, education level, residence, marital status, as well as health indicators such as history of hypertension, chronic kidney disease, smoking, and alcohol consumption. Anthropometric measurements include height, weight,all measured according to standardized protocols. Body mass index (BMI) was calculated by dividing weight (in kilograms) by the square of height (in meters, kg/m\u0026sup2;). Based on the World Health Organization (WHO) guidelines for the Chinese population, participants were categorized as underweight/normal weight (BMI\u0026thinsp;\u0026lt;\u0026thinsp;24 kg/m\u0026sup2;), overweight (BMI\u0026thinsp;\u0026ge;\u0026thinsp;24 kg/m\u0026sup2;) or obese(BMI\u0026thinsp;\u0026ge;\u0026thinsp;28 kg/m\u0026sup2;). According to previous studies[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], education was categorized into three groups: primary school or below, middle school, and high school or above. Marital status was divided into two categories: married and others. Based on the response to the question \"Are you taking medication to control dyslipidemia?\" participants were classified into two groups: using lipid-lowering treatment and not using lipid-lowering treatment. A history of hypertension was determined by specific questionnaire responses, where participants were asked \"Have you ever been diagnosed with hypertension by a doctor?\" and \"Are you currently using medication to control hypertension?\" A positive response to either question was categorized as having a history of hypertension. Furthermore, individuals diagnosed with diabetes by a doctor, or those with fasting blood glucose levels\u0026thinsp;\u0026ge;\u0026thinsp;126 mg/dL or HbA1c levels\u0026thinsp;\u0026ge;\u0026thinsp;6.5%, were classified as having a history of diabetes.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses were conducted using RStudio version 4.3.2. A two-tailed P-value of less than 0.05 was deemed statistically significant. Continuous variables were summarized as either mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) or median (interquartile range), depending on the distribution of the data. Comparisons of baseline data for normally distributed and skewed data were performed using analysis of variance (ANOVA) and the Kruskal-Wallis H test, respectively. Categorical variables were presented as counts and percentages, with differences evaluated through chi-square tests. Trend tests were carried out based on the median values of the AIP quartiles.Multiple imputation to handle missing values was performed using the 'mice' package.\u003c/p\u003e \u003cp\u003eTo investigate the nonlinear relationship between the AIP and the risk of incident stroke, restricted cubic spline (RCS) regression was conducted. Cox proportional hazards regression models, implemented using the survival package, were employed to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for the associations among the AIP, CRP levels, and the risk of stroke. The AIP was analysed both as a continuous variable and categorized by quartiles or the median value (0.302), whereas CRP levels were classified according to predefined thresholds (\u0026lt;\u0026thinsp;1 mg/L and \u0026ge;\u0026thinsp;1 mg/L, \u0026lt;\u0026thinsp;2 mg/L and \u0026ge;\u0026thinsp;2 mg/L). The models were adjusted incrementally for potential confounders, including demographic factors, health indicators, and lifestyle behaviours.\u003c/p\u003e \u003cp\u003eSubgroup analyses were performed to assess whether the relationship between the AIP and the risk of stroke varied across predefined subgroups. Stratified Cox regression models were employed to evaluate interactions between the AIP and key covariates. Mediation analysis, conducted using the \u0026ldquo;mediation\u0026rdquo; package, was used to examine the bidirectional mediating effects of the AIP and CRP on the risk of stroke, quantifying their respective contributions to the total effect. Time-dependent AUC Curve were established to assess the predictive value of AIP and hsCRP on incidence of stroke, and the C-statistic, was used to quantify. To further estimate additional the predictive power beyond the basic models, the net reclassification improvement (NRI) and integrated discrimination improvement (IDI) index were computed .\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eBaseline characteristics of the study population\u003c/h2\u003e \u003cp\u003eData are presented as the means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviations (SDs) for continuous variables and as frequencies with percentages for categorical variables.The analysis of baseline characteristics (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e) revealed significant differences in various demographic characteristics, health indicators, disease conditions, and lifestyle factors across the participants stratified by different quartiles of the AIP. As the AIP increased, body mass index, blood pressure, and the levels of uric acid, triglycerides, and C-reactive protein significantly increased, whereas the high-density lipoprotein cholesterol level significantly decreased. Furthermore, the incidences of dyslipidaemia, heart diseases, and stroke were significantly greater in the high AIP group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Overall, the increase in AIP is closely associated with the deterioration of metabolic indicators and an increased incidence of stroke events, suggesting that the AIP may have potential value for stroke risk assessment.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAssociations of baseline AIP and hsCRP with incident stroke\u003c/h3\u003e\n\u003cp\u003eTo further examine the relationship between AIP and the risk of stroke, we employed the RCS regression model, revealing a nonlinear relationship of AIP with stroke (P for non-linearity\u0026thinsp;=\u0026thinsp;0.028, as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA multivariable Cox proportional hazards regression model was employed to assess the impact of the AIP and hsCRP levels on the risk of incident stroke events(Table S2). A total of 8,909 person-years were analyzed, documenting the occurrence of events alongside the corresponding AIP and CRP levels. AIP was categorized by quartiles and medians, while CRP was grouped according to predefined thresholds to evaluate their association with event risk.The results demonstrated that in the AIP quartile analysis, hazard ratios (HRs) increased progressively across higher quartiles (Q2 to Q4), with the highest quartile (Q4) showing an HR of 1.49 (95% CI: 1.18\u0026ndash;1.88, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). A significant trend was observed (P for trend\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating a positive association between AIP levels and event risk. Further analysis based on median categorization revealed that individuals with higher AIP levels had a significantly increased risk of events (HR 1.26, 95% CI: 1.08\u0026ndash;1.47, p\u0026thinsp;=\u0026thinsp;0.004).For CRP, groups with levels\u0026thinsp;\u0026ge;\u0026thinsp;1 showed a significantly higher risk (HR 1.43, 95% CI: 1.23\u0026ndash;1.68, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and this association persisted in the group with CRP levels\u0026thinsp;\u0026ge;\u0026thinsp;2 (HR 1.22, 95% CI: 1.04\u0026ndash;1.44, p\u0026thinsp;=\u0026thinsp;0.017).These findings suggest that elevated levels of AIP and CRP are independent predictors of incident event risk.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eAssociations of the AIP with new-onset stroke according to the hsCRP level\u003c/h2\u003e \u003cp\u003eThe combined impact of the AIP, with a threshold defined by the median value (0.302), and hsCRP, with a cut-off of 1 mg/L, on the risk of stroke was assessed. The participants were divided into four groups: (1) AIP\u0026thinsp;\u0026lt;\u0026thinsp;median \u0026amp; hsCRP\u0026thinsp;\u0026lt;\u0026thinsp;1 mg/L, (2) AIP\u0026thinsp;\u0026lt;\u0026thinsp;median \u0026amp; hsCRP\u0026thinsp;\u0026ge;\u0026thinsp;1 mg/L, (3) AIP\u0026thinsp;\u0026ge;\u0026thinsp;median \u0026amp; hsCRP\u0026thinsp;\u0026lt;\u0026thinsp;1 mg/L, and (4) AIP\u0026thinsp;\u0026ge;\u0026thinsp;median \u0026amp; hsCRP\u0026thinsp;\u0026ge;\u0026thinsp;1 mg/L. Comparisons between groups were made using the χ\u0026sup2; test for categorical variables and analysis of variance (ANOVA) for continuous variables. Owing to the lack of universally accepted clinical thresholds for the AIP, the median value from previous studies was applied, with AIP values above the median considered elevated. As illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, multivariable Cox proportional hazards regression analysis revealed that individuals with both elevated AIP and hsCRP levels had an adjusted hazard ratio (HR) of 1.69 (95% CI: 1.36\u0026ndash;2.10) for stroke compared with those with lower levels of both markers. Similarly, individuals with low AIP and elevated hsCRP levels presented an adjusted HR of 1.40 (95% CI: 1.11\u0026ndash;1.76) relative to the reference group (low AIP and low hsCRP). An association between elevated AIP and low hsCRP levels was observed, with a Model 2-adjusted HR of 1.29 (95% CI: 1.02\u0026ndash;1.64). However, this association lost statistical significance after further adjustment in Model 3. The Kaplan\u0026ndash;Meier curves of the cumulative incidence of stroke are shown in Supplementary file 1: Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eSensitivity analysis\u003c/h2\u003e \u003cp\u003eThe sensitivity analyses corroborated our primary findings. Even when a cut-off of 2 mg/L was applied to define high hsCRP levels, following the grouping approach used in the Canakinumab Antiinflammatory Thrombosis Outcome Study (CANTOS)[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], the results remained largely unchanged (Supplementary file 1, Table S4). Compared with individuals with lower AIP and hsCRP levels, those with elevated levels of both AIP and hsCRP had a significantly greater risk of stroke, with a multivariable-adjusted hazard ratio (HR) of 1.40 (95% CI: 1.12\u0026ndash;1.76). Moreover, the trend in stroke risk across these four groups consistently reinforced our primary conclusion.