Association between atherogenic index of plasma and new-onset stroke in individuals with different glucose metabolism status: insights from CHARLS

Association between atherogenic index of plasma and new-onset stroke in individuals with different glucose metabolism status: insights from CHARLS | 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 between atherogenic index of plasma and new-onset stroke in individuals with different glucose metabolism status: insights from CHARLS Longjie qu, Shuang Fang, Zhen Lan, Shuai Xu, Jialiu Jiang, Yilin Pan, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4261103/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 20 Jun, 2024 Read the published version in Cardiovascular Diabetology → Version 1 posted 13 You are reading this latest preprint version Abstract Background Circulating atherogenic index of plasma (AIP) levels has been proposed as a novel biomarker for dyslipidemia and as a predictor of insulin resistance (IR) risk. However, the association between AIP and the incidence of new-onset stroke, particularly in individuals with varying glucose metabolism states, remains ambiguous. Methods A total of 8727 participants aged 45 years or older without a history of stroke from the China Health and Retirement Longitudinal Study (CHARLS)were included in this study. The AIP was calculated using the formula log [Triglyceride (mg/dL) / High-density lipoprotein cholesterol (mg/dL)]. Participants were divided into four groups based on their baseline AIP levels: Q1(AIP≤0.122), Q2(0.122<AIP≤0.329), Q3(0.3290.562). The primary endpoint was the occurrence of new-onset stroke events. The Kaplan–Meier curves, Multivariate Cox proportional hazard models, and Restricted cubic spline (RCS) analysis were applied to explore the association between baseline AIP levels and the risk of developing a stroke among individuals with varying glycemic metabolic states. Results During a median follow-up of 9 years, 734 participants (8.4%) had a first stroke event. The relative risk for stroke increased with each increasing quartile of baseline AIP levels. Kaplan–Meier curve analysis revealed a significant difference in stroke occurrence among the AIP groups in all participants, as well as in those with prediabetes mellitus (Pre-DM) and diabetes mellitus (DM) (all P-values <0.05). After adjusting for potential confounders, the prevalence of stroke was significantly higher in the Q2, Q3, and Q4 groups than in the Q1 group in all participants. The respective hazard ratios (95% confidence interval) for stroke in the Q2, Q3, and Q4 groups were 1.34 (1.05-1.71), 1.52 (1.19-1.93), and 1.84 (1.45-2.34). Furthermore, high levels of AIP were found to be linked to an increased risk of stroke in both pre-diabetic and diabetic participants across all three Cox models. However, this association was not observed in participants with normal glucose regulation (NGR) (p>0.05). Restricted cubic spline analysis also demonstrated that higher baseline AIP levels were associated with hazard ratios for stroke in all participants and those with glucose metabolism disorders. Conclusions An increase in baseline AIP levels was significantly associated with the risk of stroke in middle-aged and elderly individuals, and exhibited distinct characteristics depending on the individual’s glucose metabolism status. Atherogenic index of plasma Stroke Dysglycemia Figures Figure 1 Figure 2 Figure 3 Introduction Stroke is a major global public health burden with high morbidity and mortality. Despite diligent primary prevention efforts, the prevalence and incidence of stroke in China continue to exhibit an alarming increase. Consequently, it is imperative to develop low-cost and reproducible indicators that can hence early identification of high-risk individuals with stroke(1). Many studies have elucidated that metabolic disorders, including dyslipidemia and hyperglycemia, are significant risk factors for stroke. Several metabolic indicators, such as remnant cholesterol and TyG index, have been utilized to assess the risk and prognosis of stroke(2–4), but the predictive accuracy is still limited. The atherogenic index of plasma (AIP), a logarithmically transformed ratio of fasting triglyceride to fasting high-density lipoprotein cholesterol, is a sensitive marker of lipoprotein profiles primarily reflecting plasma lipid levels. Recent researches suggest that rising AIP can indicate the severity of insulin resistance and is closely related to the development of insulin resistance and type 2 diabetes(5–7). As a robust biomarker of dyslipidemia, AIP has been considered to be a powerful independent predictor of adverse cardiovascular and cerebrovascular events. AIP is potentially served as a significant predictor of intracranial arterial stenosis, the risk of ischemic stroke, and poor stroke outcomes(8–11). On the other hand, AIP serves as a reliable indicator of insulin resistance, which indicates that it may differentiate stroke risk in individuals with abnormal glucose metabolism. However, the relationship between AIP and stroke based on an individual's glucose metabolism status has been poorly investigated. Therefore, prospective cohort studies with a large sample size are warranted to clarify the relationship. In the present study, we used data from the China Health and Retirement Longitudinal Study ( CHARLS) to test the association between baseline AIP levels and stroke under different glucose metabolic states. Methods Study participants The CHARLS is a national prospective cohort study that commenced in 2011, focusing on individuals aged 45 years and older in China. The participants are traced once every 2–3 years to identify their health status. To date, five rounds of follow-up surveys have been completed, with data collected in 2011, 2013, 2015, 2018, and 2020. The detailed research methods have been described previously(12). We initially enrolled 17708 participants in CHARLS wave 1. Among these people, 8981 were excluded for meeting the following exclusion criteria: (1) missing available data on AIP, fasting blood glucose (FPG), glycosylated hemoglobin (HbA1c) (n = 6132), (2) age < 45 years old or missing data on age (n = 423), (3) personal cancer history (n = 102), (4) history of stroke (n = 345), (5) lack of data on stroke or lost to follow-up (n = 1979). Finally, 8727 participants were divided into four groups according to the baseline AIP quartiles and were followed up until 2020 (Fig. 1). The CHARLS study received ethical approval from the Biomedical Ethics Review Committee of Peking University (IRB00001052-1101). Data collection Trained interviewers collected the demographic information (such as age, sex, and marital status), health status and functioning (such as smoking, drinking, hypertension, and diabetes) of the participants with standard questionnaires. All participants, except those with an arm injury, were instructed to undergo three blood pressure measurements at a 45-second interval, and the average of three values was reported. Body weight and height were measured using standardized scales to the nearest 0.1 kg and 0.1 cm, respectively. Venous blood samples were collected from each participant by medically trained staff following a standard phlebotomy protocol and assayed for biochemical measurements. Regrettably, nearly 8% of the participants who gave blood reported that they fasted less than 8 hours before blood collection. The enzymatic colorimetric method was applied to measure FPG levels and serum lipid parameters, while HbA1c was measured using Boronate affinity HPLC(13). Figure 1 The flowchart of study participants Definitions Hypertension diagnosis was based on a self-reported physician-diagnosed, and/or any antihypertensive medication use, and/or an average systolic/diastolic blood pressure (SBP/DBP) ≥ 140/90 mmHg(14). Diabetes mellitus (DM) was defined as FPG ≥ 126mg/dl or HbA1c ≥ 6.5%, and/or a self-reported physician-diagnosed, and/or taking hypoglycemic agents. Prediabetes mellitus (Pre-DM) was characterized by an FPG of 100 to 125 mg/dL or an HbA1c of 5.7–6.4%. Individuals who were without DM or Pre-DM were classified as having normal glucose regulation (NGR)(15). Dyslipidemia was diagnosed by a self-reported physician-diagnosed, and/or current use of lipid-lowering drugs, and/or total cholesterol (TC) ≥ 240 mg/dl, triglyceride (TG) ≥ 150 mg/dl, high-density lipoprotein cholesterol (HDL-C) < 40 mg/dl, low-density lipoprotein cholesterol (LDL-C) ≥ 160 mg/dl(16). Cancer and heart disease were determined by participants' self-reported. Body mass index (BMI) was calculated as the following formula: weight/height 2 (kg/m 2 ). The atherogenic index of plasma (AIP) was calculated as log (TG/HDL-C)(17). Follow up of endpoint events The primary focus of the study was on the occurrence of first stroke, which included both cerebral infarction and cerebral hemorrhage. Self-reported stroke was assessed with the following questions: “Have you been diagnosed with stroke by a doctor”; “Have you been diagnosed with stroke by a doctor since the last follow-up visit?”; “Compared to when we interviewed you last time, is your stroke condition better, about the same as it was then, or worse?”. The time of stroke events was determined by participants' responses to questions: “When was the stroke first diagnosed or known by yourself?”; “When was your most recent stroke?”. All participants were followed up through five-wave interviews conducted from 2011 to either the occurrence of a stroke or 2020, whichever came first. Statistical analysis Normally distributed continuous data were presented as mean ± standard deviation and analyzed for statistical significance using a one-way ANOVA. Non-normally distributed continuous data were expressed as median and interquartile range and analyzed by the Kruskal–Wallis test. Categorical data were described with counts and percentages and assessed using the chi-square test. Missing data of the participants included in this study were presented in Table S1 , and the multiple imputations method was applied to impute the missing data, assuming that the data was randomly missing. Participants were divided into four groups according to the quartile level of the AIP: Quartile 1 (Q1), AIP ≤ 0.122; Quartile 2 (Q2), 0.122 < AIP ≤ 0.329; Quartile 3 (Q3), 0.329 0.562. Based on AIP grouping, the Kaplan–Meier method was used to estimate the cumulative incidence of stroke and compared using the log-rank test. Cox proportional hazard regression analyses were conducted to investigate the relationship between baseline AIP and the occurrence of stroke and to calculate the hazard ratio (HR) and 95% confidence interval (CI). Before analyzing, the assumption of proportional hazards was visually assessed by calculating the Schoenfeld residuals. Three models were estimated: Model 1 applied an unadjusted model to estimate crude HR; Model 2 included adjustments for age, gender, marital status, drinking, smoking, residence, SBP, DBP, and BMI; Model 3 included adjustments for the variables in Model 2 as well as history of hypertension and heart disease, TC, FPG, HbA1c. The quartile 1 group was set as the reference in all models. In addition, restricted cubic splines (RCS) analysis based on multivariable-adjusted Cox regression was conducted to visualize the linear or nonlinear relationship between baseline AIP levels and the risk of stroke. Moreover, to determine the prognostic value of AIP for stroke in different glucose metabolic states, we analyzed participants with NGR, Pre-DM, and DM, respectively. Subgroup analyses were stratified by baseline age (< 60 and ≥ 60 years), gender, BMI (< 24 and ≥ 24 kg/m2), residence (rural and urban), hypertension, and glucose metabolic states (NGR, Pre-DM, and DM) to assess the consistency of the adverse effect of AIP on new-onset stroke. All statistical analyses were performed using IBM SPSS Statistics (Version 26) and Rstudio (Version 4.3.2). A two-sided P-value < 0.05 was considered to indicate statistical significance in the present study. Results General characteristics of participants The baseline clinical and demographic characteristics of participants grouped by AIP quartile were presented in Table 1. The average age of participants at baseline was 58.04±8.75 years, with 4742 (54.3%) being female. Participants in higher AIP quartiles tended to be younger, female, married, and better-educated compared to those in the lowest quartile. Additionally, they had lower proportions of rural residents, current smokers, and current alcohol consumers. The prevalence of hypertension, diabetes, dyslipidemia, and heart disease was higher among those in higher AIP quartiles. Moreover, SBP, DBP, heart rate, BMI, FPG, HbA1c, TC, TG, and LDL levels were elevated, while HDL-C levels were lower in these groups. Table 1. Baseline characteristics of participants categorized by AIP quartiles. Characteristics Total Quartiles of AIP P value Q1 Q 2 Q 3 Q 4 No. of participants 8727 2182 2182 2182 2181 Age, years 58.04±8.75 58.42±8.95 58.18±8.91 57.93±8.62 57.63±8.49 0.021 Female, n (%) 4742(54.3) 1081(49.5) 1208(55.4) 1252(57.4) 1201(55.1) <0.001 SBP, mmHg 129.40±20.01 126.21±19.35 127.75±19.52 131.07±20.53 132.58±20.01 <0.001 DBP, mmHg 75.46±11.37 73.30±11.23 74.40±10.93 76.43±11.46 77.73±11.33 <0.001 Heart rate, bpm 71.95±9.84 70.85±9.73 71.50±9.83 72.22±9.72 73.22±9.92 <0.001 BMI, kg/m2 23.52±3.35 21.98±2.89 23.00±3.17 24.11±3.33 25.00±3.22 <0.001 Rural residence, n (%) 5755(65.9) 1596(73.1) 1486(68.1) 1380(63.2) 1293(59.3) <0.001 Education, n (%) 0.007 Junior high school and below 7839(89.8) 1996(91.5) 1971(90.3) 1956(89.6) 1916(87.8) Senior high school 788(9.0) 169(7.7) 186(8.5) 201(9.2) 232(10.6) Tertiary 100(1.1) 17(0.8) 25(1.1) 25(1.1) 33(1.5) Marital status, n (%) 0.02 Married 7337(84.1) 1815(83.2) 1814(83.1) 1829(83.8) 1879(86.2) Others 1390(15.9) 367(16.8) 368(16.9) 353(16.2) 302(13.8) Smoking, n (%) Never 5400(61.9) 1280(58.7) 1363(62.5) 1392(63.8) 1365(62.6) 0.001 Former 793(9.1) 194(8.9) 181(8.3) 204(9.3) 214(9.8) Current 2534(29.0) 708(32.4) 638(29.2) 586(26.9) 602(27.6) Drinking, n (%) <0.001 Never 5143(58.9) 1155(52.9) 1310(60.0) 1332(61.0) 1346(61.7) Former 668(7.7) 137(6.3) 171(7.9) 207(9.5) 153(7.0) Current 2916(33.4) 890(40.8) 701(32.1) 643(29.5) 682(31.3) Hypertension, n (%) 3377(38.7) 664(30.4) 735(33.7) 931(42.7) 1047(48.0) <0.001 Diabetes, n (%) 1216(13.9) 155(7.1) 217(9.9) 282(12.9) 562(25.8) <0.001 Dyslipidemia, n (%) 4230(48.5) 314(14.4) 536(24.6) 1200(55.0) 2180(100.0) <0.001 Heart Disease, n (%) 1087(12.5) 219(10.0) 227(10.4) 298(13.7) 343(15.7) <0.001 FPG, mg/dl 102.42(94.32,113.04) 99.36(91.98,107.82) 100.44(93.60,109.08) 101.88(94.50, 111.78) 109.44(99.54, 126.09) <0.001 HbA1c, % 5.1(4.9,5.4) 5.1(4.8,5.4) 5.1 (4.9,5.4) 5.1 (4.9,5.4) 5.2(4.9, 5.6) <0.001 TC, mg/dl 190.98(167.78,215.34) 185.18(165.08,208.38) 187.89(165.46,211.08) 192.91(168.94, 216.88) 197.55(172.81, 225.39) <0.001 TG, mg/dl 106.20(75.23,156.65) 61.95(53.10,71.69) 90.27(78.77, 102.66) 126.56(109.74, 145.14) 210.63(172.58, 279.66) <0.001 HDL-C, mg/dl 49.48(40.21,59.92) 64.95(57.22,74.61) 53.35(47.55, 59.92) 46.01(40.98, 51.80) 36.34(31.31, 41.75) <0.001 LDL-C, mg/dl 114.43(93.94,137.24) 109.02(90.46,128.74) 117.53(97.81,139.56) 122.17(100.13, 144.30) 109.79(85.44, 135.31) <0.001 GMS, n (%) <0.001 NGR 3467(39.7) 1073(49.2) 994(45.6) 876(40.1) 524(24.0) Pre-DM 4044(46.4) 954(43.7) 971(44.5) 1024(46.9) 1095(50.2) DM 1216(13.9) 155(7.1) 217(9.9) 282(12.9) 562(25.8) Data were presented as mean±SD, median and interquartile range, or as n (%) Abbreviations: AIP, Atherogenic index of plasma; SBP, Systolic blood pressure; DBP, Diastolic blood pressure; BMI, Body mass index; FPG, Fasting plasma glucose; HbA1c, Hemoglobin A1c; TC, Total cholesterol; TG, Triglyceride; HDL-C, High-density lipoprotein cholesterol; LDL-C, Low-density lipoprotein cholesterol; GMS, glucose metabolic states; NGR, Normal glucose regulation; Pre-DM, Prediabetes mellitus; DM, Diabetes mellitus; Predictive value of baseline AIP for first stroke events During a median follow-up period of 9 years, 734 (8.4%) participants experienced their first stroke. According to the AIP quartiles, the incidences of stroke, from Q1 to Q4, were 5.65, 7.99, 10.18, 13.55 per 1,000 person-years, respectively. Analysis of the Kaplan–Meier cumulative incidence curve revealed a gradual