Relationship between the circulating N-terminal pro B-type natriuretic peptide and the risk of carotid artery plaque in different glucose metabolic states in patients with coronary heart disease: a CSCD-TCM plus study in China

Relationship between the circulating N-terminal pro B-type natriuretic peptide and the risk of carotid artery plaque in different glucose metabolic states in patients with coronary heart disease: a CSCD-TCM plus study in China | 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 Relationship between the circulating N-terminal pro B-type natriuretic peptide and the risk of carotid artery plaque in different glucose metabolic states in patients with coronary heart disease: a CSCD-TCM plus study in China Tong Yang, Hongmei Zheng, Fengmin Liu, Ruiying Guo, Guangwei Pan, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3298912/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Nov, 2023 Read the published version in Cardiovascular Diabetology → Version 1 posted 7 You are reading this latest preprint version Abstract Objective: Circulating N-terminal pro B-type natriuretic peptide(NT-proBNP) is often used as a marker of heart failure in patients with coronary heart disease (CHD), and it is associated with glycaemic abnormalities. Studies on the association and diagnostic value of NT-proBNP with carotid plaque (CAP) in patients with CHD are limited. Methods: This study included 5,093 patients diagnosed with CHD. Their NT-proBNP levels, blood glucose levels, the occurrence of CAP, and the number and nature of CAP were measured, and glucose metabolic status was defined as normoglycaemic (NG), prediabetes (Pre-DM), and diabetes mellitus (DM); logistic regression analyses were used to compare the relationship between NT-proBNP and the risk of CAP occurrence, and the number and nature of CAP. Patients were divided into three groups according to NT-proBNP tertiles. The diagnostic value of NT-proBNP for CAP risk was measured using ROC curves. There was a significant correlation between NT-proBNP and the risk of CAP in CHD patients in different glucose metabolic states. Results: Multiple logistic regression results showed that consecutive NT-proBNP was associated with risk of CAP progression. When NT-proBNP levels at T1 was used as the reference, T3 were associated with an increased OR for CAP (OR: 1.69; 95% CI: 1.37-2.10). This association was also present in the in both the pre-DM and DM states, which with the highest OR in the DM state (OR: 1.84; 95% CI: 1.26-2.69). This association was significant in DM status in men and pre-DM in women, and was significant in DM status in patients ≤60 years of age and in pre-DM and DM in patients >60 years of age. We also measured the diagnostic accuracy of CAP occurrence in NT-proBNP in CHD patients. The critical values were 182ng/l, with an AUC value of 0.627(95% CI: 0.592-0.631). Conclusion: The increase in NT-proBNP is significantly associated with the risk of CAP in patients with CHD, and exhibits specific characteristics under different glucose metabolism states, this association exists between different sexes and ages. Trial registration: The study was approved by the Ethics Committee of Tianjin University of Traditional Chinese Medicine (approval number TJUTCM-EC20210007) and certified by the Chinese Clinical Trials Registry on April 4, 2022 (registration number ChiCTR2200058296) and on March 25, 2022 by ClinicalTrials.gov (registration number NCT05309343). circulating N-terminal pro B-type natriuretic peptide carotid artery plaque glucose metabolic states coronary heart disease Figures Figure 1 Figure 2 Background Coronary heart disease (CHD) is a chronic noncommunicable disease whose morbidity and mortality are increasing with each passing year, representing a major public health burden worldwide [1], and whose pathogenesis is closely linked to the presence of endothelial dysfunction [2]. On the other hand, diabetes mellitus (DM) is another important risk factor independent of traditional risk factors such as