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eSubgroup analyses\u003c/h2\u003e \u003cp\u003eStratified analyses were performed to investigate whether the associations between the AIP and hsCRP level and the incidence of stroke were influenced by predefined subgroups (sex, age, smoking status, and alcohol consumption). The study's findings revealed consistent associations across various subgroups. Specifically, the joint assessment of AIP and hsCRP levels demonstrated a stable relationship with the incidence of stroke events(Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). This consistency was observed when hsCRP levels were stratified at a threshold of 2 mg/L(Supplementary file 1: Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e)。\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eMediation Analysis\u003c/h2\u003e \u003cp\u003eThe mediating effects of the AIP and hsCRP on stroke risk were examined to explore the mechanisms underlying their interaction. The results revealed bidirectional mediation pathways between the AIP and hsCRP in relation to the risk of stroke (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Specifically, the AIP indirectly affects stroke risk through hsCRP, while hsCRP also indirectly influences stroke risk via the AIP. Specifically, the proportions of the indirect effect of the AIP on stroke risk mediated by hsCRP were 1.92% (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and 1.86% (p\u0026thinsp;=\u0026thinsp;0.04), whereas the proportions of the effect of hsCRP mediated through the AIP accounted for 10.4% (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and 5.61% (p\u0026thinsp;=\u0026thinsp;0.04) of the total effect. These findings suggest that the AIP plays a relatively stronger mediating role in the association between hsCRP and stroke risk than vice versa. Together, these results point to a complex interrelationship between the AIP and hsCRP in their impact on the risk of stroke, underscoring the potential for synergistic effects between the two in the pathophysiological mechanisms of stroke.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eIncremental predictive performance of the AIP and hsCRP in the incident Stroke\u003c/h2\u003e \u003cp\u003eIn predictive model analysis(Supplementary file 1: Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e), we compared the performance of a model using a single biomarker (AIP or CRP) versus a model combining AIP and CRP in predicting stroke risk. The results showed that the combined model of AIP and CRP significantly outperformed both the single biomarker model and traditional models at all follow-up time points, demonstrating the best performance under the receiver operating characteristic curve. Moreover,the NRI and IDI for stroke were significant(Supplementary file 1: Table S5).This suggests that incorporating both AIP and CRP into the prediction model substantially improves stroke risk prediction accuracy, particularly in non-diabetic individuals. This combined model may become a more accurate tool for stroke risk stratification and cardiovascular health management, especially for early screening and risk monitoring.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eAssociations of baseline CumAIP and CumhsCRP with incident stroke\u003c/h2\u003e \u003cp\u003eIn our analysis of a subset from CHARLS spanning 2011 to 2020, we investigated the potential association between cumulative exposure to the AIP and hsCRP with the incidence of stroke, as detailed in Table S6. The study encompassed 5,954 participants, out of whom 497 experienced a stroke during the 5-year follow-up period.\u003c/p\u003e \u003cp\u003eThe study divided cumulative AIP levels into tertiles (Q1 to Q3), with Q1 as the reference group. In the Q2 group, the stroke risk was 44% higher than in Q1 (HR: 1.44, 95% CI: 1.14\u0026ndash;1.81, P\u0026thinsp;=\u0026thinsp;0.002), and this increase was also significant in Models 2 and 3. In the Q3 group, the stroke risk was 75% higher than in Q1 (HR: 1.75, 95% CI: 1.40\u0026ndash;2.19, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with significant findings in all models. The trend test showed a significant increase in stroke risk with higher AIP levels (P for trend\u0026thinsp;\u0026lt;\u0026thinsp;0.001).When AIP was categorized by the median, participants with AIP at or above the median had a 38% higher stroke risk (HR: 1.38, 95% CI: 1.15\u0026ndash;1.65, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with significant findings in Models 2 and 3.For cumulative hsCRP, each unit increase was associated with a 2% higher stroke risk (HR: 1.02, 95% CI: 1.01\u0026ndash;1.03, P\u0026thinsp;=\u0026thinsp;0.001). When stratified by median, those with hsCRP at or above the median had a 38% higher stroke risk (HR: 1.38, 95% CI: 1.16\u0026ndash;1.66, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with persistent significance in subsequent models.\u003c/p\u003e \u003cp\u003eTable S7 primarily discusses the association between CumAIP combined with CumhsCRP levels and stroke incidence. When both CumAIP and CumhsCRP levels are at or above the median, participants have a significantly higher risk of stroke. This association remains significant across all three models, indicating the robustness of the results.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study, which utilized data from the CHARLS, investigated the relationship between the AIP and the risk of stroke in nondiabetic individuals, with a particular focus on varying levels of hsCRP. Elevated AIP levels were significantly associated with an increased stroke risk, emphasizing the synergistic role of lipid metabolism and inflammation in the pathogenesis of stroke. Notably, individuals with high levels of both the AIP and hsCRP had the greatest risk of stroke, highlighting the collaborative impact of lipid dysregulation and inflammation in promoting atherosclerosis and ultimately increasing the risk of stroke. The focus on nondiabetic individuals is particularly important, as diabetes itself is a well-established independent risk factor for stroke, which could confound the relationships among the AIP, inflammation, and the risk of stroke. By excluding diabetic patients, this study provides a clearer understanding of how the AIP and hsCRP independently contribute to the risk of stroke in this specific population.\u003c/p\u003e \u003cp\u003eMoreover, the findings suggest a potential bidirectional relationship between the AIP and hsCRP with regard to the risk of stroke among nondiabetic individuals. The AIP may influence the risk of stroke indirectly by driving inflammation via lipid metabolism, whereas hsCRP could exacerbate the risk of stroke by promoting chronic inflammation that accelerates atherosclerosis driven by the AIP. These results underscore the clinical importance of combining assessments of the AIP and hsCRP to refine the stratification of the risk of stroke in nondiabetic populations. Identifying high-risk individuals allows the implementation of more precise interventions and personalized management strategies, particularly for those with concurrent lipid dysregulation and systemic inflammation. Furthermore, incorporating lipid metabolism and inflammatory markers into clinical risk prediction models could increase the accuracy of the assessment of the risk of stroke and optimize targeted prevention strategies. Future research should further explore the combined predictive and therapeutic potential of the AIP and hsCRP in diverse nondiabetic populations.\u003c/p\u003e \u003cp\u003eAn elevated AIP has been reported to correlate with an increased risk of ischaemic stroke [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], serving as a composite biomarker that reflects the balance between TG and HDL-C levels. Higher AIP values are typically associated with elevated TG and reduced HDL-C levels, both of which are recognized as significant cardiovascular risk factors. Impaired TG metabolism, considered a residual risk factor beyond LDL-C levels, contributes to stroke development and adverse clinical outcomes. Conversely, HDL-C possesses antioxidant and anti-inflammatory properties, mitigating inflammatory responses by inhibiting macrophage activation and reducing cytokine secretion [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Additionally, HDL-C plays a key role in reverse cholesterol transport, providing protective effects against atherosclerosis and reducing the risk of cardiovascular diseases and related conditions. In line with these mechanisms, our study revealed a clear dose‒response relationship between the AIP and the risk of stroke, with individuals in the highest AIP quartile exhibiting a significantly elevated HR for stroke. This strong correlation persisted even after excluding diabetic individuals, highlighting the AIP as an independent predictor of the risk of stroke in nondiabetic populations. These findings underscore the clinical relevance of the AIP as a valuable marker of the risk of atherosclerosis.