increase in stroke events from the Q1 to Q4 groups, with statistically significant differences observed (Fig 2A. log-rank test P<0.0001). The Cox proportional hazard models confirmed a significant relationship between baseline AIP levels and new‐onset stroke. Baseline AIP was analyzed as both a continuous variable and as a categorical variable (quartiles). Following adjustment for potential confounding factors, per 1-unit increase in baseline AIP was associated with a 90% higher risk of stroke in Model 3 (HR 1.90, 95% CI 1.52–2.36). Furthermore, the risk of stroke showed an increasing trend across quartiles of AIP in Model 3 (HR 1.34, 95%CI 1.05-1.71 for Q2; HR 1.52, 95%CI 1.19-1.93 for Q3; HR 1.84, 95%CI 1.45-2.34 for Q4; p -trend 0.001, Table 2). Multivariable-adjusted RCS analysis also demonstrated a significant dose-response relationship between the AIP as a continuous variable and the risk of stroke (P for overall trend<0.001; P for nonlinear=0.2551)(Fig. 3A). Associations between AIP and stroke regulated by individual glucose metabolic states During the follow-up period, 219 (6.3%) participants with NGR, 352 (8.7%) participants with Pre-DM and 163 (13.4%) participants with DM were detected with the first stroke. The Kaplan–Meier curves (Fig. 2B–D) showed a significant difference in the cumulative incidence of stroke among Pre-DM and DM across the four AIP groups (P<0.0001), while no significant difference was observed for NGR (P=0.57). The results presented in Table 3 indicated that, in comparison to Q1, other AIP groups showed a significant association with an increased risk of stroke in individuals with Pre-DM and DM in Model 3. Specifically, for individuals with Pre-DM, HR values were 1.49 (95% CI 1.03-2.16) for Q2, 1.80 (95% CI 1.26-2.57) for Q3, and 2.27 (95% CI 1.60-3.23) for Q4, with a p-trend of 0.001. In individuals with DM, HR values were 3.08 (95% CI 1.16-8.20) for Q2, 3.95 (95% CI 1.54-10.12) for Q3, and 4.58 (95% CI 1.83-11.47) for Q4, with a p-trend of 0.001. However, no significant differences were found among AIP groups in individuals with NGR in the three Cox models (all p-values > 0.05). The RCS analysis showed a notable increase in the risk of stroke in individuals with Pre-DM and DM as baseline AIP rises, demonstrating a linear relationship (Pre-DM: P for nonlinear= 0.1193; DM: P for nonlinear=0.3121) (Fig. 3C–D). Conversely, the analysis did not reveal a significant dose-response correlation between AIP and the risk of stroke in individuals with NGR (Fig. 3B). Together, these results provided valuable insights into the correlation between elevated AIP levels and the risk of stroke in individuals with abnormal glucose metabolism. Subgroup analysis To further explore the association between baseline AIP and first stroke events, we performed a subgroup analysis stratified by potential risk factors. As shown in Table 4, elevated AIP levels were associated with a higher incidence of stroke, which was consistent across different subgroups including age, gender, BMI, residence, and hypertension. Among individuals with Pre-DM and DM, increased AIP levels was linked to a higher stroke risk, whereas this association was not observed in the NGR groups. Significant interactions were noted between AIP and BMI (P value for interaction=0.011) as well as between AIP and glucose metabolic status (P value for interaction=0.031). However, no significant interactions were detected between AIP and other variables (all P values for interaction> 0.05). Table 2 The HR (95% CI) of stroke according to AIP in the three Models Categories Event, n(%) Model1 Model2 Model3 HR (95% CI) P value HR (95% CI) P value HR (95% CI) P value Continuous variable per unit 734(8.4) 2.65(2.19-3.20) <0.001 2.19(1.78-2.69) <0.001 1.90(1.52-2.36) <0.001 Quartile Q1 111(5.1) Ref. Ref. Ref. Q2 157(7.2) 1.43(1.12-1.82) 0.004 1.35(1.05-1.72) 0.017 1.34(1.05-1.71) 0.019 Q3 200(9.2) 1.83(1.45-2.31) <0.001 1.58(1.24-2.01) <0.001 1.52(1.19-1.93) 0.001 Q4 266(12.2) 2.49(1.99-3.10) <0.001 2.05(1.62-2.58) <0.001 1.84(1.45-2.34) <0.001 Model1: unadjusted Model 2: adjusted for age, gender, marital status, drinking, smoking, residence, SBP, DBP, BMI Model 3: Model 2+adjusted for hypertension, heart disease, TC, FPG, HbA1c Table 3 Association between AIP and the risk of stroke according to glucose metabolic states Categories Event, n(%) Model1 Model2 Model3 HR (95% CI) P value HR (95% CI) P value HR (95% CI) P value NGR Continuous variable per unit 219(6.3) 1.48(0.93-2.36) 0.102 1.10(0.67-1.81) 0.696 1.01(0.61-1.67) 0.969 Quartile Q1 59(5.5) Ref. Ref. Ref. Q2 64(6.4) 1.18(0.83-1.68) 0.37 1.08(0.75-1.54) 0.681 1.06(0.74-1.52) 0.752 Q3 59(6.7) 1.23(0.86-1.77) 0.26 1.07(0.74-1.56) 0.721 1.03(0.71-1.50) 0.873 Q4 37(7.1) 1.30(0.86-1.96) 0.22 1.03(0.67-1.58) 0.907 0.96(0.62-1.48) 0.849 Pre-DM Continuous variable per unit 352(8.7) 2.73(2.02-3.70) <0.001 2.44(1.77-3.37) <0.001 2.37(1.70-3.30) <0.001 Quartile Q1 47(4.9) Ref. Ref. Ref. Q2 71(7.3) 1.50(1.04-2.17) 0.031 1.48(1.02-2.14) 0.040 1.49(1.03-2.16) 0.036 Q3 100(9.8) 2.03(1.43-2.86) <0.001 1.82(1.27-2.60) 0.001 1.80(1.26-2.57) 0.001 Q4 134(12.2) 2.57(1.85-3.59) <0.001 2.35(1.66-3.33) <0.001 2.27(1.60-3.23) <0.001 DM Continuous variable per unit 163(13.4) 2.16(1.56-3.00) <0.001 2.04(1.44-2.90) <0.001 2.00(1.36-2.93) <0.001 Quartile Q1 5(3.2) Ref. Ref. Ref. Q2 22(10.1) 3.22(1.22-8.51) 0.018 3.03(1.14-8.05) 0.026 3.08(1.16-8.20) 0.024 Q3 41(14.5) 4.71(1.86-11.93) 0.001 4.18(1.63-10.67) 0.003 3.95(1.54-10.12) 0.004 Q4 95(16.9) 5.62(2.29-13.81) <0.001 4.87(1.95-12.16) 0.001 4.58(1.83-11.47) 0.001 Model1: unadjusted Model 2: adjusted for age, gender, marital status, drinking, smoking, residence, SBP, DBP, BMI Model 3: Model 2+adjusted for hypertension, heart disease, TC, FPG, HbA1c Table 4 Subgroup and interaction analyses of the association between AIP and stroke Quartiles of AIP P for interaction Q1 Q2 Q3 Q4 Age 0.268 <60years (Case/Total） 1 (Ref.) 40/1282 1.55(1.05-2.31) 66/1301 1.92(1.32-2.81) 95/1314 2.30(1.58-3.34) 140/1381 ≥60years (Case/Total） 1 (Ref.) 71/900 1.23(0.90-1.68) 91/881 1.27(0.93-1.74) 105/868 1.57(1.15-2.16) 126/800 Gender 0.807 Female (Case/Total） 1 (Ref.) 50/1081 1.38(0.97-1.97) 82/1208 1.45(1.03-2.05) 106/1252 1.88(1.34-2.64) 143/1201 Male (Case/Total） 1 (Ref.) 61/1101 1.28(0.91-1.81) 75/974 1.58(1.13-2.21) 94/930 1.79(1.27-2.52) 123/980 BMI 0.011 <24kg/m2 (Case/Total） 1 (Ref.) 91/1749 1.22(0.91-1.64) 92/1447 1.14(0.83-1.56) 70/1095 1.99(1.47-2.69) 99/834 ≥24kg/m2 (Case/Total） 1 (Ref.) 20/433 1.75(1.06-2.89) 65/735 2.14(1.33-3.45) 130/1087 2.08(1.30-3.34) 167/1347 Residence 0.6533 Rural (Case/Total） 1 (Ref.) 79/1596 1.46(1.09-1.95) 114/1486 1.67(1.25-2.23) 133/1380 2.03(1.52-2.72) 161/1293 Urban (Case/Total） 1 (Ref.) 32/586 1.06(0.67-1.68) 43/696 1.19(0.77-1.84) 67/802 1.53(1.00-2.33) 105/888 Hypertension 0.300 Yes (Case/Total） 1 (Ref.) 56/664 1.20(0.85-1.70) 77/735 1.60(1.16-2.21) 135/931 1.76(1.27-2.42) 175/1047 No (Case/Total） 1 (Ref.) 55/1518 1.53(1.08-2.17) 80/1447 1.35(0.93-1.96) 65/1251 1.99(1.38-2.86) 91/1134 GMS 0.031 NGR (Case/Total） 1 (Ref.) 59/1073 1.06(0.74-1.52) 64/994 1.03(0.71-1.50) 59/876 0.96(0.62-1.48) 37/524 Pre-DM (Case/Total） 1 (Ref.) 47/954 1.49(1.03-2.16) 71/971 1.80(1.26-2.57) 100/1024 2.27(1.60-3.23) 134/1095 DM (Case/Total） 1 (Ref.) 5/155 3.08(1.16-8.20) 22/217 3.95(1.54-10.12) 41/282 4.58(1.83-11.47) 95/562 Model adjusted for age, gender, marital status, drinking, smoking, residence, SBP, DBP, BMI, hypertension, heart disease, TC, FPG, HbA1c Discussion In the large national longitudinal survey cohort of middle-aged and elderly individuals, a significant correlation was elaborated between higher baseline AIP levels and an increased risk of a new-onset stroke. This association was particularly prominent in those with abnormal glucose metabolism, including Pre-DM and DM. The study suggested that baseline AIP might be a dependable biomarker for stratifying stroke risk. Maintaining a low AIP level might be beneficial for the primary prevention of stroke in individuals with abnormal glucose metabolism. Dyslipidaemia and insulin resistance are features of the metabolic syndrome, both of which contribute to the risk of stroke(18-20). The AIP is widely recognized as a reliable marker of dyslipidemia and atherosclerosis. Several studies have demonstrated that both baseline and cumulative AIP exposure are linked to cardiovascular diseases, particularly coronary artery disease(17, 21-23). Notably, the impact of AIP on cardiovascular diseases may differ depending on an individual’s glucose metabolic states. Min et al. have found that individuals with abnormal glucose metabolism and higher AIP levels may have a greater risk of developing cardiovascular diseases(24). In addition, higher levels of AIP have been shown to be positively correlated with the risk of hypertension and non-alcoholic fatty liver disease, with this relationship potentially influenced by the glucose metabolic states(25-27). Interestingly, a cross-sectional study has shown a strong association between elevated AIP levels and an increased risk of insulin resistance and the onset of type 2 diabetes(5). Recent research has explored the association between AIP and cerebrovascular disease, revealing that higher AIP levels are correlated with a higher incidence of atherosclerotic stenosis in the carotid and intracranial arteries(9, 28). In the general population, increased baseline and cumulative AIP levels are associated with a greater risk of ischemic stroke(8, 10). To the best of our knowledge, this study is the first to demonstrate the significant predictive value of baseline AIP level for stroke in individuals with glycemic dysregulation. This study found that high baseline AIP levels were associated with new-onset stroke in individuals with Pre-DM and DM, which was consistent with previous reports that high AIP was associated with the risk and prognosis of stroke. Therefore, assessing AIP levels in middle-aged and elderly individuals with glycemic dysregulation would have clinical significance. Further studies are warranted to investigate the baseline levels of AIP , which is able to predict and identify stroke risk. Lowering LDL-cholesterol has been traditionally believed to be beneficial in preventing overall stroke, with statins being commonly used for this purpose(29). However, a multicenter clinical trial shows that patients with high triglycerides levels faced a high risk of ischemic stroke, despite statin therapy(30). Recent data also advocate for lowering triglycerides as a strategy to prevent stroke(31). Therefore, it is plausible to consider that lowering triglycerides, which is equivalent to lowering AIP levels, contributes to stroke prevention. Although the mechanisms underlying the relationship between AIP and stroke remain unclear, several possible explanations have been proposed. Firstly, triglycerides appear to be related to vascular inflammation and subclinical atherosclerosis. Raised serum triglycerides can potentiate inflammatory responses in vascular endothelial cells and vascular smooth muscle cells, especially in diabetic patients(32, 33). Emerging evidence indicates that triglycerides-rich lipoproteins like chylomicrons and very low-density lipoproteins may play a role in atherosclerotic lesion formation(34, 35). On the other hand, HDL particles exhibit various vasoprotective properties, such as reducing cellular death, dampening inflammatory response, and shielding against pathological oxidation(36). Therefore, it can be inferred that as AIP values increase, higher levels of triglycerides lead to more significant damage to vascular structure and function, while lower levels of HDL offer less protection to the vasculature. Secondly, the level of AIP has been shown to be closely associated with traditional risk factors for stroke, including BMI, hypertension, diabetes, dyslipidemia, and heart disease. The results of the present study are consistent with these studies. Elevated AIP levels may interact with other cerebrovascular risk factors and potentially exacerbate the progression of stroke. However, further research is necessary to fully understand the underlying mechanisms. Strengths and limitations This is the largest population-based investigation into the association between AIP and stroke among middle-aged and elderly individuals with glycemic dysregulation. The data were obtained from a high-quality, nationally representative longitudinal survey of the middle-aged and elderly population across various regions of China, including urban and rural areas. In order to obtain robust results, we included potential confounders to exclude interference in the results. With nearly a decade of follow-up, our analysis indicates that baseline AIP is a reliable predictor of stroke in middle-aged and elderly individuals with dysglycaemia. Moreover, since standard assay for TG and HDL-C are widely used in clinical practice and it is straightforward to calculate AIP from TG and HDL-C, it is sensible to recommend AIP as an efficient and convenient indicator for assessing the risk of stroke. However, there are several limitations of the study that require consideration. Firstly, while almost all participants adhered to standard phlebotomy protocol, a minority did not adhere to the fasting requirement of up to 8 hours prior to blood collection. This deviation may have impacted the accuracy of the calculated AIP values. Secondly, the study was based on middle-aged and elderly Chinese, and further validation in other ethnic and age groups is needed. Thirdly, this study focused on the impact of baseline AIP level and did not examine the longitudinal changes in AIP during the follow-up period. Forthly, endpoint events for this study were determined based on questionnaire interviews with participants and end-point assessments could not be validated by hospital records. Fifthly, due to a lack of data on stroke subtypes in CHARLS, this study was unable to assess the effect of AIP on ischemic or hemorrhagic stroke, respectively. Finally, despite efforts to control for confounding variables, there is a possibility that some confounders were not accounted for. Further investigations are needed to verify our findings in other large cohort studies. Conclusions In the pursuit of primary prevention strategies to reduce stroke incidence, this longitudinal prospective study found that baseline high AIP levels in individuals with glycemic dysregulation might indicate subgroups at a higher risk of developing stroke, particularly among individuals under 60 years old with a BMI≥24kg/m 2 residing in rural areas. However, the levels of AIP in middle-aged and older adults without dysglycaemia do not affect the occurrence of stroke. Abbreviations AIP: Atherogenic index of plasma; IR: Insulin resistance; GMS: Glucose metabolic states; NGR: Normal glucose regulation; Pre-DM: Prediabetes mellitus; DM: Diabetes mellitus; SBP: Systolic blood pressure; DBP: Diastolic blood pressure; BMI: Body mass index; FPG: Fasting blood glucose; HbA1c: Glycosylated hemoglobin; TC: Total cholesterol; TG: Triglyceride; HDL-C: High-density lipoprotein cholesterol; LDL-C: Low-density lipoprotein cholesterol. Declarations Ethics approval and consent to participate The study was approved by the Biomedical Ethics Review Committee of Peking University (IRB00001052-1101). The participants provided their written informed consent to participate in this study. Consent for publication Not applicable. Availability of data and materials Datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request. Competing interests The authors declare no competing interests. Funding This research was supported by the Natural Science Foundation of Jiangsu Province (BK20211005 to J.J., BK20231120 to X.Z), the Key Research and Development Program of Jiangsu Province of China (BE2020620 to Y.X.), the STI2030-Major Projects-2022ZD0211800, the Jiangsu Province Key Medical Discipline (ZDXK202216 to Y.X.) and Nanjing Medical Science and Technology Development Foundation (ZKX22025 to X.Z.). Authors' contributions JJ, XZ and LQ were involved in the design of this study; LQ and SF contributed to manuscript writing; SX and YP were reponsible for data collection and data management; LQ, JJ, and ZL contributed to the statistical analysis; JJ, XZ and YX participated in data review and manuscript revision. All authors have reviewed and approved the final manuscript. Acknowledgements The authors would like to thank all participants of the CHALRS. Authors' information 1 Department of Neurology, Nanjing Drum Tower Hospital, Clinical College of Nanjing Medical University, Nanjing 210008, China. 