hypertension, hyperlipidemia and smoking, and is associated with a 2-3-fold increase in the incidence of CHD, myocardial infarction (MI) and CHD mortality [3]. In addition, CHD often occurs in combination with DM, possibly because risk factors for both include abnormal inflammatory response or abnormal lipid metabolism [4]. Early detection of atherosclerosis, especially when CHD patients are asymptomatic, is crucial. Carotid artery plaque (CAP), as an atherosclerotic lesion, is thought to be able to predict poor outcome from CHD and can be used as a surrogate for atherosclerotic disease, and previous studies have found that abnormalities in glucose metabolism are strongly associated with the risk of developing carotid artery plaque [5-6]. Circulating N-terminal pro B-type natriuretic peptide (NT-proBNP), a typical diagnostic and prognostic marker for heart failure, is an inactive amino acid fragment of brain natriuretic peptide (BNP), released by cardiomyocytes in response to volume or pressure overload and promotes vasodilation and natriuretic effect [7], which is a better indicator of diabetes mellitus than traditional risk factors, may better differentiate the risk of death from cardiovascular disease (CVD) in diabetic patients [8], and NT-proBNP may also be useful in monitoring diabetes-related micro- and macrovascular complications [9-10], but there are no studies on the association between NT-proBNP and CVD risk in different glucose metabolism states, and the association between NT-proBNP and CAP in the CHD population is also unknown. Therefore, the aim of this study was to compare the correlation between NT-proBNP and the risk of CAP in the Chinese population with CHD, and to confirm the relationship NT-proBNP for the development of CAP in different glucose metabolism conditions, age and gender. Methods Subjects We conducted a large, multicenter cohort study called Retrospective Cohort Study on Cohort Study on Treatment of Cardiovascular Diseases with Traditional Chinese Medicine (CSCD-TCM plus ) . During the study, we established a CAD database, which included 214,717 inpatients with CAD from 6 hospitals in Tianjin, including First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin Medical University General Hospital, Tianjin Hospital of ITCWM Nankai Hospital, Tianjin Chest Hospital, Tianjin Academy of Traditional Chinese Medicine Affiliated Hospital, Second Teaching Hospital of Tianjin University of Traditional Chinese Medicine from 1 January 2014 to 30 June 2022. Patients with oncological, infectious, or serious liver or renal diseases or lacking data on fasting blood glucose (FBG) and carotid ultrasound measurements were excluded. Ultimately, 5,093 eligible subjects were enrolled in the final analysis. The flow chart of patient recruitment is shown in Fig 1. Data collection The following data for the analysis of this study were obtained from the CAD database, data that came from the medical records: clinical history, anthropometric data, blood analysis, and medical imaging data. Anthropometric data, including blood pressure, and personal information such as age, sex, smoking status, drinking status, current antihypertensive treatment, and current antilipid treatment were recorded. Fasting venous blood samples were obtained from all subjects on the second day of hospitalization. Carotid artery ultrasound examinations were performed by a certified professional technician using a diagnostic ultrasound system. In B-mode imaging, the common carotid artery, internal carotid artery, and carotid artery bifurcation were scanned and visualized. The carotid artery intima-media thickness (CIMT) was determined as the average of the IMT of the left and right common carotid arteries [11]. The professional physicians analyzed the color of the carotid artery based on the Doppler ultrasound results and recorded the number of CAPs and ultrasonographic features. CAP cases were divided into single (n = 1) and multiple (n ≥ 2). The echogenic properties of CAP were categorized as