\u003c/p\u003e \u003cp\u003eMoreover, chronic inflammation is another important cardiovascular risk factor. Numerous reports have suggested that elevated inflammation levels synergistically increase the risk of arterial stiffness[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Among various inflammatory markers, hsCRP has garnered the most attention because of its significant role in risk assessment and reclassification for cardiovascular and cerebrovascular diseases[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Increased inflammation, as indicated by elevated hsCRP levels, may be associated with the progression of arterial stiffness induced by lipid abnormalities[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. The role of inflammation in the propagation of atherosclerosis and susceptibility to cardiovascular events has been well established. We observed that hsCRP was also positively associated with the risk of stroke, and the combination of the AIP and hsCRP significantly improved the predictive accuracy. This combined effect highlights the critical interplay between lipid metabolism and inflammation in the pathogenesis of stroke. Individuals with elevated levels of both the AIP and hsCRP had the highest stroke risk, reinforcing the hypothesis that lipid dysregulation and inflammation synergistically promote atherosclerosis, thereby increasing the likelihood of stroke [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Sensitivity analyses further confirmed the robustness of these results, even when a higher hsCRP threshold of 2 mg/L was applied.\u003c/p\u003e \u003cp\u003eSeveral potential mechanisms may underlie the complex relationships among the AIP, inflammation levels, and arterial stiffness or vascular lesions. Dyslipidaemia, metabolic syndrome, vascular stiffness, and IR are closely related. IR is characterized by endothelial dysfunction and a proinflammatory state, which play crucial roles in the pathophysiology of atherosclerosis, whereas dyslipidaemia, a traditional risk marker for IR and atherosclerosis, has long been involved in chronic inflammation-related pathological processes. In this context, the AIP, as a comprehensive marker, is not only closely associated with IR but also reflects the role of dyslipidaemia in promoting atherosclerosis. Elevated inflammation levels, particularly increased hsCRP, may exacerbate this process. Inflammation may promote the development of IR through mechanisms involving the RAAS, sympathetic nervous system activation, and gut insulin-modulating factors (such as DPP-4), which further promote IR progression[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. The association between hsCRP and lipid particles, along with its ability to activate inflammatory complement[\u003cspan additionalcitationids=\"CR38\" citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], suggests that hsCRP may be involved in the pathogenesis of stroke events.\u003c/p\u003e \u003cp\u003eBuilding on these studies, it is clear that alongside lipid-lowering strategies, addressing cardiovascular risk by reducing inflammation is critically important. Currently, numerous anti-inflammatory therapies, including low-dose colchicine and interleukin-6 inhibitors, are undergoing clinical investigation[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. PCSK9 inhibitors, which are widely employed in clinical practice, not only exert lipid-lowering effects but also demonstrate potential anti-inflammatory properties and protective effects against arterial and venous thrombosis[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Sodium‒glucose cotransporter 2 inhibitors and glucagon-like peptide-1 receptor agonists have been demonstrated to modulate inflammation and reduce the incidence of vascular events[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Accordingly, the assessment of the AIP and hsCRP levels may provide clinicians with critical insights to guide the selection of appropriate therapeutic strategies for the long-term prevention of atherosclerosis.\u003c/p\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eThis study has the limitations inherent in an observational design, which means that a causal relationship could not be determined. Although we found associations of the AIP and hsCRP with the risk of stroke, the nature of the study design did not allow us to rule out the potential impact of confounding factors on the results. Moreover, some information in the CHARLS data is based on self-reports, such as smoking and drinking habits, which may introduce recall bias or inaccurate reporting, thereby affecting the assessment of stroke risk factors. Finally, although this study proposed a stroke risk prediction model based on the AIP and CRP, this model has not yet been externally validated in other independent cohorts, so its applicability and clinical utility in different populations still need further verification.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study demonstrated that elevated AIP and hsCRP levels are significantly associated with the risk of stroke in individuals without diabetes and that the combination of the AIP with hsCRP enhances stroke risk prediction capabilities. The results validate the potential use of the AIP and hsCRP as effective tools for cardiovascular risk assessment and highlight their clinical value in enhancing risk stratification in nondiabetic populations. By facilitating the identification of high-risk individuals among those without diabetes, particularly those with concurrent lipid dysregulation and systemic inflammation, the AIP provides a foundation for developing more precise and personalized treatment strategies.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eCHARLS China Health and Retirement Longitudinal Study；AIP Atherogenic Index of Plasma；hsCRP High-sensitivity C-reactive protein；BMI Body mass index；BUN Blood urea nitrogen；Cr creatinine；CI Confidence interval；FBG Fasting blood glucose；HbA1c Glycosylated hemoglobin A1c；HDLC High-density lipoprotein cholesterol；HR Hazard ratio；hsCRP High-sensitivity C-reactive protein；LDLC Low-density lipoprotein cholesterol；RAAS Renin\u0026ndash;angiotensin\u0026ndash;aldosterone system；RCS Restricted cubic spline；TC Total cholesterol；TG Triglyceride；UA Uric acid ROC Receiver operating characteristic；AUC Area under the curve.\u003c/p\u003e\n"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflict of interest:\u0026nbsp;\u003c/strong\u003eThe authors declare that they have no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u0026nbsp;\u003c/strong\u003eThe CHARLS study was approved by the Institutional Review Board of Peking University (IRB00001052-11015) and conducted according to the Declaration of Helsinki. Written informed consent was obtained from all participants.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u003c/strong\u003eAll the authors listed have approved the manuscript that is enclosed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u003c/strong\u003eThe data that support the findings of this study are available for request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003eThis study was supported by grants from National Natural Science Foundation of China (82001568), Guangdong Basic and Applied Basic Research Foundation (2019A1515110102), China Postdoctoral Science Foundation (2020M682675), PhD Start-up Fund of the Third Affiliated Hospital of Guangzhou Medical University (2019B08) and Science and Technology Projects in Guangzhou (202201020167). The funders played no role in the study design, data collection or analysis, decision to publish, or preparation of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions:\u0026nbsp;\u003c/strong\u003eXiaotong Yao: conception and design of the study, acquisition, analysis, and interpretation of data, drafting a significant portion of the manuscript or figures.Liting Liang, MM: acquisition, analysis, and interpretation of data. Jia Chen, MD PhD : acquisition, analysis, and interpretation of data. Liting Liang, MM, Zhuoji Liang, Ying Piao , MM, and Manting Yang, MM: analysis and interpretation of data.Yanling Liang, MD PhD: conception and design of the study, acquisition, analysis. Xiaobo Fang,, MD PhD: conception and design of the study, interpretation of data, drafting a significant portion of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u0026nbsp;\u003c/strong\u003eThis study used data from China Health and Retirement Longitudinal Study (CHARLS). We would like to thank the CHARLS research team for the time and effort into the CHARLS project.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGe R, You S, Zheng D, Zhang Z, Cao Y, Chang J: \u003cstrong\u003eGlobal, regional, and national temporal trends of diet-related ischemic stroke mortality and disability from 1990 to 2019.\u003c/strong\u003e \u003cem\u003eInternational journal of stroke : official journal of the International Stroke Society\u003c/em\u003e 2024, \u003cstrong\u003e19\u003c/strong\u003e(6):665-675.\u003c/li\u003e\n\u003cli\u003eSu B, Guo S, Zheng X: \u003cstrong\u003eTransitions in Chronic Disease Mortality in China: Evidence and Implications.\u003c/strong\u003e \u003cem\u003eCHINA CDC WEEKLY\u003c/em\u003e 2023, \u003cstrong\u003e5\u003c/strong\u003e(50):1131-1134.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eThe transition to modernity and chronic disease: mismatch and natural selection - Nature Reviews Genetics\u003c/strong\u003e. 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Cardiovascular pharmacotherapy\u003c/em\u003e 2019, \u003cstrong\u003e5\u003c/strong\u003e(4):237-245.\u003c/li\u003e\n\u003cli\u003eNeves JS, Borges-Canha M, Vasques-N\u0026oacute;voa F, Green JB, Leiter LA, Granger CB, Carvalho D, Leite-Moreira A, Hernandez AF, Del Prato S\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eGLP-1 Receptor Agonist Therapy With and Without SGLT2 Inhibitors in Patients With Type 2 Diabetes.\u003c/strong\u003e \u003cem\u003eJ AM COLL CARDIOL\u003c/em\u003e 2023, \u003cstrong\u003e82\u003c/strong\u003e(6):517-525.