2 Department of Neurology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing 210008, China. 3 State Key Laboratory of Pharmaceutical Biotechnology and Institute of Translational Medicine for Brain Critical Diseases, Nanjing University, Nanjing 210008, China. 4 Jiangsu Key Laboratory for Molecular Medicine, Medical School of Nanjing University, Nanjing 210008, China. 5 Nanjing Neurology Clinical Medical Center, Nanjing 210008, China. References Owolabi MO, Thrift AG, Mahal A, Ishida M, Martins S, Johnson WD, et al. Primary stroke prevention worldwide: translating evidence into action. The Lancet Public health. 2022;7(1):e74-e85. Wadström BN, Wulff AB, Pedersen KM, Jensen GB, Nordestgaard BG. Elevated remnant cholesterol increases the risk of peripheral artery disease, myocardial infarction, and ischaemic stroke: a cohort-based study. European heart journal. 2022;43(34):3258-69. Cai W, Xu J, Wu X, Chen Z, Zeng L, Song X, et al. Association between triglyceride-glucose index and all-cause mortality in critically ill patients with ischemic stroke: analysis of the MIMIC-IV database. Cardiovascular diabetology. 2023;22(1):138. Ago T, Matsuo R, Hata J, Wakisaka Y, Kuroda J, Kitazono T, et al. Insulin resistance and clinical outcomes after acute ischemic stroke. Neurology. 2018;90(17):e1470-e7. Yin B, Wu Z, Xia Y, Xiao S, Chen L, Li Y. Non-linear association of atherogenic index of plasma with insulin resistance and type 2 diabetes: a cross-sectional study. Cardiovascular diabetology. 2023;22(1):157. Shi Y, Wen M. Sex-specific differences in the effect of the atherogenic index of plasma on prediabetes and diabetes in the NHANES 2011-2018 population. Cardiovascular diabetology. 2023;22(1):19. Tan MH, Johns D, Glazer NB. Pioglitazone reduces atherogenic index of plasma in patients with type 2 diabetes. Clinical chemistry. 2004;50(7):1184-8. Zhang Y, Chen S, Tian X, Xu Q, Xia X, Zhang X, et al. Elevated atherogenic index of plasma associated with stroke risk in general Chinese. Endocrine. 2024. Yu S, Yan L, Yan J, Sun X, Fan M, Liu H, et al. The predictive value of nontraditional lipid parameters for intracranial and extracranial atherosclerotic stenosis: a hospital-based observational study in China. Lipids in health and disease. 2023;22(1):16. Zheng H, Wu K, Wu W, Chen G, Chen Z, Cai Z, et al. Relationship between the cumulative exposure to atherogenic index of plasma and ischemic stroke: a retrospective cohort study. Cardiovascular diabetology. 2023;22(1):313. Liu H, Liu K, Pei L, Li S, Zhao J, Zhang K, et al. Atherogenic Index of Plasma Predicts Outcomes in Acute Ischemic Stroke. Frontiers in neurology. 2021;12:741754. Zhao Y, Hu Y, Smith JP, Strauss J, Yang G. Cohort profile: the China Health and Retirement Longitudinal Study (CHARLS). International journal of epidemiology. 2014;43(1):61-8. Chen X, Crimmins E, Hu PP, Kim JK, Meng Q, Strauss J, et al. Venous Blood-Based Biomarkers in the China Health and Retirement Longitudinal Study: Rationale, Design, and Results From the 2015 Wave. American journal of epidemiology. 2019;188(11):1871-7. Williams B, Mancia G, Spiering W, Agabiti Rosei E, Azizi M, Burnier M, et al. 2018 ESC/ESH Guidelines for the management of arterial hypertension. European heart journal. 2018;39(33):3021-104. ElSayed NA, Aleppo G, Aroda VR, Bannuru RR, Brown FM, Bruemmer D, et al. 2. Classification and Diagnosis of Diabetes: Standards of Care in Diabetes-2023. Diabetes care. 2023;46(Suppl 1):S19-s40. Mach F, Baigent C, Catapano AL, Koskinas KC, Casula M, Badimon L, et al. 2019 ESC/EAS Guidelines for the management of dyslipidaemias: lipid modification to reduce cardiovascular risk. European heart journal. 2020;41(1):111-88. Fernández-Macías JC, Ochoa-Martínez AC, Varela-Silva JA, Pérez-Maldonado IN. Atherogenic Index of Plasma: Novel Predictive Biomarker for Cardiovascular Illnesses. Archives of medical research. 2019;50(5):285-94. DeBoer MD, Filipp SL, Sims M, Musani SK, Gurka MJ. Risk of Ischemic Stroke Increases Over the Spectrum of Metabolic Syndrome Severity. Stroke. 2020;51(8):2548-52. He Q, Wang W, Li H, Xiong Y, Tao C, Ma L, et al. Genetic insights into the risk of metabolic syndrome and its components on stroke and its subtypes: Bidirectional Mendelian randomization. Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism. 2023;43(2_suppl):126-37. Zhang F, Liu L, Zhang C, Ji S, Mei Z, Li T. Association of Metabolic Syndrome and Its Components With Risk of Stroke Recurrence and Mortality: A Meta-analysis. Neurology. 2021;97(7):e695-e705. Kim SH, Cho YK, Kim YJ, Jung CH, Lee WJ, Park JY, et al. Association of the atherogenic index of plasma with cardiovascular risk beyond the traditional risk factors: a nationwide population-based cohort study. Cardiovascular diabetology. 2022;21(1):81. Alifu J, Xiang L, Zhang W, Qi P, Chen H, Liu L, et al. Association between the atherogenic index of plasma and adverse long-term prognosis in patients diagnosed with chronic coronary syndrome. Cardiovascular diabetology. 2023;22(1):255. Wang Y, Wang S, Sun S, Li F, Zhao W, Yang H, et al. The predictive value of atherogenic index of plasma for cardiovascular outcomes in patients with acute coronary syndrome undergoing percutaneous coronary intervention with LDL-C below 1.8mmol/L. Cardiovascular diabetology. 2023;22(1):150. Min Q, Wu Z, Yao J, Wang S, Duan L, Liu S, et al. Association between atherogenic index of plasma control level and incident cardiovascular disease in middle-aged and elderly Chinese individuals with abnormal glucose metabolism. Cardiovascular diabetology. 2024;23(1):54. Li K, Li J, Cheng X, Wang J, Li J. Association between the atherogenic index of plasma and new-onset non-alcoholic fatty liver disease in non-obese participants. Frontiers in endocrinology. 2022;13:969783. Samimi S, Rajabzadeh S, Rabizadeh S, Nakhjavani M, Nakhaei P, Avanaki FA, et al. Atherogenic index of plasma is an independent predictor of metabolic-associated fatty liver disease in patients with type 2 diabetes. European journal of medical research. 2022;27(1):112. Tan M, Zhang Y, Jin L, Wang Y, Cui W, Nasifu L, et al. Association between atherogenic index of plasma and prehypertension or hypertension among normoglycemia subjects in a Japan population: a cross-sectional study. Lipids in health and disease. 2023;22(1):87. Huang Q, Liu Z, Wei M, Huang Q, Feng J, Liu Z, et al. The atherogenic index of plasma and carotid atherosclerosis in a community population: a population-based cohort study in China. Cardiovascular diabetology. 2023;22(1):125. Sun L, Clarke R, Bennett D, Guo Y, Walters RG, Hill M, et al. Causal associations of blood lipids with risk of ischemic stroke and intracerebral hemorrhage in Chinese adults. Nature medicine. 2019;25(4):569-74. Bhatt DL, Steg PG, Miller M, Brinton EA, Jacobson TA, Ketchum SB, et al. Cardiovascular Risk Reduction with Icosapent Ethyl for Hypertriglyceridemia. The New England journal of medicine. 2019;380(1):11-22. Aradine E, Hou Y, Cronin CA, Chaturvedi S. Current Status of Dyslipidemia Treatment for Stroke Prevention. Current neurology and neuroscience reports. 2020;20(8):31. Wang YI, Schulze J, Raymond N, Tomita T, Tam K, Simon SI, et al. Endothelial inflammation correlates with subject triglycerides and waist size after a high-fat meal. American journal of physiology Heart and circulatory physiology. 2011;300(3):H784-91. Gordillo-Moscoso A, Ruiz E, Carnero M, Reguillo F, Rodriguez E, Tejerina T, et al. Relationship between serum levels of triglycerides and vascular inflammation, measured as COX-2, in arteries from diabetic patients: a translational study. Lipids in health and disease. 2013;12:62. Öörni K, Lehti S, Sjövall P, Kovanen PT. Triglyceride-Rich Lipoproteins as a Source of Proinflammatory Lipids in the Arterial Wall. Current medicinal chemistry. 2019;26(9):1701-10. Raposeiras-Roubin S, Rosselló X, Oliva B, Fernández-Friera L, Mendiguren JM, Andrés V, et al. Triglycerides and Residual Atherosclerotic Risk. Journal of the American College of Cardiology. 2021;77(24):3031-41. Kontush A. HDL-mediated mechanisms of protection in cardiovascular disease. Cardiovascular research. 2014;103(3):341-9. Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterial.docx Cite Share Download PDF Status: Published Journal Publication published 20 Jun, 2024 Read the published version in Cardiovascular Diabetology → Version 1 posted Editorial decision: Revision requested 08 May, 2024 Reviews received at journal 08 May, 2024 Reviews received at journal 06 May, 2024 Reviews received at journal 27 Apr, 2024 Reviewers agreed at journal 17 Apr, 2024 Reviews received at journal 17 Apr, 2024 Reviewers agreed at journal 16 Apr, 2024 Reviewers agreed at journal 16 Apr, 2024 Reviewers agreed at journal 16 Apr, 2024 Reviewers invited by journal 16 Apr, 2024 Editor assigned by journal 16 Apr, 2024 Submission checks completed at journal 15 Apr, 2024 First submitted to journal 13 Apr, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4261103","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":292340425,"identity":"d0ee1d5d-5d20-4050-b0b9-31a73be890ef","order_by":0,"name":"Longjie qu","email":"","orcid":"","institution":"Nanjing Drum Tower Hospital, Clinical College of Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Longjie","middleName":"","lastName":"qu","suffix":""},{"id":292340426,"identity":"d6164530-f60e-4fd2-9413-863a1144f9cf","order_by":1,"name":"Shuang Fang","email":"","orcid":"","institution":"Nanjing Drum Tower 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University","correspondingAuthor":true,"prefix":"","firstName":"Jiali","middleName":"","lastName":"Jin","suffix":""}],"badges":[],"createdAt":"2024-04-13 08:46:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4261103/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4261103/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12933-024-02314-y","type":"published","date":"2024-06-21T00:30:14+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":55249796,"identity":"661ba5c7-8e18-43d6-a5e8-11e0f8a62416","added_by":"auto","created_at":"2024-04-24 17:31:05","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":595171,"visible":true,"origin":"","legend":"\u003cp\u003eThe flowchart of study participants\u003c/p\u003e","description":"","filename":"OnlineFig.1.png","url":"https://assets-eu.researchsquare.com/files/rs-4261103/v1/0cb3b81db758673ed0317cb5.png"},{"id":55249797,"identity":"f427c707-c838-4e0f-a01f-cc2cfc23c481","added_by":"auto","created_at":"2024-04-24 17:31:05","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":364103,"visible":true,"origin":"","legend":"\u003cp\u003eThe Kaplan–Meier analysis for stroke was based on AIP quartiles for total participants (A), participants with NGR (B), participants with Pre-DM (C), and participants with DM (D)\u003c/p\u003e","description":"","filename":"OnlineFig.2.png","url":"https://assets-eu.researchsquare.com/files/rs-4261103/v1/c86334e00c80bce00b89b43c.png"},{"id":55249798,"identity":"93c5e519-abec-4c18-8446-734d754d0daa","added_by":"auto","created_at":"2024-04-24 17:31:05","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":290222,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation of AIP and the risk of stroke using a multivariable-adjusted restricted cubic spines model. Restricted cubic spline analysis has four knots at the 5th, 35th, 65th, and 95th percentiles of AIP. A, total participants; B, participants with NGR; C, participants with Pre-DM. D, participants with DM\u003c/p\u003e","description":"","filename":"OnlineFig.3.png","url":"https://assets-eu.researchsquare.com/files/rs-4261103/v1/2b252324f3eacfe27c29e02b.png"},{"id":58842029,"identity":"1542ae60-285f-41a1-95df-eb26405f6814","added_by":"auto","created_at":"2024-06-22 00:30:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2956518,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4261103/v1/c2862392-4c4f-4c86-a0a2-c415df771a50.pdf"},{"id":55249795,"identity":"f5676029-eab2-42b3-9023-8eff7653778e","added_by":"auto","created_at":"2024-04-24 17:31:05","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":14788,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-4261103/v1/35b8592b8c7529ce00948163.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association between atherogenic index of plasma and new-onset stroke in individuals with different glucose metabolism status: insights from CHARLS","fulltext":[{"header":"Introduction","content":"\u003cp\u003eStroke is a major global public health burden with high morbidity and mortality. Despite diligent primary prevention efforts, the prevalence and incidence of stroke in China continue to exhibit an alarming increase. Consequently, it is imperative to develop low-cost and reproducible indicators that can hence early identification of high-risk individuals with stroke(1). Many studies have elucidated that metabolic disorders, including dyslipidemia and hyperglycemia, are significant risk factors for stroke. Several metabolic indicators, such as remnant cholesterol and TyG index, have been utilized to assess the risk and prognosis of stroke(2\u0026ndash;4), but the predictive accuracy is still limited. The atherogenic index of plasma (AIP), a logarithmically transformed ratio of fasting triglyceride to fasting high-density lipoprotein cholesterol, is a sensitive marker of lipoprotein profiles primarily reflecting plasma lipid levels. Recent researches suggest that rising AIP can indicate the severity of insulin resistance and is closely related to the development of insulin resistance and type 2 diabetes(5\u0026ndash;7). As a robust biomarker of dyslipidemia, AIP has been considered to be a powerful independent predictor of adverse cardiovascular and cerebrovascular events. AIP is potentially served as a significant predictor of intracranial arterial stenosis, the risk of ischemic stroke, and poor stroke outcomes(8\u0026ndash;11). On the other hand, AIP serves as a reliable indicator of insulin resistance, which indicates that it may differentiate stroke risk in individuals with abnormal glucose metabolism. However, the relationship between AIP and stroke based on an individual's glucose metabolism status has been poorly investigated. Therefore, prospective cohort studies with a large sample size are warranted to clarify the relationship.\u003c/p\u003e \u003cp\u003eIn the present study, we used data from the China Health and Retirement Longitudinal Study ( CHARLS) to test the association between baseline AIP levels and stroke under different glucose metabolic states.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy participants\u003c/h2\u003e \u003cp\u003eThe CHARLS is a national prospective cohort study that commenced in 2011, focusing on individuals aged 45 years and older in China. The participants are traced once every 2\u0026ndash;3 years to identify their health status. To date, five rounds of follow-up surveys have been completed, with data collected in 2011, 2013, 2015, 2018, and 2020. The detailed research methods have been described previously(12). We initially enrolled 17708 participants in CHARLS wave 1. Among these people, 8981 were excluded for meeting the following exclusion criteria: (1) missing available data on AIP, fasting blood glucose (FPG), glycosylated hemoglobin (HbA1c) (n\u0026thinsp;=\u0026thinsp;6132), (2) age\u0026thinsp;\u0026lt;\u0026thinsp;45 years old or missing data on age (n\u0026thinsp;=\u0026thinsp;423), (3) personal cancer history (n\u0026thinsp;=\u0026thinsp;102), (4) history of stroke (n\u0026thinsp;=\u0026thinsp;345), (5) lack of data on stroke or lost to follow-up (n\u0026thinsp;=\u0026thinsp;1979). Finally, 8727 participants were divided into four groups according to the baseline AIP quartiles and were followed up until 2020 (Fig.\u0026nbsp;1). The CHARLS study received ethical approval from the Biomedical Ethics Review Committee of Peking University (IRB00001052-1101).