hypoechogenic, isoechogenic, hyperechogenic, and mixed types. Strict quality control procedures were used in this study to maintain consistency in the acquisition and analysis of monitoring and test images. Interlaboratory quality was assessed by licensed experimenters [12]. Definitions Smokers smoke at least 100 cigarettes in their lifetime [13]. Drinkers are defined as consuming alcohol at least 1 time per week [14]. Hypertension is defined as systolic blood pressure (SBP) ≥ 140 mmHg and/or diastolic blood pressure (DBP) ≥ 90 mmHg or current use of antihypertensive medication [15]. Diabetic status includes normoglycemia (NG) (FBG5.6mmol/L < 5.7%), Pre-DM (5.6 ≤FBG ≤6.9mmol/L), DM (FBG ≥ 7.0mmol/L) [16]. Statistical analysis The NT-proBNP terms are T1 (NT-proBNP 480ng/L). The Kolmogorov-Smirnov test was used to check the data for normality. Demographic differences between groups were assessed using the Kruskal-Wallis H test. Logistic regression models, estimated using odds ratios (ORs) and 95% confidence intervals (95% CIs), were used to examine the association of NT-proBNP with CAP. Two-sided P < 0.05 was considered statistically significant. Collinearity of the different models was tested before logistic regression. Diagnostic value of NT-proBNP measurement using ROC curves for the risk of developing CAP. Missing values were imputed using the multiple imputation method. All statistical analyses were performed using the Statistical Package for the Social Sciences, version 24.0 (IBM Corp, New York, NY, USA). Results Subject Characteristics A total of 5,093 participants were included in this study, of whom 2,490 (49.0%) were female and 2,597 (51.0%) were male, with a median age of 65 years, and 3,632 (71.3%) were over the age of 60 years. 1,807 (35.5%), 1,325 (26.0%), and 1,961 (38.5%) were NG, pre-DM, and DM, respectively. Subjects were divided into three groups according to the tertile level of the NT-proBNP index including T1, T2 and T3 group. Compared to T1 group, the distribution of FBG and HbA1c levels was higher in T2 and T3 group. CAP incidence (88.4%) was highest in the T3 group. Baseline characteristics of participants according to NT-proBNP are shown in Table 1. Association between the NT-proBNP and the risk of carotid artery plaques As shown in Table 2, multivariate logistic regression analyses showed that, when NT-proBNP was used as a continuous variable, it was strongly associated with the risk of CAP development (OR: 1.50; 95% CI: 1.18-1.91). In the unadjusted model, when T1 was used as the reference, NT-proBNP levels at T2 and T3 were associated with an increased OR for CAP, with the highest correlation in T3 group (OR: 2.50; 95% CI: 2.08-3.01). Adjustment for potential confounders in models b and c showed that, when T1 was used as the reference, NT-proBNP levels at T3 were associated with an increased OR for CAP (OR: 1.58; 95% CI: 1.29-1.92), (OR: 1.69; 95% CI: 1.37-2.10). When NT-proBNP was used as a continuous variable, the NT-proBNP index (tertiles) was consistent with P for trend with CAP ( P <0.001). As shown in Table 3 and Table 4, after correcting for potential confounders, this association was equally significant across sex and age groups. Using T1 as a reference, the risk of CAP development was significantly associated with NT-proBNP at the level as T3, in both males and females, or in patients ≤60 and >60 years of age. Association between the NT-proBNP and the risk of carotid artery plaques according to glucose regulation state As shown in Table 5, after multivariate adjustment. NT-proBNP as a continuous variable was correlated with CAP occurrence (OR: 1.98; 95% CI: 1.24-2.14). Using T1 as a reference, T3 was significantly associated with an increased risk of CAP in both the pre-DM and DM states, which with the highest OR in the DM state (OR: 1.84; 95% CI: 1.26-2.69). As shown in Table 6, after adjusting for possible confounders, in male CHD patients with NG status, NT-proBNP is associated with the risk of CAP development (OR: 2.06; 95% CI: 1.16-3.67). Using T1 as