\u003c/li\u003e\n\u003cli\u003ePatorno E, Htoo PT, Glynn RJ, Schneeweiss S, Wexler DJ, Pawar A, Bessette LG, Chin K, Everett BM, Kim SC: \u003cstrong\u003eSodium-Glucose Cotransporter-2 Inhibitors Versus Glucagon-like Peptide-1 Receptor Agonists and the Risk for Cardiovascular Outcomes in Routine Care Patients With Diabetes Across Categories of Cardiovascular Disease.\u003c/strong\u003e \u003cem\u003eANN INTERN MED\u003c/em\u003e 2021, \u003cstrong\u003e174\u003c/strong\u003e(11):1528-1541.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1: Baseline characteristics of the study population across\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eAIP\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;quartiles.\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"718\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.7822%;\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1834%;\"\u003e\n \u003cp\u003eOverall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.7536%;\"\u003e\n \u003cp\u003eQuartile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.6103%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0401%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0401%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.5903%;\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.7822%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1834%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.7536%;\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.6103%;\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0401%;\"\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0401%;\"\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.5903%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.7822%;\"\u003e\n \u003cp\u003eParticipants,No.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1834%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;8909\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.7536%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;2228\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.6103%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;2227\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0401%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;2227\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0401%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;2227\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.5903%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.7822%;\"\u003e\n \u003cp\u003eAge,years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1834%;\"\u003e\n \u003cp\u003e58.56(9.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.7536%;\"\u003e\n \u003cp\u003e59.26(9.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.6103%;\"\u003e\n \u003cp\u003e58.71 (9.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0401%;\"\u003e\n \u003cp\u003e58.42(9.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0401%;\"\u003e\n \u003cp\u003e57.85(9.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.5903%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.7822%;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1834%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.7536%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.6103%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0401%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0401%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.5903%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.7822%;\"\u003e\n \u003cp\u003efemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1834%;\"\u003e\n \u003cp\u003e4784(53.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.7536%;\"\u003e\n \u003cp\u003e1097(49.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.6103%;\"\u003e\n \u003cp\u003e1213 (54.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0401%;\"\u003e\n \u003cp\u003e1226 (55.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0401%;\"\u003e\n \u003cp\u003e1248 (56.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.5903%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.7822%;\"\u003e\n \u003cp\u003emale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1834%;\"\u003e\n \u003cp\u003e4125(46.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.7536%;\"\u003e\n \u003cp\u003e1131(50.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.6103%;\"\u003e\n \u003cp\u003e1014 (45.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0401%;\"\u003e\n \u003cp\u003e1001(44.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0401%;\"\u003e\n \u003cp\u003e979(44.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.5903%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.7822%;\"\u003e\n \u003cp\u003eEducation level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1834%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.7536%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.6103%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0401%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0401%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.5903%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.7822%;\"\u003e\n \u003cp\u003ehigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1834%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; 139 ( 1.6)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.7536%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;29 ( 1.3)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.6103%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;30 ( 1.3)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0401%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;35 ( 1.6)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0401%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;45 ( 2.0)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.5903%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.7822%;\"\u003e\n \u003cp\u003emiddle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1834%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;2643 (29.7)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.7536%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; 591 (26.5)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.6103%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; 643 (28.9)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0401%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; 685 (30.8)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0401%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; 724 (32.5)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.5903%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.7822%;\"\u003e\n \u003cp\u003eprimary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1834%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;6127(68.8)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.7536%;\"\u003e\n \u003cp\u003e\u0026nbsp;1608(72.2)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.6103%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;1554 (69.8)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0401%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;1507 (67.7)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0401%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;1458 (65.5)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.5903%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.7822%;\"\u003e\n \u003cp\u003eResidence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1834%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.7536%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.6103%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0401%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0401%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.5903%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.7822%;\"\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1834%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;7365(82.7)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.7536%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;1927(86.5)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.6103%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;1873 (84.1)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0401%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;1829 (82.1)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0401%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;1736 (78.0)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.5903%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.7822%;\"\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1834%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;1544(17.3)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.7536%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;301(13.5)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.6103%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; 354 (15.9)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0401%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; 398 (17.9)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0401%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; 491 (22.0)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.5903%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.7822%;\"\u003e\n \u003cp\u003eMarital status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1834%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.7536%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.6103%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0401%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0401%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.5903%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.7822%;\"\u003e\n \u003cp\u003emarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1834%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;7914(88.8)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.7536%;\"\u003e\n \u003cp\u003e\u0026nbsp;1963(88.1)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.6103%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;1974 (88.6)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0401%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;1980 (88.9)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0401%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;1997 (89.7)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.5903%;\"\u003e\n \u003cp\u003e\u0026nbsp;0.413\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.7822%;\"\u003e\n \u003cp\u003eothers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1834%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; 995 (11.2)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.7536%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;265(11.9)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.6103%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; 253 (11.4)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0401%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; 247 (11.1)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0401%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; 230 (10.3)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.5903%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.7822%;\"\u003e\n \u003cp\u003eSmoking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1834%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.7536%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.6103%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0401%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0401%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.5903%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.7822%;\"\u003e\n \u003cp\u003eno\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1834%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;5620(63.1)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.7536%;\"\u003e\n \u003cp\u003e\u0026nbsp;1353(60.7)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.6103%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;1403 (63.0)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0401%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;1438 (64.6)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0401%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;1426 (64.0)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.5903%;\"\u003e\n \u003cp\u003e\u0026nbsp;0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.7822%;\"\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1834%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;3289(36.9)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.7536%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;875(39.3)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.6103%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; 824 (37.0)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0401%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; 789 (35.4)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0401%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; 801 (36.0)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.5903%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.7822%;\"\u003e\n \u003cp\u003eDrinking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1834%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.7536%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.6103%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0401%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0401%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.5903%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.7822%;\"\u003e\n \u003cp\u003eno\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1834%;\"\u003e\n \u003cp\u003e5937 (66.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.7536%;\"\u003e\n \u003cp\u003e1316 (59.1)\u003c/p\u003e\n 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703 (31.6)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0401%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; 672 (30.2)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0401%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; 685 (30.8)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.5903%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.7822%;\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1834%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.7536%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.6103%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0401%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0401%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.5903%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.7822%;\"\u003e\n \u003cp\u003eContinuous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1834%;\"\u003e\n \u003cp\u003e21.80 (4.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.7536%;\"\u003e\n \u003cp\u003e20.71 (4.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.6103%;\"\u003e\n \u003cp\u003e\u0026nbsp;21.45 (4.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0401%;\"\u003e\n \u003cp\u003e\u0026nbsp;22.10 (5.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0401%;\"\u003e\n \u003cp\u003e\u0026nbsp;22.93 (5.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.5903%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n 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\u003cp\u003e24-27.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1834%;\"\u003e\n \u003cp\u003e2131 (23.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.7536%;\"\u003e\n \u003cp\u003e342 (15.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.6103%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; 494 (22.2)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0401%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; 598 (26.9)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0401%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; 697 (31.3)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.5903%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.7822%;\"\u003e\n \u003cp\u003e\u0026ge;28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1834%;\"\u003e\n \u003cp\u003e2126 (23.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.7536%;\"\u003e\n \u003cp\u003e365 (16.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.6103%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; 451 (20.3)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0401%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; 582 (26.1)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0401%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; 728 (32.7)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.5903%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.7822%;\"\u003e\n \u003cp\u003eSBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1834%;\"\u003e\n \u003cp\u003e123.64 (24.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.7536%;\"\u003e\n \u003cp\u003e121.94 (23.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.6103%;\"\u003e\n \u003cp\u003e122.70 (24.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0401%;\"\u003e\n \u003cp\u003e124.60 (25.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0401%;\"\u003e\n \u003cp\u003e125.31 (25.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.5903%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.7822%;\"\u003e\n \u003cp\u003eDBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1834%;\"\u003e\n \u003cp\u003e73.90 (16.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.7536%;\"\u003e\n \u003cp\u003e72.59 (16.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.6103%;\"\u003e\n \u003cp\u003e\u0026nbsp;73.21 (16.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0401%;\"\u003e\n \u003cp\u003e\u0026nbsp;74.38 (16.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0401%;\"\u003e\n \u003cp\u003e\u0026nbsp;75.42 (17.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.5903%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.7822%;\"\u003e\n \u003cp\u003eBUN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1834%;\"\u003e\n \u003cp\u003e15.69 (4.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.7536%;\"\u003e\n \u003cp\u003e16.50 (4.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.6103%;\"\u003e\n \u003cp\u003e\u0026nbsp;15.89 (4.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0401%;\"\u003e\n \u003cp\u003e\u0026nbsp;15.28 (4.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0401%;\"\u003e\n \u003cp\u003e\u0026nbsp;15.11 (4.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.5903%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.7822%;\"\u003e\n \u003cp\u003eUA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1834%;\"\u003e\n \u003cp\u003e4.42 (1.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.7536%;\"\u003e\n \u003cp\u003e4.23 (1.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.6103%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;4.29 (1.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0401%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;4.41 (1.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0401%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;4.75 (1.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.5903%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.7822%;\"\u003e\n \u003cp\u003eTG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1834%;\"\u003e\n \u003cp\u003e121.82(75.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.7536%;\"\u003e\n \u003cp\u003e61.50 (14.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.6103%;\"\u003e\n \u003cp\u003e\u0026nbsp;88.55 (17.