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eData collection\u003c/h2\u003e \u003cp\u003eTrained interviewers collected the demographic information (such as age, sex, and marital status), health status and functioning (such as smoking, drinking, hypertension, and diabetes) of the participants with standard questionnaires. All participants, except those with an arm injury, were instructed to undergo three blood pressure measurements at a 45-second interval, and the average of three values was reported. Body weight and height were measured using standardized scales to the nearest 0.1 kg and 0.1 cm, respectively. Venous blood samples were collected from each participant by medically trained staff following a standard phlebotomy protocol and assayed for biochemical measurements. Regrettably, nearly 8% of the participants who gave blood reported that they fasted less than 8 hours before blood collection. The enzymatic colorimetric method was applied to measure FPG levels and serum lipid parameters, while HbA1c was measured using Boronate affinity HPLC(13).\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure\u0026nbsp;1\u003c/b\u003e The flowchart of study participants\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eDefinitions\u003c/h2\u003e \u003cp\u003eHypertension diagnosis was based on a self-reported physician-diagnosed, and/or any antihypertensive medication use, and/or an average systolic/diastolic blood pressure (SBP/DBP)\u0026thinsp;\u0026ge;\u0026thinsp;140/90 mmHg(14). Diabetes mellitus (DM) was defined as FPG\u0026thinsp;\u0026ge;\u0026thinsp;126mg/dl or HbA1c\u0026thinsp;\u0026ge;\u0026thinsp;6.5%, and/or a self-reported physician-diagnosed, and/or taking hypoglycemic agents. Prediabetes mellitus (Pre-DM) was characterized by an FPG of 100 to 125 mg/dL or an HbA1c of 5.7\u0026ndash;6.4%. Individuals who were without DM or Pre-DM were classified as having normal glucose regulation (NGR)(15). Dyslipidemia was diagnosed by a self-reported physician-diagnosed, and/or current use of lipid-lowering drugs, and/or total cholesterol (TC)\u0026thinsp;\u0026ge;\u0026thinsp;240 mg/dl, triglyceride (TG)\u0026thinsp;\u0026ge;\u0026thinsp;150 mg/dl, high-density lipoprotein cholesterol (HDL-C)\u0026thinsp;\u0026lt;\u0026thinsp;40 mg/dl, low-density lipoprotein cholesterol (LDL-C)\u0026thinsp;\u0026ge;\u0026thinsp;160 mg/dl(16). Cancer and heart disease were determined by participants' self-reported. Body mass index (BMI) was calculated as the following formula: weight/height\u003csup\u003e2\u003c/sup\u003e (kg/m\u003csup\u003e2\u003c/sup\u003e). The atherogenic index of plasma (AIP) was calculated as log (TG/HDL-C)(17).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eFollow up of endpoint events\u003c/h2\u003e \u003cp\u003eThe primary focus of the study was on the occurrence of first stroke, which included both cerebral infarction and cerebral hemorrhage. Self-reported stroke was assessed with the following questions: \u0026ldquo;Have you been diagnosed with stroke by a doctor\u0026rdquo;; \u0026ldquo;Have you been diagnosed with stroke by a doctor since the last follow-up visit?\u0026rdquo;; \u0026ldquo;Compared to when we interviewed you last time, is your stroke condition better, about the same as it was then, or worse?\u0026rdquo;. The time of stroke events was determined by participants' responses to questions: \u0026ldquo;When was the stroke first diagnosed or known by yourself?\u0026rdquo;; \u0026ldquo;When was your most recent stroke?\u0026rdquo;. All participants were followed up through five-wave interviews conducted from 2011 to either the occurrence of a stroke or 2020, whichever came first.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eNormally distributed continuous data were presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation and analyzed for statistical significance using a one-way ANOVA. Non-normally distributed continuous data were expressed as median and interquartile range and analyzed by the Kruskal\u0026ndash;Wallis test. Categorical data were described with counts and percentages and assessed using the chi-square test. Missing data of the participants included in this study were presented in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e, and the multiple imputations method was applied to impute the missing data, assuming that the data was randomly missing.\u003c/p\u003e \u003cp\u003eParticipants were divided into four groups according to the quartile level of the AIP: Quartile 1 (Q1), AIP\u0026thinsp;\u0026le;\u0026thinsp;0.122; Quartile 2 (Q2), 0.122\u0026thinsp;\u0026lt;\u0026thinsp;AIP\u0026thinsp;\u0026le;\u0026thinsp;0.329; Quartile 3 (Q3), 0.329\u0026thinsp;\u0026lt;\u0026thinsp;AIP\u0026thinsp;\u0026le;\u0026thinsp;0.562; Quartile 4 (Q4), AIP\u0026thinsp;\u0026gt;\u0026thinsp;0.562. Based on AIP grouping, the Kaplan\u0026ndash;Meier method was used to estimate the cumulative incidence of stroke and compared using the log-rank test. Cox proportional hazard regression analyses were conducted to investigate the relationship between baseline AIP and the occurrence of stroke and to calculate the hazard ratio (HR) and 95% confidence interval (CI). Before analyzing, the assumption of proportional hazards was visually assessed by calculating the Schoenfeld residuals. Three models were estimated: Model 1 applied an unadjusted model to estimate crude HR; Model 2 included adjustments for age, gender, marital status, drinking, smoking, residence, SBP, DBP, and BMI; Model 3 included adjustments for the variables in Model 2 as well as history of hypertension and heart disease, TC, FPG, HbA1c. The quartile 1 group was set as the reference in all models. In addition, restricted cubic splines (RCS) analysis based on multivariable-adjusted Cox regression was conducted to visualize the linear or nonlinear relationship between baseline AIP levels and the risk of stroke. Moreover, to determine the prognostic value of AIP for stroke in different glucose metabolic states, we analyzed participants with NGR, Pre-DM, and DM, respectively. Subgroup analyses were stratified by baseline age (\u0026lt;\u0026thinsp;60 and \u0026ge;\u0026thinsp;60 years), gender, BMI (\u0026lt;\u0026thinsp;24 and \u0026ge;\u0026thinsp;24 kg/m2), residence (rural and urban), hypertension, and glucose metabolic states (NGR, Pre-DM, and DM) to assess the consistency of the adverse effect of AIP on new-onset stroke.\u003c/p\u003e \u003cp\u003eAll statistical analyses were performed using IBM SPSS Statistics (Version 26) and Rstudio (Version 4.3.2). A two-sided P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered to indicate statistical significance in the present study.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eGeneral characteristics of participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe baseline clinical and demographic characteristics of participants grouped by AIP quartile were presented in Table 1. The average age of participants at baseline was 58.04\u0026plusmn;8.75 years, with 4742 (54.3%) being female. Participants in higher AIP quartiles tended to be younger, female, married, and better-educated compared to those in the lowest quartile. Additionally, they had lower proportions of rural residents, current smokers, and current alcohol consumers. The prevalence of hypertension, diabetes, dyslipidemia, and heart disease was higher among those in higher AIP quartiles. Moreover, SBP, DBP, heart rate, BMI, FPG, HbA1c, TC, TG, and LDL levels were elevated, while HDL-C levels were lower in these groups.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1. Baseline characteristics of participants categorized by AIP quartiles.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"558\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.043010752688172%\" valign=\"top\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.620071684587813%\" valign=\"top\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"54.121863799283155%\" colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eQuartiles of AIP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.21505376344086%\" valign=\"top\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.964285714285715%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003eQ 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003eQ 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003eQ 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.178571428571429%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.964285714285715%\" valign=\"top\"\u003e\n \u003cp\u003eNo. of participants\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e8727\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e2182\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e2182\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e2182\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e2181\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.178571428571429%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.964285714285715%\" valign=\"top\"\u003e\n \u003cp\u003eAge, years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e58.04\u0026plusmn;8.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e58.42\u0026plusmn;8.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e58.18\u0026plusmn;8.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e57.93\u0026plusmn;8.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e57.63\u0026plusmn;8.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.178571428571429%\" valign=\"top\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.964285714285715%\" valign=\"top\"\u003e\n \u003cp\u003eFemale, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e4742(54.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e1081(49.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e1208(55.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e1252(57.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e1201(55.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.178571428571429%\" 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 width=\"21.964285714285715%\" valign=\"top\"\u003e\n \u003cp\u003eSBP, mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e129.40\u0026plusmn;20.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e126.21\u0026plusmn;19.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e127.75\u0026plusmn;19.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e131.07\u0026plusmn;20.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e132.58\u0026plusmn;20.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.178571428571429%\" 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 width=\"21.964285714285715%\" valign=\"top\"\u003e\n \u003cp\u003eDBP, mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e75.46\u0026plusmn;11.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e73.30\u0026plusmn;11.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e74.40\u0026plusmn;10.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e76.43\u0026plusmn;11.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e77.73\u0026plusmn;11.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.178571428571429%\" 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 width=\"21.964285714285715%\" valign=\"top\"\u003e\n \u003cp\u003eHeart rate, bpm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e71.95\u0026plusmn;9.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e70.85\u0026plusmn;9.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e71.50\u0026plusmn;9.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e72.22\u0026plusmn;9.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e73.22\u0026plusmn;9.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.178571428571429%\" 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 width=\"21.964285714285715%\" valign=\"top\"\u003e\n \u003cp\u003eBMI, kg/m2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e23.52\u0026plusmn;3.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e21.98\u0026plusmn;2.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e23.00\u0026plusmn;3.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e24.11\u0026plusmn;3.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e25.00\u0026plusmn;3.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.178571428571429%\" 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 width=\"21.964285714285715%\" valign=\"top\"\u003e\n \u003cp\u003eRural residence, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e5755(65.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e1596(73.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e1486(68.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e1380(63.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e1293(59.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.178571428571429%\" 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 width=\"21.964285714285715%\" valign=\"top\"\u003e\n \u003cp\u003eEducation, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.178571428571429%\" valign=\"top\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.964285714285715%\" valign=\"top\"\u003e\n \u003cp\u003eJunior high school\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eand below\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e7839(89.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e1996(91.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e1971(90.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e1956(89.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e1916(87.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.178571428571429%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.964285714285715%\" valign=\"top\"\u003e\n \u003cp\u003eSenior high school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e788(9.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e169(7.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e186(8.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e201(9.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e232(10.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.178571428571429%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.964285714285715%\" valign=\"top\"\u003e\n \u003cp\u003eTertiary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e100(1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e17(0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e25(1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e25(1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e33(1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.178571428571429%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.964285714285715%\" valign=\"top\"\u003e\n \u003cp\u003eMarital status, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.178571428571429%\" valign=\"top\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.964285714285715%\" valign=\"top\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e7337(84.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e1815(83.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e1814(83.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e1829(83.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e1879(86.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.178571428571429%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.964285714285715%\" valign=\"top\"\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e1390(15.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e367(16.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e368(16.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e353(16.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e302(13.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.178571428571429%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.964285714285715%\" valign=\"top\"\u003e\n \u003cp\u003eSmoking, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.178571428571429%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.964285714285715%\" valign=\"top\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e5400(61.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e1280(58.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e1363(62.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e1392(63.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e1365(62.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.178571428571429%\" valign=\"top\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.964285714285715%\" valign=\"top\"\u003e\n \u003cp\u003eFormer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e793(9.