a reference, T3 was associated with the risk of developing CAP in men with DM state in men (OR: 1.92; 95% CI: 1.12-3.30). In contrast, women in the pre-DM state had a significantly increased risk of CAP at T3 (OR: 1.94; 95% CI: 1.12-3.40). As shown in Table 7, after adjusting for possible confounders, in CHD patients ≤60 years of age, NT-proBNP in the DM state was associated with the occurrence of CAP (OR: 2.94; 95% CI: 1.04-8.28), and conversely, in CHD patients >60 years of age, the correlation between continuous NT-proBNP and CAP was statistically significant in the NG and pre-DM states (OR: 2.52; 95% CI. 95% CI: 1.14-5.59), (OR: 7.04; 95% CI: 1.02-25.77). T3 was associated with the risk of developing CAP in CHD patients ≤60 years of age in the DM state, using T1 as a reference (OR: 2.01; 95% CI: 1.19-3.41). When CHD patients were >60 years of age in the both of pre-DM and DM state, there had a significantly increased risk of developing CAP with T3 (OR: 1.94; 95% CI: 1.12-3.40), (OR: 1.81; 95% CI: 1.05-3.13). Diagnostic value of NT-proBNP in the risk of carotid artery plaques As shown in Figure 2, we measured the diagnostic accuracy of CAP occurrence in NT-proBNP in CHD patients. The critical values were 182ng/l, with an AUC value of 0.627(95% CI: 0.592-0.631), sensitivity 50.7%, specificity 68.0%. Association between the NT-proBNP and the echo property and number of carotid artery plaques As shown in Supplementary material, when NT-proBNP was used as a continuous variable, it was strongly associated with multiple CAP. When T1 was used as a reference, T3 levels of NT-proBNP were associated with the development of ≥2 numbers of CAP. As shown in Supplementary Table 2, NT-proBNP was more likely to develop hypoechoic plaques and mixed plaques at the T1 level, whereas NT-proBNP was more likely to develop hypoechoic plaques at the T3 level. Discussion This study demonstrates the relationship between NT-proBNP and CAP in Chinese CHD patients with different glucose metabolic states. The association was also evaluated according to gender, age and glucose metabolism status. NT-proBNP was also measured to have some diagnostic significance for CAP. Myocardial NT-proBNP synthesis and release is a counterregulatory response to increased myocardial wall stress, sympathetic nerve tension and vasoconstriction, acting in different systems and affecting different biological processes [17]. NT-proBNP and its receptors not only coordinate cardiovascular homeostasis and health by regulating blood pressure, blood volume and sodium balance, but are also involved in glucose and lipid metabolism in adipose and muscle tissues [18], and NT-proBNP has been shown to make an additional contribution to the prediction of long-term CHD risk compared with conventional risk factors, including smoking, alcohol consumption, and hypertension [19], but is also strongly associated with the risk of DM [20], and in addition, NT-proBNP levels are significantly associated with adverse cardiovascular events in patients with chronic CHD in the pre-diabetic state [21]. In the present study, we found that whether NT-proBNP was used as a continuous or categorical variable, its elevation was associated with the risk of CHD, and it also had some validity to be used as a diagnostic marker of CHD occurrence, which may be related to the close association between NT-proBNP and endothelial dysfunction: The biological action of the natriuretic peptides represented by NT-proBNP is mediated through the guanylate cyclase (GC) receptor, which is present in many tissues, including vascular endothelium and smooth muscle. Binding of NT-proBNP to the GC receptor increases cyclic guanosine monophosphate (cGMP), whose activation of protein kinase G phosphorylation is involved in smooth muscle contraction [22]; secondly, it has been shown to induce nitric oxide (NO) synthase, leading to higher levels of NO, a potent vasodilator and marker of endothelial function [23]; furthermore, another possible mechanism is that it may inhibit the production