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0401%;\"\u003e\n \u003cp\u003e121.74 (24.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0401%;\"\u003e\n \u003cp\u003e215.51 (90.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.5903%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.7822%;\"\u003e\n \u003cp\u003eTC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1834%;\"\u003e\n \u003cp\u003e191.79(37.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.7536%;\"\u003e\n \u003cp\u003e187.41(34.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.6103%;\"\u003e\n \u003cp\u003e189.15 (36.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0401%;\"\u003e\n \u003cp\u003e192.54 (36.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0401%;\"\u003e\n \u003cp\u003e198.05 (40.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.5903%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.7822%;\"\u003e\n \u003cp\u003eHDLC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1834%;\"\u003e\n \u003cp\u003e52.02 (14.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.7536%;\"\u003e\n \u003cp\u003e67.49 (13.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.6103%;\"\u003e\n \u003cp\u003e\u0026nbsp;54.80 (10.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0401%;\"\u003e\n \u003cp\u003e\u0026nbsp;47.55 (8.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0401%;\"\u003e\n \u003cp\u003e\u0026nbsp;38.21 (8.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.5903%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.7822%;\"\u003e\n \u003cp\u003eLDLC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1834%;\"\u003e\n \u003cp\u003e116.58(33.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.7536%;\"\u003e\n \u003cp\u003e110.23(29.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.6103%;\"\u003e\n \u003cp\u003e119.04 (32.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0401%;\"\u003e\n \u003cp\u003e122.39 (33.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0401%;\"\u003e\n \u003cp\u003e114.68 (37.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.5903%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.7822%;\"\u003e\n \u003cp\u003eHbA1c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1834%;\"\u003e\n \u003cp\u003e5.10 (0.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.7536%;\"\u003e\n \u003cp\u003e5.07 (0.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.6103%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;5.09 (0.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0401%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;5.10 (0.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0401%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;5.13 (0.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.5903%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.7822%;\"\u003e\n \u003cp\u003ehsCRP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1834%;\"\u003e\n \u003cp\u003e2.06 (3.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.7536%;\"\u003e\n \u003cp\u003e1.89 (3.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.6103%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;1.99 (3.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0401%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;2.11 (3.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0401%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;2.25 (3.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.5903%;\"\u003e\n \u003cp\u003e\u0026nbsp;0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.7822%;\"\u003e\n \u003cp\u003eFBG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1834%;\"\u003e\n \u003cp\u003e99.92(12.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.7536%;\"\u003e\n \u003cp\u003e97.77(12.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.6103%;\"\u003e\n \u003cp\u003e\u0026nbsp;98.54 (11.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0401%;\"\u003e\n \u003cp\u003e\u0026nbsp;99.95 (11.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0401%;\"\u003e\n \u003cp\u003e103.44 (11.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.5903%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.7822%;\"\u003e\n \u003cp\u003eCr\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1834%;\"\u003e\n \u003cp\u003e0.78 (0.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.7536%;\"\u003e\n \u003cp\u003e0.77 (0.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.6103%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;0.77 (0.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0401%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;0.78 (0.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0401%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;0.79 (0.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.5903%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.7822%;\"\u003e\n \u003cp\u003eComorbidities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1834%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.7536%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.6103%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0401%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0401%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.5903%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.7822%;\"\u003e\n \u003cp\u003eDyslipidemia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1834%;\"\u003e\n \u003cp\u003e654 ( 7.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.7536%;\"\u003e\n \u003cp\u003e101 ( 4.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.6103%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; 129 ( 5.8)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0401%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; 163 ( 7.3)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0401%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; 261 (11.7)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.5903%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.7822%;\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1834%;\"\u003e\n \u003cp\u003e1954 (21.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.7536%;\"\u003e\n \u003cp\u003e353 (15.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.6103%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; 410 (18.4)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0401%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; 552 (24.8)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0401%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; 639 (28.7)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.5903%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.7822%;\"\u003e\n \u003cp\u003eHeart disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1834%;\"\u003e\n \u003cp\u003e1321 (14.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.7536%;\"\u003e\n \u003cp\u003e310 (13.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.6103%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; 302 (13.6)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0401%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; 372 (16.7)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0401%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; 337 (15.1)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.5903%;\"\u003e\n \u003cp\u003e\u0026nbsp;0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.7822%;\"\u003e\n \u003cp\u003eStroke\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1834%;\"\u003e\n \u003cp\u003e696 ( 7.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.7536%;\"\u003e\n \u003cp\u003e123 ( 5.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.6103%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; 171 ( 7.7)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0401%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; 197 ( 8.8)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0401%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; 205 ( 9.2)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.5903%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.7822%;\"\u003e\n \u003cp\u003eHistory of medication use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1834%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.7536%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.6103%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0401%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0401%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.5903%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.7822%;\"\u003e\n \u003cp\u003eDyslipidemia medications\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1834%;\"\u003e\n \u003cp\u003e348 ( 3.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.7536%;\"\u003e\n \u003cp\u003e43 ( 1.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.6103%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;70 ( 3.1)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0401%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;85 ( 3.8)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0401%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; 150 ( 6.7)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.5903%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23.7822%;\"\u003e\n \u003cp\u003eHypertension medications\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.1834%;\"\u003e\n \u003cp\u003e1411 (15.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.7536%;\"\u003e\n \u003cp\u003e235 (10.