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e194(8.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e181(8.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e204(9.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e214(9.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.178571428571429%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.964285714285715%\" valign=\"top\"\u003e\n \u003cp\u003eCurrent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e2534(29.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e708(32.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e638(29.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e586(26.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e602(27.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.178571428571429%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.964285714285715%\" valign=\"top\"\u003e\n \u003cp\u003eDrinking, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.178571428571429%\" 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 width=\"21.964285714285715%\" valign=\"top\"\u003e\n \u003cp\u003eNever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e5143(58.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e1155(52.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e1310(60.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e1332(61.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e1346(61.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.178571428571429%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.964285714285715%\" valign=\"top\"\u003e\n \u003cp\u003eFormer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e668(7.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e137(6.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e171(7.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e207(9.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e153(7.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.178571428571429%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.964285714285715%\" valign=\"top\"\u003e\n \u003cp\u003eCurrent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e2916(33.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e890(40.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e701(32.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e643(29.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e682(31.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.178571428571429%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.964285714285715%\" valign=\"top\"\u003e\n \u003cp\u003eHypertension, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e3377(38.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e664(30.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e735(33.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e931(42.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e1047(48.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.178571428571429%\" 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 width=\"21.964285714285715%\" valign=\"top\"\u003e\n \u003cp\u003eDiabetes, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e1216(13.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e155(7.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e217(9.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e282(12.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e562(25.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.178571428571429%\" 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 width=\"21.964285714285715%\" valign=\"top\"\u003e\n \u003cp\u003eDyslipidemia, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e4230(48.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e314(14.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e536(24.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e1200(55.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e2180(100.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.178571428571429%\" 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 width=\"21.964285714285715%\" valign=\"top\"\u003e\n \u003cp\u003eHeart Disease, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e1087(12.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e219(10.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e227(10.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e298(13.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e343(15.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.178571428571429%\" 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 width=\"21.964285714285715%\" valign=\"top\"\u003e\n \u003cp\u003eFPG, mg/dl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e102.42(94.32,113.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e99.36(91.98,107.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e100.44(93.60,109.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e101.88(94.50, 111.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e109.44(99.54, 126.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.178571428571429%\" 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 width=\"21.964285714285715%\" valign=\"top\"\u003e\n \u003cp\u003eHbA1c, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e5.1(4.9,5.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e5.1(4.8,5.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e5.1 (4.9,5.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e5.1 (4.9,5.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e5.2(4.9, 5.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.178571428571429%\" 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 width=\"21.964285714285715%\" valign=\"top\"\u003e\n \u003cp\u003eTC, mg/dl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e190.98(167.78,215.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e185.18(165.08,208.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e187.89(165.46,211.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e192.91(168.94, 216.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e197.55(172.81, 225.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.178571428571429%\" 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 width=\"21.964285714285715%\" valign=\"top\"\u003e\n \u003cp\u003eTG, mg/dl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e106.20(75.23,156.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e61.95(53.10,71.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e90.27(78.77, 102.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e126.56(109.74, 145.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e210.63(172.58, 279.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.178571428571429%\" 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 width=\"21.964285714285715%\" valign=\"top\"\u003e\n \u003cp\u003eHDL-C, mg/dl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e49.48(40.21,59.92)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e64.95(57.22,74.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e53.35(47.55, 59.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e46.01(40.98, 51.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e36.34(31.31, 41.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.178571428571429%\" 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 width=\"21.964285714285715%\" valign=\"top\"\u003e\n \u003cp\u003eLDL-C, mg/dl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e114.43(93.94,137.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e109.02(90.46,128.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e117.53(97.81,139.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e122.17(100.13, 144.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e109.79(85.44, 135.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.178571428571429%\" 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 width=\"21.964285714285715%\" valign=\"top\"\u003e\n \u003cp\u003eGMS, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.178571428571429%\" 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 width=\"21.964285714285715%\" valign=\"top\"\u003e\n \u003cp\u003eNGR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e3467(39.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e1073(49.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e994(45.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e876(40.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e524(24.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.178571428571429%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.964285714285715%\" valign=\"top\"\u003e\n \u003cp\u003ePre-DM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e4044(46.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e954(43.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e971(44.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e1024(46.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e1095(50.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.178571428571429%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.964285714285715%\" valign=\"top\"\u003e\n \u003cp\u003eDM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e1216(13.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e155(7.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e217(9.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e282(12.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.571428571428571%\" valign=\"top\"\u003e\n \u003cp\u003e562(25.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.178571428571429%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eData were presented as mean\u0026plusmn;SD, median and\u0026nbsp;interquartile range, or as n (%)\u003c/p\u003e\n\u003cp\u003eAbbreviations: AIP, Atherogenic index of plasma; SBP, Systolic blood pressure; DBP, Diastolic blood pressure; BMI, Body mass index; FPG, Fasting plasma glucose; HbA1c, Hemoglobin A1c; TC, Total cholesterol; TG, Triglyceride; HDL-C, High-density lipoprotein cholesterol; LDL-C, Low-density lipoprotein cholesterol; GMS, glucose metabolic states; NGR, Normal glucose regulation; Pre-DM, Prediabetes mellitus; DM, Diabetes mellitus;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePredictive value of baseline AIP for first stroke events\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDuring a median follow-up period of 9 years, 734 (8.4%) participants experienced their first stroke. According to the AIP quartiles, the incidences of stroke, from Q1 to Q4, were 5.65, 7.99, 10.18, 13.55 per 1,000 person-years, respectively. Analysis of the \u0026nbsp;Kaplan\u0026ndash;Meier cumulative incidence curve revealed a gradual increase in stroke events from the Q1 to Q4 groups, with statistically significant differences observed (Fig 2A. log-rank test P\u0026lt;0.0001). The Cox proportional hazard models confirmed a significant relationship between baseline AIP levels and new‐onset stroke. Baseline AIP was analyzed as both a continuous variable and as a categorical variable (quartiles). Following adjustment for potential confounding factors, per 1-unit increase in baseline AIP was associated with a 90% higher risk of stroke in Model 3 (HR 1.90, 95% CI 1.52\u0026ndash;2.36). Furthermore, the risk of stroke showed an increasing trend across quartiles of AIP in Model 3 (HR 1.34, 95%CI 1.05-1.71 for Q2; HR 1.52, 95%CI 1.19-1.93 for Q3; HR 1.84, 95%CI 1.45-2.34 for Q4; p -trend 0.001, Table 2). Multivariable-adjusted RCS analysis also demonstrated a significant dose-response relationship between the AIP as a continuous variable and the risk of stroke (P for overall trend\u0026lt;0.001; P for nonlinear=0.2551)(Fig. \u0026nbsp;3A).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssociations between\u0026nbsp;AIP and\u0026nbsp;stroke regulated by individual glucose metabolic states\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDuring the follow-up period, 219 (6.3%) participants with NGR, 352 (8.7%) participants with Pre-DM and 163 (13.4%) participants with DM were detected with the first stroke. The Kaplan\u0026ndash;Meier curves (Fig. 2B\u0026ndash;D) showed a significant difference in the cumulative incidence of stroke among Pre-DM and DM across the four AIP groups (P\u0026lt;0.0001), while no significant difference was observed for NGR (P=0.57). The results presented in Table 3 indicated that, in comparison to Q1, other AIP groups showed a significant association with an increased risk of stroke in individuals with Pre-DM and DM in Model 3. Specifically, for individuals with Pre-DM, HR values were 1.49 (95% CI 1.03-2.16) for Q2, 1.80 (95% CI 1.26-2.57) for Q3, and 2.27 (95% CI 1.60-3.23) for Q4, with a p-trend of 0.001. In individuals with DM, HR values were 3.08 (95% CI 1.16-8.20) for Q2, 3.95 (95% CI 1.54-10.12) for Q3, and 4.58 (95% CI 1.83-11.47) for Q4, with a p-trend of 0.001. However, no significant differences were found among AIP groups in individuals with NGR in the three Cox models (all p-values \u0026gt; 0.05). The RCS analysis showed a notable increase in the risk of stroke in individuals with Pre-DM and DM as baseline AIP rises, demonstrating a linear relationship (Pre-DM: P for nonlinear= 0.1193; DM: P for nonlinear=0.3121) (Fig. 3C\u0026ndash;D). Conversely, the analysis did not reveal a significant dose-response correlation between AIP and the risk of stroke in individuals with NGR (Fig. 3B). Together, these results provided valuable insights into the correlation between elevated AIP levels and the risk of stroke in individuals with abnormal glucose metabolism.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSubgroup analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo further explore the association between baseline AIP and first stroke events, we performed a subgroup analysis stratified by potential risk factors. As shown in Table 4, elevated AIP levels were associated with a higher incidence of stroke, which was consistent across different subgroups including age, gender, BMI, residence, and hypertension. Among individuals with Pre-DM and DM, increased AIP levels was linked to a higher stroke risk, whereas this association was not observed in the NGR groups. Significant interactions were noted between AIP and BMI (P value for interaction=0.011) as well as between AIP and glucose metabolic status (P value for interaction=0.031). However, no significant interactions were detected between AIP and other variables (all P values for interaction\u0026gt; 0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2 The HR (95% CI) of stroke according to AIP in the three Models\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"577\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.791666666666668%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eCategories\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eEvent, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.916666666666668%\" valign=\"top\"\u003e\n \u003cp\u003eModel1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.916666666666668%\" valign=\"top\"\u003e\n \u003cp\u003eModel2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.916666666666668%\" valign=\"top\"\u003e\n \u003cp\u003eModel3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003eHR (95% CI) \u0026nbsp; \u0026nbsp; P value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003eHR (95% CI) \u0026nbsp; \u0026nbsp; P value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003eHR (95% CI) \u0026nbsp; P value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.791666666666668%\" valign=\"top\"\u003e\n \u003cp\u003eContinuous variable per unit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e734(8.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.916666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e2.65(2.19-3.20) \u0026nbsp; \u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.916666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e2.19(1.78-2.69) \u0026nbsp;\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.916666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e1.90(1.52-2.36) \u0026nbsp;\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.791666666666668%\" valign=\"top\"\u003e\n \u003cp\u003eQuartile\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.916666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.916666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.916666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.791666666666668%\" valign=\"top\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e111(5.