and secretion of endothelin-1, a potent vasoconstrictor [24]. NT-proBNP is also a good predictor of CHD outcome in certain patient groups. For example, E. Cosson et al. found that in diabetic patients NT-proBNP is a useful biomarker in the diagnosis of CHD [25]. Given the increasing prevalence of atrial disease and DM and the increasing cardiovascular burden [26-27], there is growing interest in thoroughly investigating patients with impaired glucose metabolism and exploring more valuable prognostic biomarkers. Therefore, the present study for the first time examined the relationship between NT-proBNP and CAP in different glucose metabolism states and found that, when used as a continuous variable, this relationship existed only in the NG state, and when divided into tertiles, it was found to be significant in both the pre-DM and DM states, with the 480 ng/L group had a significantly higher risk of developing CAD, possibly arising from the possible presence of elevated NT-proBNP as a protective factor against DM, which has been shown in previous studies where NT-proBNP concentration was negatively associated with future risk of DM in a population-based cohort study [28]. Similar observations have been described in the Community Atherosclerosis Risk Study, where individuals with low NT-proBNP levels had a significantly higher risk of diabetes [29]. Therefore, it is possible that NT-proBNP in the NG state patients in the present study had high levels and was more strongly associated with carotid atherosclerosis. Whereas when low NT-proBNP levels were used as a reference, high NT-proBNP and high blood glucose acted together to induce inflammation, oxidative stress, and metabolic changes, and inflammation led to vascular endothelial damage and increased plaque incidence. In addition, Dr. Margaret M. Redfield states in a 2002 study. [30] that the concentration of NT-proBNP increases gradually with age, and is significantly higher in women than in men; therefore, given that the interpretation of NT-proBNP must account for specific differences in age and sex, the present study compared the association of NT-proBNP with CAP, as well as its association under different glucose metabolism, according to sex and age, and found that this association was most significant in the DM state in men and the pre-DM state in women, and significant in the DM state in patients ≤60 years and the pre-DM state and DM in patients >60 years. This result may be attributable to differences in endothelin and angiotensin-converting enzyme activity due to sex-dependent hormonal status and structural changes in the heart due to advancing age. The nature and quantity of plaques determine the process of rupture or erosion, which can cause adverse cardiovascular events in a short period of time [31-32]. Therefore, detection of the change in NT-proBNP level and atherosclerotic plaque characteristics in patients with CHD may have important clinical significance. In this study, a high NT-proBNP level was found to be associated with the formation of multiple CAPs, and the corresponding plaque property was more likely to be hyperechogenic plaque, which is consistent with the previously found high NT-proBNP level and a large number of calcified plaques [33]. Hyperechogenic plaques usually exhibit calcification characteristics, and the presence of multiple plaques may indicate more severe luminal stenosis. Calcified plaques often coexist with more severe luminal stenosis in the middle and late stages of the disease [34-35] and are associated with inflammatory processes. Once plaque rupture or erosion occurs, the extent of myocardial infarction may be more severe compared to early disease with non-calcified plaques. Therefore, factors such as NT-proBNP, which can predict and diagnose the risk of CAD, should be taken seriously by researchers. Strengths and limitations This study has some strengths. First, to our knowledge, this is the largest population-based study of the association of NT-proBNP with CAP in patients with