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.6103%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; 286 (12.8)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0401%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; 401 (18.0)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.0401%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; 489 (22.0)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.5903%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eP value was based on \u0026chi;2 or analysis of variance or Kruskal-Wallis rank sum test where appropriate.\u003c/p\u003e\n\u003cp\u003eAIP: Q1 \u0026lt; 0.105 , Q2 0.105\u0026ndash;0.302 , Q3 0.302-0.522, Q4 \u0026gt;0.522 .\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBMI body mass index, BUN blood urea nitrogen, DBP diastolic blood pressure, DM diabetes mellitus, FBG fasting blood glucose, HbA1c glycosylated hemoglobin A1c, HDLC high density lipoprotein cholesterol, hsCRP high-sensitivity C-reactive protein, LDLC low density lipoprotein cholesterol, SBP systolic blood pressure, TC total cholesterol, TG triglycerides, UA uric acid,AIP atherogenic index of plasma.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003cstrong\u003e: Cox Proportional Hazards Model Analysis of AIP and hsCRP Levels with\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eStroke\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eEvent Ris\u003c/strong\u003e\u003cstrong\u003ek\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"718\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNo. of events (Incident ratea)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003eModel1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003eModel2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003eModel3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAIP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQuartiles\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.44(1.14-1.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.43(1.13-1.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.39(1.10-1.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e197\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.69(1.35-2.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.67(1.33-2.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.48(1.18-1.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.79(1.43-2.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.75(1.40-2.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.49(1.18-1.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eP for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003esensitivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAIP \u0026lt; median\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e294\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAIP\u0026ge;median\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e402\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.43(1.23-1.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.41(1.34-1.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.26(1.08-1.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ehsCRP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ehsCRP \u0026lt;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e275\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ehsCRP\u0026ge;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e421\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.58(1.36-1.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.56(1.11-1.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.43(1.23-1.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003esensitivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ehsCRP \u0026lt; 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e480\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ehsCRP\u0026ge;2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e216\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.30(1.10-1.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.28(1.08-1.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.18(1.01-1.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eAbbreviations: CI, confidence interval HR, hazard ratio.\u003c/p\u003e\n\u003cp\u003eMode1: Adjusted for age, gender.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMode2: Adjusted for age, gender, bmi level, smoking status, and drinking status.\u003c/p\u003e\n\u003cp\u003eMode3: Adjusted for age, gender, bmi level, marital status,education level, smoking status, drinking status, hypertension, heart disease, dyslipidaemia, and history of medication use for dyslipidaemia、hypertension.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003cstrong\u003e: Risk of\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003estroke\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;upon coexposure stratified by the\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eAIP\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;and hsCRP\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"611\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eModel1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eModel 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eAIP \u0026lt; median \u0026amp; CRP \u0026lt; \u0026nbsp;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eAIP \u0026lt; median \u0026amp; crp\u0026nbsp;\u0026ge;\u0026nbsp;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e1.48 (1.17-1.86) \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e1.45 (1.15-1.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.001 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e1.40(1.11-1.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eAIP\u0026nbsp;\u0026ge;\u0026nbsp;median \u0026amp; CRP \u0026lt; 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e1.32 (1.04-1.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e1.29 (1.02-1.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e1.21 (0.96-1.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e0.111\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eAIP\u0026nbsp;\u0026ge;\u0026nbsp;median \u0026amp; CRP\u0026nbsp;\u0026ge;\u0026nbsp;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e2.04 (1.66-2.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e1.95 (1.59-2.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e1.69 (1.36-2.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 43px;\"\u003e\n \u003cp\u003eP for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e\u003cstrong\u003eSensitivity analysis of AIP and hsCRP with new-onset stroke.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"610\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eModel1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003eModel 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003eAIP \u0026lt; median \u0026amp; CRP \u0026lt; 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003eAIP \u0026lt; median \u0026amp; CRP\u0026nbsp;\u0026ge;\u0026nbsp;2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e1.30 (1.00-1.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e1.28 (0.99-1.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e0.065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e1.23(0.95-1.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0.122\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003eAIP\u0026nbsp;\u0026ge;\u0026nbsp;median \u0026amp; CRP \u0026lt; 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e1.43 (1.19-1.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e1.39 (1.16-1.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e1.28(1.07-1.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003eAIP\u0026nbsp;\u0026ge;\u0026nbsp;median \u0026amp; CRP\u0026nbsp;\u0026ge;\u0026nbsp;2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e1.74 (1.41-2.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e1.66 (1.34-2.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e1.40(1.12-1.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003eP for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003cstrong\u003e: Incremental predictive value of\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ehsCRP\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;and\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eAIP\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;beyond traditional risk factors.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eModels\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAUC(95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003csub\u003eAUC\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNRI (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003csub\u003eNRI\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIDI (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003csub\u003eIDI\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTraditional model \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.673(0.652-0.693)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTraditional model +hs CRP + AIP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.680(0.659-0.700)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.106(0.028-0.183)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.002(0.001-0.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations:\u003c/strong\u003e AUC, area under curve; BMI, body mass index; CI, confidence interval; NRI,net reclassification index;IDI,integrated discrimination.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ea\u003c/sup\u003e Traditional model based on gender, sex, marital status, residence, education level, smoking status, drinking status,hypertension, heart disease, dyslipidaemia, and history of medication use for hypertension, \u0026nbsp; dyslipidaemia.