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.916666666666668%\" valign=\"top\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.916666666666668%\" valign=\"top\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.916666666666668%\" valign=\"top\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.791666666666668%\" valign=\"top\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e157(7.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.916666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e1.43(1.12-1.82) \u0026nbsp; 0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.916666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e1.35(1.05-1.72) \u0026nbsp;0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.916666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e1.34(1.05-1.71) \u0026nbsp;0.019\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.791666666666668%\" valign=\"top\"\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e200(9.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.916666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e1.83(1.45-2.31) \u0026nbsp;\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.916666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e1.58(1.24-2.01) \u0026nbsp;\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.916666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e1.52(1.19-1.93) \u0026nbsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.791666666666668%\" valign=\"top\"\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e266(12.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.916666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e2.49(1.99-3.10) \u0026nbsp;\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.916666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e2.05(1.62-2.58) \u0026nbsp;\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.916666666666668%\" valign=\"top\"\u003e\n \u003cp\u003e1.84(1.45-2.34) \u0026nbsp;\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eModel1: unadjusted\u003c/p\u003e\n\u003cp\u003eModel 2: adjusted for age, gender, marital status, drinking, smoking, residence, SBP, DBP, BMI\u003c/p\u003e\n\u003cp\u003eModel 3: Model 2+adjusted for hypertension, heart disease, TC, FPG, HbA1c\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3 Association between AIP and the risk of stroke according to glucose metabolic states\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"586\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.420783645655877%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eCategories\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.243611584327088%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eEvent, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.190800681431003%\" valign=\"top\"\u003e\n \u003cp\u003eModel1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.487223168654175%\" valign=\"top\"\u003e\n \u003cp\u003eModel2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.657580919931856%\" valign=\"top\"\u003e\n \u003cp\u003eModel3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.88943488943489%\" valign=\"top\"\u003e\n \u003cp\u003eHR (95% CI) \u0026nbsp; \u0026nbsp; P value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.432432432432435%\" valign=\"top\"\u003e\n \u003cp\u003eHR (95% CI) \u0026nbsp; \u0026nbsp; P value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.67813267813268%\" valign=\"top\"\u003e\n \u003cp\u003eHR (95% CI) \u0026nbsp; P value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.420783645655877%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNGR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.243611584327088%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.190800681431003%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.487223168654175%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.657580919931856%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.420783645655877%\" valign=\"top\"\u003e\n \u003cp\u003eContinuous variable per unit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.243611584327088%\" valign=\"top\"\u003e\n \u003cp\u003e219(6.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.190800681431003%\" valign=\"top\"\u003e\n \u003cp\u003e1.48(0.93-2.36) \u0026nbsp;0.102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.487223168654175%\" valign=\"top\"\u003e\n \u003cp\u003e1.10(0.67-1.81) \u0026nbsp;0.696\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.657580919931856%\" valign=\"top\"\u003e\n \u003cp\u003e1.01(0.61-1.67) \u0026nbsp;0.969\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.420783645655877%\" valign=\"top\"\u003e\n \u003cp\u003eQuartile\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.243611584327088%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.190800681431003%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.487223168654175%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.657580919931856%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.420783645655877%\" valign=\"top\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.243611584327088%\" valign=\"top\"\u003e\n \u003cp\u003e59(5.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.190800681431003%\" valign=\"top\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.487223168654175%\" valign=\"top\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.657580919931856%\" valign=\"top\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.420783645655877%\" valign=\"top\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.243611584327088%\" valign=\"top\"\u003e\n \u003cp\u003e64(6.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.190800681431003%\" valign=\"top\"\u003e\n \u003cp\u003e1.18(0.83-1.68) \u0026nbsp; 0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.487223168654175%\" valign=\"top\"\u003e\n \u003cp\u003e1.08(0.75-1.54) \u0026nbsp;0.681\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.657580919931856%\" valign=\"top\"\u003e\n \u003cp\u003e1.06(0.74-1.52) \u0026nbsp;0.752\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.420783645655877%\" valign=\"top\"\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.243611584327088%\" valign=\"top\"\u003e\n \u003cp\u003e59(6.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.190800681431003%\" valign=\"top\"\u003e\n \u003cp\u003e1.23(0.86-1.77) \u0026nbsp; 0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.487223168654175%\" valign=\"top\"\u003e\n \u003cp\u003e1.07(0.74-1.56) \u0026nbsp;0.721\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.657580919931856%\" valign=\"top\"\u003e\n \u003cp\u003e1.03(0.71-1.50) \u0026nbsp;0.873\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.420783645655877%\" valign=\"top\"\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.243611584327088%\" valign=\"top\"\u003e\n \u003cp\u003e37(7.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.190800681431003%\" valign=\"top\"\u003e\n \u003cp\u003e1.30(0.86-1.96) \u0026nbsp; 0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.487223168654175%\" valign=\"top\"\u003e\n \u003cp\u003e1.03(0.67-1.58) \u0026nbsp;0.907\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.657580919931856%\" valign=\"top\"\u003e\n \u003cp\u003e0.96(0.62-1.48) \u0026nbsp;0.849\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.420783645655877%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePre-DM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.243611584327088%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.190800681431003%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.487223168654175%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.657580919931856%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.420783645655877%\" valign=\"top\"\u003e\n \u003cp\u003eContinuous variable per unit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.243611584327088%\" valign=\"top\"\u003e\n \u003cp\u003e352(8.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.190800681431003%\" valign=\"top\"\u003e\n \u003cp\u003e2.73(2.02-3.70) \u0026nbsp; \u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.487223168654175%\" valign=\"top\"\u003e\n \u003cp\u003e2.44(1.77-3.37) \u0026nbsp;\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.657580919931856%\" valign=\"top\"\u003e\n \u003cp\u003e2.37(1.70-3.30) \u0026nbsp;\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.420783645655877%\" valign=\"top\"\u003e\n \u003cp\u003eQuartile\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.243611584327088%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.190800681431003%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.487223168654175%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.657580919931856%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.420783645655877%\" valign=\"top\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.243611584327088%\" valign=\"top\"\u003e\n \u003cp\u003e47(4.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.190800681431003%\" valign=\"top\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.487223168654175%\" valign=\"top\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.657580919931856%\" valign=\"top\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.420783645655877%\" valign=\"top\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.243611584327088%\" valign=\"top\"\u003e\n \u003cp\u003e71(7.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.190800681431003%\" valign=\"top\"\u003e\n \u003cp\u003e1.50(1.04-2.17) \u0026nbsp; 0.031\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.487223168654175%\" valign=\"top\"\u003e\n \u003cp\u003e1.48(1.02-2.14) \u0026nbsp;0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.657580919931856%\" valign=\"top\"\u003e\n \u003cp\u003e1.49(1.03-2.16) \u0026nbsp;0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.420783645655877%\" valign=\"top\"\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.243611584327088%\" valign=\"top\"\u003e\n \u003cp\u003e100(9.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.190800681431003%\" valign=\"top\"\u003e\n \u003cp\u003e2.03(1.43-2.86) \u0026nbsp; \u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.487223168654175%\" valign=\"top\"\u003e\n \u003cp\u003e1.82(1.27-2.60) \u0026nbsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.657580919931856%\" valign=\"top\"\u003e\n \u003cp\u003e1.80(1.26-2.57) \u0026nbsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.420783645655877%\" valign=\"top\"\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.243611584327088%\" valign=\"top\"\u003e\n \u003cp\u003e134(12.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.190800681431003%\" valign=\"top\"\u003e\n \u003cp\u003e2.57(1.85-3.59) \u0026nbsp; \u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.487223168654175%\" valign=\"top\"\u003e\n \u003cp\u003e2.35(1.66-3.33) \u0026nbsp;\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.657580919931856%\" valign=\"top\"\u003e\n \u003cp\u003e2.27(1.60-3.23) \u0026nbsp;\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.420783645655877%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.243611584327088%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.190800681431003%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.487223168654175%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.657580919931856%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.420783645655877%\" valign=\"top\"\u003e\n \u003cp\u003eContinuous variable per unit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.243611584327088%\" valign=\"top\"\u003e\n \u003cp\u003e163(13.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.190800681431003%\" valign=\"top\"\u003e\n \u003cp\u003e2.16(1.56-3.00) \u0026nbsp; \u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.487223168654175%\" valign=\"top\"\u003e\n \u003cp\u003e2.04(1.44-2.90) \u0026nbsp;\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.657580919931856%\" valign=\"top\"\u003e\n \u003cp\u003e2.00(1.36-2.93) \u0026nbsp;\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.420783645655877%\" valign=\"top\"\u003e\n \u003cp\u003eQuartile\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.243611584327088%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.190800681431003%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.487223168654175%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.657580919931856%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.420783645655877%\" valign=\"top\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.243611584327088%\" valign=\"top\"\u003e\n \u003cp\u003e5(3.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.190800681431003%\" valign=\"top\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.487223168654175%\" valign=\"top\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.657580919931856%\" valign=\"top\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.420783645655877%\" valign=\"top\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.243611584327088%\" valign=\"top\"\u003e\n \u003cp\u003e22(10.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.190800681431003%\" valign=\"top\"\u003e\n \u003cp\u003e3.22(1.22-8.51) \u0026nbsp; 0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.487223168654175%\" valign=\"top\"\u003e\n \u003cp\u003e3.03(1.14-8.05) \u0026nbsp;0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.657580919931856%\" valign=\"top\"\u003e\n \u003cp\u003e3.08(1.16-8.20) \u0026nbsp;0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.420783645655877%\" valign=\"top\"\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.243611584327088%\" valign=\"top\"\u003e\n \u003cp\u003e41(14.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.190800681431003%\" valign=\"top\"\u003e\n \u003cp\u003e4.71(1.86-11.93) \u0026nbsp; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.487223168654175%\" valign=\"top\"\u003e\n \u003cp\u003e4.18(1.63-10.67) \u0026nbsp;0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.657580919931856%\" valign=\"top\"\u003e\n \u003cp\u003e3.95(1.54-10.12) \u0026nbsp;0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.420783645655877%\" valign=\"top\"\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.243611584327088%\" valign=\"top\"\u003e\n \u003cp\u003e95(16.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.190800681431003%\" valign=\"top\"\u003e\n \u003cp\u003e5.62(2.29-13.81) \u0026nbsp;\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.487223168654175%\" valign=\"top\"\u003e\n \u003cp\u003e4.87(1.95-12.16) \u0026nbsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.657580919931856%\" valign=\"top\"\u003e\n \u003cp\u003e4.58(1.83-11.47) \u0026nbsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eModel1: unadjusted\u003c/p\u003e\n\u003cp\u003eModel 2: adjusted for age, gender, marital status, drinking, smoking, residence, SBP, DBP, BMI\u003c/p\u003e\n\u003cp\u003eModel 3: Model 2+adjusted for hypertension, heart disease, TC, FPG, HbA1c\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4 Subgroup and interaction analyses of the association between AIP and stroke\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"565\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.345132743362832%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"65.84070796460178%\" colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003eQuartiles of AIP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.8141592920354%\" valign=\"top\"\u003e\n \u003cp\u003eP for interaction\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.345132743362832%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.15929203539823%\" valign=\"top\"\u003e\n \u003cp\u003eQ1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.460176991150444%\" valign=\"top\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.8141592920354%\" valign=\"top\"\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.4070796460177%\" valign=\"top\"\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.8141592920354%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.345132743362832%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.15929203539823%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.460176991150444%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.8141592920354%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.4070796460177%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.8141592920354%\" valign=\"top\"\u003e\n \u003cp\u003e0.268\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.345132743362832%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;60years\u003c/p\u003e\n \u003cp\u003e(Case/Total）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.15929203539823%\" valign=\"top\"\u003e\n \u003cp\u003e1 (Ref.) \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e40/1282\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.460176991150444%\" valign=\"top\"\u003e\n \u003cp\u003e1.55(1.05-2.31)\u003c/p\u003e\n \u003cp\u003e66/1301\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.8141592920354%\" valign=\"top\"\u003e\n \u003cp\u003e1.92(1.32-2.81)\u003c/p\u003e\n \u003cp\u003e95/1314\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.4070796460177%\" valign=\"top\"\u003e\n \u003cp\u003e2.30(1.58-3.34)\u003c/p\u003e\n \u003cp\u003e140/1381\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.8141592920354%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.345132743362832%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026ge;60years\u003c/p\u003e\n \u003cp\u003e(Case/Total）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.15929203539823%\" valign=\"top\"\u003e\n \u003cp\u003e1 (Ref.)