CHD. The study also validated this association in different glucose metabolism, age, and sex to examine the characteristics of different populations. Second, the analyses also included possible confounders to exclude their interference with the results. In addition, we investigated the diagnostic value of NT-proBNP for CAP, which may be useful to expand the clinical application of this marker in relation to atherosclerosis. However, this study has some limitations. The most important of these is that, as an observational study, this study is not suitable for investigating the causal relationship between NT-proBNP and CAP. Second, because of missing data, currently used glucose-lowering medications and body mass index (BMI), as important confounders, were not included in the regression model. Finally, as this is a multicentre study, there may be some unavoidable between-centre bias. Conclusions NT-proBNP is closely related to the risk of CAP formation in different glucose metabolic states patients with CHD, which is also significant in gender and age groups, especially in men with DM and women with pre-DM, or in patients ≤ 60 years old with DM status and patients >60 years old with pre-DM and DM status. High levels of NT-proBNP have a stronger correlation with high echogenicity and multiple CAP. The use of NT-proBNP in the diagnosis of CAP in CHD patients has a certain validity, and NT-proBNP may be considered as a risk factor for clinical use in measuring the occurrence of CAP, especially for patients with abnormal glucose metabolism. Abbreviations CHD, coronary heart disease DM, diabetes mellitus MI, myocardial infarction CAP, carotid artery plaque NT-proBNP, Circulating N-terminal pro B-type natriuretic peptide BNP, brain natriuretic peptide CAD, cardiovascular disease FBG, fasting blood glucose CIMT, carotid intima-media thickness SBP, systolic blood pressure DBP, diastolic blood pressure NG, normoglycemic ORs, odds ratios CI: confidence intervals GC, guanylate cyclase cGMP, cyclic guanosine monophosphate NO, nitric oxide BMI, body mass index Declarations Ethics approval and consent to participate The study was approved by the Ethics Committee of Tianjin University of Traditional Chinese Medicine (approval number TJUTCM-EC20210007) and certified by the Chinese Clinical Trials Registry on April 4, 2022 (registration number ChiCTR2200058296) and on March 25, 2022 by ClinicalTrials.gov (registration number NCT05309343). Consent for publication Not applicable. Availability of data and materials The datasets used and/or analyzed in the current study are available from the corresponding author upon reasonable request. Competing interests The authors declare no competing interests. Funding This study was supported by the National Natural Science Foundation of China (82074140, 82104565, 82204142), Tianjin Hongrentang Pharmaceutical Co., Ltd., Tianjin, China (No. HX2020-16), Shanghai Hutchison Pharmaceuticals Ltd. (No. HX2020-39). Authors’ contributions Tong Yang, Hongmei Zheng, Fengmin Liu designed the study, performed the experiments, analysed the data and wrote the manuscript, performed the experiments and edited the manuscript. Ruiying Guo, Guangwei Pan, Shengyuan Liu, Shuang Tao analysed the data and edited the manuscript. Lin Li, Rongrong Yang, Chunquan Yu provided patient samples, designed the study, analysed the data, provided funding and edited the manuscript. All authors reviewed the manuscript. Acknowledgments All authors would like to thank all collaborators of this study. References Virani SS, Newby LK, Arnold SV, Bittner V, Brewer LC, Demeter SH, Dixon DL, Fearon WF, Hess B, Johnson HM, Kazi DS, Kolte D, Kumbhani DJ, LoFaso J, Mahtta D, Mark DB, Minissian M, Navar AM, Patel AR, Piano MR, Rodriguez F, Talbot AW, Taqueti VR, Thomas RJ, van Diepen S, Wiggins B, Williams MS. 2023 AHA/ACC/ACCP/ASPC/NLA/PCNA Guideline for the Management of Patients With Chronic Coronary Disease: A Report of the American Heart Association/American College of Cardiology Joint Committee on Clinical Practice Guidelines. Circulation. 