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003cstrong\u003e: Cox Proportional Hazards Model Analysis of\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eCum\u003c/strong\u003e\u003cstrong\u003eAIP and\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eCum\u003c/strong\u003e\u003cstrong\u003ehsCRP Levels with\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eStroke\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eEvent Ris\u003c/strong\u003e\u003cstrong\u003ek\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"718\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNo. of events (Incident ratea)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003eModel1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003eModel2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003eModel3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCumAIP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTertiles\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.44(1.14-1.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.37(1.09-1.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.35(1.07-1.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e202\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.75(1.40-2.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.57(1.25-1.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.49(1.18-1.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eP for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003esensitivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCumAIP \u0026lt; median\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e217\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCumAIP\u0026ge;median\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e280\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.38(1.15-1.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.27(1.06-1.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.21(1.01-1.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCumhsCRP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eContinuous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e497\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.02(1.01-1.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.01(1.01-1.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.01(1.00-1.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCumhsCRP \u0026lt;median\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCumhsCRP\u0026ge;median\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e293\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.38(1.16-1.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.29(1.08-1.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.20(1.01-1.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eAbbreviations: CI, confidence interval HR, hazard ratio.\u003c/p\u003e\n\u003cp\u003eMode1: Adjusted for age, gender.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMode2: Adjusted for age, gender, bmi level, smoking status, and drinking status.\u003c/p\u003e\n\u003cp\u003eMode3: Adjusted for age, gender, bmi level, marital status,education level, smoking status, drinking status, hypertension, heart disease, dyslipidaemia, and history of medication use for dyslipidaemia、hypertension.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e7\u003c/strong\u003e\u003cstrong\u003e: Risk of\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003estroke\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;upon coexposure stratified by the\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eCum\u003c/strong\u003e\u003cstrong\u003eAIP\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;and\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Cum\u003c/strong\u003e\u003cstrong\u003ehsCRP\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"733\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eModel1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eModel 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLow Risk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eModerate Risk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.30(1.03-1.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.27(01.01-1.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.18(0.931-1.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.172\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHigh Risk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.76 (1.38-2.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.63(1.27-2.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.41(1.10-1.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eP for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eLow Risk:CumAIP \u0026lt; median \u0026amp; CumhsCRP \u0026lt; median; Moderate Risk:CumAIP\u0026nbsp;\u0026ge;\u0026nbsp;median \u0026amp;\u0026nbsp;CumhsCRP\u0026nbsp;\u0026lt; median , CumAIP \u0026lt;\u0026nbsp;median \u0026amp;\u0026nbsp;CumhsCRP\u0026nbsp;\u0026ge;\u0026nbsp;median;,High Risk:CumAIP\u0026nbsp;\u0026ge;\u0026nbsp;median \u0026amp;\u0026nbsp;CumhsCRP\u0026nbsp;\u0026ge;\u0026nbsp;median\u003c/p\u003e\n\u003cp\u003eModel1: Adjusted for age, gender.\u003c/p\u003e\n\u003cp\u003eModel2: Adjusted for age, gender, bmi level, smoking status, and drinking status.\u003c/p\u003e\n\u003cp\u003eModel3:Adjusted for age, gender, marital status,residence, education level, smoking status, drinking status, hypertension, diabetes, heart disease, dyslipidaemia, and history of medication use for hypertension, and dyslipidaemia.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-neurology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nurl","sideBox":"Learn more about [BMC Neurology](http://bmcneurol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nurl","title":"BMC Neurology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Atherogenic index of plasma (AIP), Insulin resistance, Inflammation, High-sensitivity C-reactive protein (hsCRP), Stroke","lastPublishedDoi":"10.21203/rs.3.rs-5831890/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5831890/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003cbr\u003e\nBoth the atherogenic index of plasma (AIP), a surrogate marker of insulin resistance, and high-sensitivity C-reactive protein (hsCRP) are predictors of stroke risk and clinical outcomes. However, most existing evidence is derived from studies involving diabetic patients, which may lead to the overestimation of the impact of the AIP and hsCRP on stroke due to the confounding effects of diabetes. This study aimed to assess the combined and interactive effects of the AIP and hsCRP on stroke events in individuals without diabetes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003cbr\u003e\nA total of 8,909 participants from the China Health and Retirement Longitudinal Study (CHARLS) 2011 who were free of stroke and diabetes at baseline were included. The AIP was calculated as lg[total cholesterol (mmol/L)/high-density lipoprotein cholesterol (mmol/L)]. A subset of 5,954 participants was studied to investigate the relationship between cumulative AIP (CumAIP) and hsCRP (CumAIP) exposure and stroke incidence.The CumAIP and CumCRP were also calculated using the same algorithm.The primary outcome was physician-diagnosed stroke occurring before 2020. We employed adjusted Cox proportional hazards regression and mediation analysis to investigate the associations between the AIP, hsCRP, and stroke events.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003cbr\u003e\nOver nine years of follow-up, 696 new stroke cases were recorded.Compared with individuals with low AIP (\u0026lt;0.302 [median level]) and hsCRP \u0026lt;1 mg/L, those with elevated levels of both the AIP and hsCRP had the highest overall risk of stroke (adjusted HR [aHR]: 1.69; 95% CI: 1.36–2.10). In a 5-year subset analysis, 497 participants suffered a stroke. Compared with individuals with low risk (CumAIP\u0026lt;1.29 [median level] and CumhsCRP \u0026lt; 4.02 mg/L [median level]), those with high risk had the highest overall risk of stroke (adjusted HR [aHR]: 1.41; 95% CI: 1.10-1.82). Moreover, hsCRP significantly mediated 5.61% of the association between the AIP and stroke, whereas the AIP mediated 1.86% of the association between hsCRP and stroke.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003cbr\u003e\nThe AIP and hsCRP exhibit coexposure effects and mutual mediation in with regard to the risk of stroke. The combined assessment of the AIP and hsCRP should be promoted for residual risk stratification and primary prevention of stroke in individuals without diabetes, particularly among middle-aged populations.\u003c/p\u003e","manuscriptTitle":"Association of the Atherogenic Index of Plasma and High-Sensitivity C-Reactive Protein with Incident Stroke Among Individuals Without Diabetes: A National Cohort Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-20 09:32:34","doi":"10.21203/rs.3.rs-5831890/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2025-03-01T09:57:32+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-01-21T15:01:00+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-01-15T13:02:11+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-01-15T13:01:46+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Neurology","date":"2025-01-15T06:43:27+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-neurology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nurl","sideBox":"Learn more about [BMC Neurology](http://bmcneurol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nurl","title":"BMC Neurology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"03110ac9-4c03-4cca-bc07-058720ff15c4","owner":[],"postedDate":"January 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-01-20T09:32:34+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-20 09:32:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5831890","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5831890","identity":"rs-5831890","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}