\u003c/p\u003e\n \u003cp\u003e71/900\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.460176991150444%\" valign=\"top\"\u003e\n \u003cp\u003e1.23(0.90-1.68) 91/881\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.8141592920354%\" valign=\"top\"\u003e\n \u003cp\u003e1.27(0.93-1.74)\u003c/p\u003e\n \u003cp\u003e105/868\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.4070796460177%\" valign=\"top\"\u003e\n \u003cp\u003e1.57(1.15-2.16)\u003c/p\u003e\n \u003cp\u003e126/800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.8141592920354%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.345132743362832%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.15929203539823%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.460176991150444%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.8141592920354%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.4070796460177%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.8141592920354%\" valign=\"top\"\u003e\n \u003cp\u003e0.807\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.345132743362832%\" valign=\"top\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003cp\u003e(Case/Total）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.15929203539823%\" valign=\"top\"\u003e\n \u003cp\u003e1 (Ref.)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e50/1081\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.460176991150444%\" valign=\"top\"\u003e\n \u003cp\u003e1.38(0.97-1.97)\u003c/p\u003e\n \u003cp\u003e82/1208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.8141592920354%\" valign=\"top\"\u003e\n \u003cp\u003e1.45(1.03-2.05)\u003c/p\u003e\n \u003cp\u003e106/1252\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.4070796460177%\" valign=\"top\"\u003e\n \u003cp\u003e1.88(1.34-2.64)\u003c/p\u003e\n \u003cp\u003e143/1201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.8141592920354%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.345132743362832%\" valign=\"top\"\u003e\n \u003cp\u003eMale (Case/Total）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.15929203539823%\" valign=\"top\"\u003e\n \u003cp\u003e1 (Ref.)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e61/1101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.460176991150444%\" valign=\"top\"\u003e\n \u003cp\u003e1.28(0.91-1.81)\u003c/p\u003e\n \u003cp\u003e75/974\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.8141592920354%\" valign=\"top\"\u003e\n \u003cp\u003e1.58(1.13-2.21)\u003c/p\u003e\n \u003cp\u003e94/930\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.4070796460177%\" valign=\"top\"\u003e\n \u003cp\u003e1.79(1.27-2.52)\u003c/p\u003e\n \u003cp\u003e123/980\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.8141592920354%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.345132743362832%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.15929203539823%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.460176991150444%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.8141592920354%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.4070796460177%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.8141592920354%\" valign=\"top\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.345132743362832%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;24kg/m2\u003c/p\u003e\n \u003cp\u003e(Case/Total）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.15929203539823%\" valign=\"top\"\u003e\n \u003cp\u003e1 (Ref.)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e91/1749\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.460176991150444%\" valign=\"top\"\u003e\n \u003cp\u003e1.22(0.91-1.64)\u003c/p\u003e\n \u003cp\u003e92/1447\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.8141592920354%\" valign=\"top\"\u003e\n \u003cp\u003e1.14(0.83-1.56)\u003c/p\u003e\n \u003cp\u003e70/1095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.4070796460177%\" valign=\"top\"\u003e\n \u003cp\u003e1.99(1.47-2.69)\u003c/p\u003e\n \u003cp\u003e99/834\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.8141592920354%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.345132743362832%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026ge;24kg/m2\u003c/p\u003e\n \u003cp\u003e(Case/Total）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.15929203539823%\" valign=\"top\"\u003e\n \u003cp\u003e1 (Ref.)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e20/433\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.460176991150444%\" valign=\"top\"\u003e\n \u003cp\u003e1.75(1.06-2.89)\u003c/p\u003e\n \u003cp\u003e65/735\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.8141592920354%\" valign=\"top\"\u003e\n \u003cp\u003e2.14(1.33-3.45)\u003c/p\u003e\n \u003cp\u003e130/1087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.4070796460177%\" valign=\"top\"\u003e\n \u003cp\u003e2.08(1.30-3.34)\u003c/p\u003e\n \u003cp\u003e167/1347\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.8141592920354%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.345132743362832%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eResidence\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.15929203539823%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.460176991150444%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.8141592920354%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.4070796460177%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.8141592920354%\" valign=\"top\"\u003e\n \u003cp\u003e0.6533\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.345132743362832%\" valign=\"top\"\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003cp\u003e(Case/Total）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.15929203539823%\" valign=\"top\"\u003e\n \u003cp\u003e1 (Ref.)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e79/1596\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.460176991150444%\" valign=\"top\"\u003e\n \u003cp\u003e1.46(1.09-1.95)\u003c/p\u003e\n \u003cp\u003e114/1486\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.8141592920354%\" valign=\"top\"\u003e\n \u003cp\u003e1.67(1.25-2.23)\u003c/p\u003e\n \u003cp\u003e133/1380\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.4070796460177%\" valign=\"top\"\u003e\n \u003cp\u003e2.03(1.52-2.72)\u003c/p\u003e\n \u003cp\u003e161/1293\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.8141592920354%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.345132743362832%\" valign=\"top\"\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003cp\u003e(Case/Total）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.15929203539823%\" valign=\"top\"\u003e\n \u003cp\u003e1 (Ref.)\u003c/p\u003e\n \u003cp\u003e32/586\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.460176991150444%\" valign=\"top\"\u003e\n \u003cp\u003e1.06(0.67-1.68)\u003c/p\u003e\n \u003cp\u003e43/696\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.8141592920354%\" valign=\"top\"\u003e\n \u003cp\u003e1.19(0.77-1.84)\u003c/p\u003e\n \u003cp\u003e67/802\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.4070796460177%\" valign=\"top\"\u003e\n \u003cp\u003e1.53(1.00-2.33)\u003c/p\u003e\n \u003cp\u003e105/888\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.8141592920354%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.345132743362832%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHypertension\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.15929203539823%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.460176991150444%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.8141592920354%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.4070796460177%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.8141592920354%\" valign=\"top\"\u003e\n \u003cp\u003e0.300\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.345132743362832%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003cp\u003e(Case/Total）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.15929203539823%\" valign=\"top\"\u003e\n \u003cp\u003e1 (Ref.)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e56/664\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.460176991150444%\" valign=\"top\"\u003e\n \u003cp\u003e1.20(0.85-1.70)\u003c/p\u003e\n \u003cp\u003e77/735\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.8141592920354%\" valign=\"top\"\u003e\n \u003cp\u003e1.60(1.16-2.21)\u003c/p\u003e\n \u003cp\u003e135/931\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.4070796460177%\" valign=\"top\"\u003e\n \u003cp\u003e1.76(1.27-2.42)\u003c/p\u003e\n \u003cp\u003e175/1047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.8141592920354%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.345132743362832%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003cp\u003e(Case/Total）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.15929203539823%\" valign=\"top\"\u003e\n \u003cp\u003e1 (Ref.)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e55/1518\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.460176991150444%\" valign=\"top\"\u003e\n \u003cp\u003e1.53(1.08-2.17)\u003c/p\u003e\n \u003cp\u003e80/1447\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.8141592920354%\" valign=\"top\"\u003e\n \u003cp\u003e1.35(0.93-1.96)\u003c/p\u003e\n \u003cp\u003e65/1251\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.4070796460177%\" valign=\"top\"\u003e\n \u003cp\u003e1.99(1.38-2.86)\u003c/p\u003e\n \u003cp\u003e91/1134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.8141592920354%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.345132743362832%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGMS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.15929203539823%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.460176991150444%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.8141592920354%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.4070796460177%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.8141592920354%\" valign=\"top\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.345132743362832%\" valign=\"top\"\u003e\n \u003cp\u003eNGR\u003c/p\u003e\n \u003cp\u003e(Case/Total）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.15929203539823%\" valign=\"top\"\u003e\n \u003cp\u003e1 (Ref.)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e59/1073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.460176991150444%\" valign=\"top\"\u003e\n \u003cp\u003e1.06(0.74-1.52)\u003c/p\u003e\n \u003cp\u003e64/994\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.8141592920354%\" valign=\"top\"\u003e\n \u003cp\u003e1.03(0.71-1.50)\u003c/p\u003e\n \u003cp\u003e59/876\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.4070796460177%\" valign=\"top\"\u003e\n \u003cp\u003e0.96(0.62-1.48)\u003c/p\u003e\n \u003cp\u003e37/524\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.8141592920354%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.345132743362832%\" valign=\"top\"\u003e\n \u003cp\u003ePre-DM\u003c/p\u003e\n \u003cp\u003e(Case/Total）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.15929203539823%\" valign=\"top\"\u003e\n \u003cp\u003e1 (Ref.)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e47/954\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.460176991150444%\" valign=\"top\"\u003e\n \u003cp\u003e1.49(1.03-2.16)\u003c/p\u003e\n \u003cp\u003e71/971\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.8141592920354%\" valign=\"top\"\u003e\n \u003cp\u003e1.80(1.26-2.57)\u003c/p\u003e\n \u003cp\u003e100/1024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.4070796460177%\" valign=\"top\"\u003e\n \u003cp\u003e2.27(1.60-3.23)\u003c/p\u003e\n \u003cp\u003e134/1095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.8141592920354%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.345132743362832%\" valign=\"top\"\u003e\n \u003cp\u003eDM\u003c/p\u003e\n \u003cp\u003e(Case/Total）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.15929203539823%\" valign=\"top\"\u003e\n \u003cp\u003e1 (Ref.)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e5/155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.460176991150444%\" valign=\"top\"\u003e\n \u003cp\u003e3.08(1.16-8.20)\u003c/p\u003e\n \u003cp\u003e22/217\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.8141592920354%\" valign=\"top\"\u003e\n \u003cp\u003e3.95(1.54-10.12)\u003c/p\u003e\n \u003cp\u003e41/282\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.4070796460177%\" valign=\"top\"\u003e\n \u003cp\u003e4.58(1.83-11.47)\u003c/p\u003e\n \u003cp\u003e95/562\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.8141592920354%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eModel adjusted for age, gender, marital status, drinking, smoking, residence, SBP, DBP, BMI, hypertension, heart disease, TC, FPG, HbA1c\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn the large national longitudinal survey cohort of middle-aged and elderly individuals, a significant correlation was elaborated between higher baseline AIP levels and an increased risk of a new-onset stroke. This association was particularly prominent in those with abnormal glucose metabolism, including Pre-DM and DM. The study suggested that baseline AIP might be a dependable biomarker for stratifying stroke risk. Maintaining a low AIP level might be beneficial for the primary prevention of stroke in individuals with abnormal glucose metabolism.\u003c/p\u003e\n\u003cp\u003eDyslipidaemia and insulin resistance are features of the metabolic syndrome, both of which contribute to the risk of stroke(18-20). The AIP is widely recognized as a reliable marker of dyslipidemia and atherosclerosis. Several studies have demonstrated that both baseline and cumulative AIP exposure are linked to cardiovascular diseases, particularly coronary artery disease(17, 21-23). Notably, the impact of AIP on cardiovascular diseases may differ depending on an individual’s glucose metabolic states. Min et al. have found that individuals with abnormal glucose metabolism and higher AIP levels may have a greater risk of developing cardiovascular diseases(24). In addition, higher levels of AIP have been shown to be positively correlated with the risk of hypertension and non-alcoholic fatty liver disease, with this relationship potentially influenced by the glucose metabolic states(25-27). Interestingly, a cross-sectional study has shown a strong association between elevated AIP levels and an increased risk of insulin resistance and the onset of type 2 diabetes(5). Recent research has explored the association between AIP and cerebrovascular disease, revealing that higher AIP levels are correlated with a higher incidence of atherosclerotic stenosis in the carotid and intracranial arteries(9, 28). In the general population, increased baseline and cumulative AIP levels are associated with a greater risk of ischemic stroke(8, 10). To the best of our knowledge, this study is the first to demonstrate the significant predictive value of baseline AIP level for stroke in individuals with glycemic dysregulation.\u003c/p\u003e\n\u003cp\u003eThis study found that high baseline AIP levels were associated with new-onset stroke in individuals with Pre-DM and DM, which was consistent with previous reports that high AIP was associated with the risk and prognosis of stroke. Therefore, assessing AIP levels in middle-aged and elderly individuals with glycemic dysregulation would have clinical significance. Further studies are warranted to investigate the baseline levels of AIP , which is able to predict and identify stroke risk. Lowering LDL-cholesterol has been traditionally believed to be beneficial in preventing overall stroke, with statins being commonly used for this purpose(29). However, a multicenter clinical trial shows that patients with high triglycerides levels faced a high risk of ischemic stroke, despite statin therapy(30). Recent data also advocate for lowering triglycerides as a strategy to prevent stroke(31). Therefore, it is plausible to consider that lowering triglycerides, which is equivalent to lowering AIP levels, contributes to stroke prevention.\u003c/p\u003e\n\u003cp\u003eAlthough the mechanisms underlying the relationship between AIP and stroke remain unclear, several possible explanations have been proposed. Firstly, triglycerides appear to be related to vascular inflammation and subclinical atherosclerosis. Raised serum triglycerides can potentiate inflammatory responses in vascular endothelial cells and vascular smooth muscle cells, especially in diabetic patients(32, 33). Emerging evidence indicates that triglycerides-rich lipoproteins like chylomicrons and very low-density lipoproteins may play a role in atherosclerotic lesion formation(34, 35). On the other hand, HDL particles exhibit various vasoprotective properties, such as reducing cellular death, dampening inflammatory response, and shielding against pathological oxidation(36). Therefore, it can be inferred that as AIP values increase, higher levels of triglycerides lead to more significant damage to vascular structure and function, while lower levels of HDL offer less protection to the vasculature. Secondly, the level of AIP has been shown to be closely associated with traditional risk factors for stroke, including BMI, hypertension, diabetes, dyslipidemia, and heart disease. The results of the present study are consistent with these studies. Elevated AIP levels may interact with other cerebrovascular risk factors and potentially exacerbate the progression of stroke. However, further research is necessary to fully understand the underlying mechanisms.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStrengths and\u0026nbsp;limitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis is the largest population-based investigation into the association between AIP and stroke among middle-aged and elderly individuals with glycemic dysregulation. The data were obtained from a high-quality, nationally representative longitudinal survey of the middle-aged and elderly population across various regions of China, including urban and rural areas. In order to obtain robust results, we included potential confounders to exclude interference in the results. With nearly a decade of follow-up, our analysis indicates that baseline AIP is a reliable predictor of stroke in middle-aged and elderly individuals with dysglycaemia. Moreover, since standard assay for TG and HDL-C are widely used in clinical practice and it is straightforward to calculate AIP from TG and HDL-C, it is sensible to recommend AIP as an efficient and convenient indicator for assessing the risk of stroke.\u003c/p\u003e\n\u003cp\u003eHowever, there are several limitations of the study that require consideration. Firstly, while almost all participants adhered to standard phlebotomy protocol, a minority did not adhere to the fasting requirement of up to 8 hours prior to blood collection. This deviation may have impacted the accuracy of the calculated AIP values. Secondly, the study was based on middle-aged and elderly Chinese, and further validation in other ethnic and age groups is needed. Thirdly, this study focused on the impact of baseline AIP level and did not examine the longitudinal changes in AIP during the follow-up period. Forthly, endpoint events for this study were determined based on questionnaire interviews with participants and end-point assessments could not be validated by hospital records. Fifthly, due to a lack of data on stroke subtypes in CHARLS, this study was unable to assess the effect of AIP on ischemic or hemorrhagic stroke, respectively. Finally, despite efforts to control for confounding variables, there is a possibility that some confounders were not accounted for. Further investigations are needed to verify our findings in other large cohort studies.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn the pursuit of primary prevention strategies to reduce stroke incidence, this longitudinal prospective study found that baseline high AIP levels in individuals with glycemic dysregulation might indicate subgroups at a higher risk of developing stroke, particularly among individuals under 60 years old with a BMI≥24kg/m\u003csup\u003e2\u003c/sup\u003e residing in rural areas. However, the levels of AIP in middle-aged and older adults without dysglycaemia do not affect the occurrence of stroke.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAIP: Atherogenic index of plasma; IR: Insulin resistance; GMS: Glucose metabolic states; NGR: Normal glucose regulation; Pre-DM: Prediabetes mellitus; DM: Diabetes mellitus; SBP: Systolic blood pressure; DBP: Diastolic blood pressure; BMI: Body mass index; FPG: Fasting blood glucose; HbA1c: Glycosylated hemoglobin; TC: Total cholesterol; TG: Triglyceride; HDL-C: High-density lipoprotein cholesterol; LDL-C: Low-density lipoprotein cholesterol.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by the Biomedical Ethics Review Committee of Peking University (IRB00001052-1101). The participants provided their written informed consent to participate in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDatasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by the Natural Science Foundation of Jiangsu Province (BK20211005 to J.J., BK20231120 to X.Z), the Key Research and Development Program of Jiangsu Province of China (BE2020620 to Y.X.), the STI2030-Major Projects-2022ZD0211800, the Jiangsu Province Key Medical Discipline (ZDXK202216 to Y.X.) and Nanjing Medical Science and Technology Development Foundation (ZKX22025 to X.Z.).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJJ, XZ and LQ were involved in the design of this study; LQ and SF contributed to manuscript writing; SX and YP were reponsible for data collection and data management; LQ, JJ, and ZL contributed to the statistical analysis; JJ, XZ and YX participated in data review and manuscript revision. All authors have reviewed and approved the final \u0026nbsp;manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank all participants of the CHALRS.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eDepartment of Neurology, Nanjing Drum Tower Hospital, Clinical College of Nanjing Medical University, Nanjing 210008, China. \u003csup\u003e2\u003c/sup\u003eDepartment of Neurology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing 210008, China. \u003csup\u003e3\u003c/sup\u003eState Key Laboratory of Pharmaceutical Biotechnology and Institute of Translational Medicine for Brain Critical Diseases, Nanjing University, Nanjing 210008, China. \u003csup\u003e4\u003c/sup\u003eJiangsu Key Laboratory for Molecular Medicine, Medical School of Nanjing University, Nanjing 210008, China. \u003csup\u003e5\u003c/sup\u003eNanjing Neurology Clinical Medical Center, Nanjing 210008, China.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eOwolabi MO, Thrift AG, Mahal A, Ishida M, Martins S, Johnson WD, et al. 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Association between atherogenic index of plasma and prehypertension or hypertension among normoglycemia subjects in a Japan population: a cross-sectional study. Lipids in health and disease. 2023;22(1):87.\u003c/li\u003e\n\u003cli\u003eHuang Q, Liu Z, Wei M, Huang Q, Feng J, Liu Z, et al. The atherogenic index of plasma and carotid atherosclerosis in a community population: a population-based cohort study in China. Cardiovascular diabetology. 2023;22(1):125.\u003c/li\u003e\n\u003cli\u003eSun L, Clarke R, Bennett D, Guo Y, Walters RG, Hill M, et al. Causal associations of blood lipids with risk of ischemic stroke and intracerebral hemorrhage in Chinese adults. Nature medicine. 2019;25(4):569-74.\u003c/li\u003e\n\u003cli\u003eBhatt DL, Steg PG, Miller M, Brinton EA, Jacobson TA, Ketchum SB, et al. Cardiovascular Risk Reduction with Icosapent Ethyl for Hypertriglyceridemia. The New England journal of medicine. 2019;380(1):11-22.\u003c/li\u003e\n\u003cli\u003eAradine E, Hou Y, Cronin CA, Chaturvedi S. Current Status of Dyslipidemia Treatment for Stroke Prevention. Current neurology and neuroscience reports. 2020;20(8):31.\u003c/li\u003e\n\u003cli\u003eWang YI, Schulze J, Raymond N, Tomita T, Tam K, Simon SI, et al. Endothelial inflammation correlates with subject triglycerides and waist size after a high-fat meal. American journal of physiology Heart and circulatory physiology. 2011;300(3):H784-91.\u003c/li\u003e\n\u003cli\u003eGordillo-Moscoso A, Ruiz E, Carnero M, Reguillo F, Rodriguez E, Tejerina T, et al. Relationship between serum levels of triglycerides and vascular inflammation, measured as COX-2, in arteries from diabetic patients: a translational study. Lipids in health and disease. 2013;12:62.\u003c/li\u003e\n\u003cli\u003e\u0026Ouml;\u0026ouml;rni K, Lehti S, Sj\u0026ouml;vall P, Kovanen PT. Triglyceride-Rich Lipoproteins as a Source of Proinflammatory Lipids in the Arterial Wall. Current medicinal chemistry. 2019;26(9):1701-10.\u003c/li\u003e\n\u003cli\u003eRaposeiras-Roubin S, Rossell\u0026oacute; X, Oliva B, Fern\u0026aacute;ndez-Friera L, Mendiguren JM, Andr\u0026eacute;s V, et al. Triglycerides and Residual Atherosclerotic Risk. Journal of the American College of Cardiology. 2021;77(24):3031-41.\u003c/li\u003e\n\u003cli\u003eKontush A. HDL-mediated mechanisms of protection in cardiovascular disease. Cardiovascular research. 2014;103(3):341-9.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"cardiovascular-diabetology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cvdb","sideBox":"Learn more about [Cardiovascular Diabetology](http://cardiab.biomedcentral.com/)","snPcode":"12933","submissionUrl":"https://submission.nature.com/new-submission/12933/3","title":"Cardiovascular Diabetology","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Atherogenic index of plasma, Stroke, Dysglycemia","lastPublishedDoi":"10.21203/rs.3.rs-4261103/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4261103/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e Circulating atherogenic index of plasma (AIP) levels has been proposed as a novel biomarker for dyslipidemia and as a predictor of insulin resistance (IR) risk. However, the association between AIP and the incidence of new-onset stroke, particularly in individuals with varying glucose metabolism states, remains ambiguous.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e A total of 8727 participants aged 45 years or older without a history of stroke from the China Health and Retirement Longitudinal Study (CHARLS)were included in this study. The AIP was calculated using the formula log [Triglyceride (mg/dL) / High-density lipoprotein cholesterol (mg/dL)]. Participants were divided into four groups based on their baseline AIP levels: Q1(AIP≤0.122), Q2(0.122\u0026lt;AIP≤0.329), Q3(0.329\u0026lt;AIP≤0.562), and Q4(AIP\u0026gt;0.562). The primary endpoint was the occurrence of new-onset stroke events. The Kaplan–Meier curves, Multivariate Cox proportional hazard models, and Restricted cubic spline (RCS) analysis were applied to explore the association between baseline AIP levels and the risk of developing a stroke among individuals with varying glycemic metabolic states.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e During a median follow-up of 9 years, 734 participants (8.4%) had a first stroke event. The relative risk for stroke increased with each increasing quartile of baseline AIP levels. Kaplan–Meier curve analysis revealed a significant difference in stroke occurrence among the AIP groups in all participants, as well as in those with prediabetes mellitus (Pre-DM) and diabetes mellitus (DM) (all P-values \u0026lt;0.05). After adjusting for potential confounders, the prevalence of stroke was significantly higher in the Q2, Q3, and Q4 groups than in the Q1 group in all participants. The respective hazard ratios (95% confidence interval) for stroke in the Q2, Q3, and Q4 groups were 1.34 (1.05-1.71), 1.52 (1.19-1.93), and 1.84 (1.45-2.34). Furthermore, high levels of AIP were found to be linked to an increased risk of stroke in both pre-diabetic and diabetic participants across all three Cox models. However, this association was not observed in participants with normal glucose regulation (NGR) (p\u0026gt;0.05). Restricted cubic spline analysis also demonstrated that higher baseline AIP levels were associated with hazard ratios for stroke in all participants and those with glucose metabolism disorders.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions \u003c/strong\u003eAn increase in baseline AIP levels was significantly associated with the risk of stroke in middle-aged and elderly individuals, and exhibited distinct characteristics depending on the individual’s glucose metabolism status.\u003c/p\u003e","manuscriptTitle":"Association between atherogenic index of plasma and new-onset stroke in individuals with different glucose metabolism status: insights from CHARLS","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-24 17:31:00","doi":"10.21203/rs.3.rs-4261103/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-05-08T14:16:47+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-05-08T12:33:53+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-05-06T20:19:00+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-04-27T07:18:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"cac48d94-51ce-4cbd-b97f-55c8c33fe1ac","date":"2024-04-17T12:37:18+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-04-17T10:35:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"0bd08bb8-045a-4889-9888-b3bbf8a93c1c","date":"2024-04-16T17:56:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"d0808710-ff41-487a-ac85-c3a033d5f4d1","date":"2024-04-16T05:49:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"cf5a3cd7-48bf-4ad2-94f0-64e169bbdb63","date":"2024-04-16T05:21:21+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-04-16T04:40:12+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-04-16T04:31:46+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-04-16T01:34:37+00:00","index":"","fulltext":""},{"type":"submitted","content":"Cardiovascular Diabetology","date":"2024-04-13T08:44:46+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"cardiovascular-diabetology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cvdb","sideBox":"Learn more about [Cardiovascular Diabetology](http://cardiab.biomedcentral.com/)","snPcode":"12933","submissionUrl":"https://submission.nature.com/new-submission/12933/3","title":"Cardiovascular Diabetology","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1614084a-8548-4630-add7-59a1227e8910","owner":[],"postedDate":"April 24th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-06-22T00:30:14+00:00","versionOfRecord":{"articleIdentity":"rs-4261103","link":"https://doi.org/10.1186/s12933-024-02314-y","journal":{"identity":"cardiovascular-diabetology","isVorOnly":false,"title":"Cardiovascular Diabetology"},"publishedOn":"2024-06-21 00:30:14","publishedOnDateReadable":"June 21st, 2024"},"versionCreatedAt":"2024-04-24 17:31:00","video":"","vorDoi":"10.1186/s12933-024-02314-y","vorDoiUrl":"https://doi.org/10.1186/s12933-024-02314-y","workflowStages":[]},"version":"v1","identity":"rs-4261103","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4261103","identity":"rs-4261103","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}