2023 Jul 20. doi: 10.1161/CIR.0000000000001168. Epub ahead of print. PMID: 37471501. Theofilis P, Oikonomou E, Chasikidis C, Tsioufis K, Tousoulis D. Pathophysiology of Acute Coronary Syndromes-Diagnostic and Treatment Considerations. Life (Basel). 2023 Jul 12;13(7):1543. doi: 10.3390/life13071543. PMID: 37511918; PMCID: PMC10381786. El-Andari R, Bozso SJ, Fialka NM, Kang JJH, Nagendran J, Nagendran J. Coronary Revascularization for Patients with Diabetes Mellitus: A Contemporary Systematic Review and Meta-Analysis. Ann Surg. 2022 Jun 1;275(6):1058-1066. doi: 10.1097/SLA.0000000000005391. Epub 2022 Jan 25. PMID: 35081569. Xu W, Tian M, Zhou Y. The relationship between insulin resistance, adiponectin and C-reactive protein and vascular endothelial injury in diabetic patients with coronary heart disease. Exp Ther Med. 2018 Sep;16(3):2022-2026. doi: 10.3892/etm.2018.6407. Epub 2018 Jul 4. PMID: 30186434; PMCID: PMC6122372. Polak JF, Tracy R, Harrington A, Zavodni AE, O'Leary DH. Carotid artery plaque and progression of coronary artery calcium: the multi-ethnic study of atherosclerosis. J Am Soc Echocardiogr. 2013 May;26(5):548-55. doi: 10.1016/j.echo.2013.02.009. Epub 2013 Mar 21. PMID: 23522805; PMCID: PMC4084492. Xu R, Zhang T, Wan Y, Fan Z, Gao X. Prospective study of hemoglobin A1c and incident carotid artery plaque in Chinese adults without diabetes. Cardiovasc Diabetol. 2019 Nov 14;18(1):153. doi: 10.1186/s12933-019-0963-5. PMID: 31727070; PMCID: PMC6857319. Oremus M, McKelvie R, Don-Wauchope A, Santaguida PL, Ali U, Balion C, Hill S, Booth R, Brown JA, Bustamam A, Sohel N, Raina P. A systematic review of BNP and NT-proBNP in the management of heart failure: overview and methods. Heart Fail Rev. 2014 Aug;19(4):413-9. doi: 10.1007/s10741-014-9440-0. Erratum in: Heart Fail Rev. 2014 Aug;19(4):565. PMID: 24953975. Wijkman MO, Claggett BL, Malachias MVB, Vaduganathan M, Ballantyne CM, Kitzman DW, Mosley T, Matsushita K, Solomon SD, Pfeffer MA. Importance of NT-proBNP and conventional risk factors for prediction of death in older adults with and without diabetes mellitus- A report from the Atherosclerosis Risk in Communities (ARIC) study. Diabetes Res Clin Pract. 2022 Dec;194:110164. doi: 10.1016/j.diabres.2022.110164. Epub 2022 Nov 19. PMID: 36410558. Birukov A, Eichelmann F, Kuxhaus O, Polemiti E, Fritsche A, Wirth J, Boeing H, Weikert C, Schulze MB. Opposing Associations of NT-proBNP With Risks of Diabetes and Diabetes-Related Complications. Diabetes Care. 2020 Dec;43(12):2930-2937. doi: 10.2337/dc20-0553. Epub 2020 Aug 17. PMID: 32816995; PMCID: PMC7770272. Welsh P, Woodward M, Hillis GS, Li Q, Marre M, Williams B, Poulter N, Ryan L, Harrap S, Patel A, Chalmers J, Sattar N. Do cardiac biomarkers NT-proBNP and hsTnT predict microvascular events in patients with type 2 diabetes? Results from the ADVANCE trial. Diabetes Care. 2014 Aug;37(8):2202-10. doi: 10.2337/dc13-2625. Epub 2014 May 30. PMID: 24879844. Liu Y, Zhu Y, Jia W, Sun D, Zhao L, Zhang C, Wang C, Chen G, Fu S, Bo Y, Xing Y. Association between lipid profiles and presence of carotid plaque. Sci Rep. 2019 Nov 29;9(1):18011. doi: 10.1038/s41598-019-54285-w. PMID: 31784590; PMCID: PMC6884522. Li Z, He Y, Wang S, Li L, Yang R, Liu Y, Cheng Q, Yu L, Zheng Y, Zheng H, Gao S, Yu C. Association between triglyceride glucose index and carotid artery plaque in different glucose metabolic states in patients with coronary heart disease: a RCSCD-TCM study in China. Cardiovasc Diabetol. 2022 Mar 11;21(1):38. doi: 10.1186/s12933-022-01470-3. PMID: 35277186; PMCID: PMC8917731. Barua RS, Rigotti NA, Benowitz NL, Cummings KM, Jazayeri MA, Morris PB, Ratchford EV, Sarna L, Stecker EC, Wiggins BS. 2018 ACC Expert Consensus Decision Pathway on Tobacco Cessation Treatment: A Report of the American College of Cardiology Task Force on Clinical Expert Consensus Documents. J Am Coll Cardiol. 2018 Dec 25;72(25):3332-3365. doi: 10.1016/j.jacc.2018.10.027. Epub 2018 Dec 5. PMID: 30527452. Ng R, Sutradhar R, Yao Z, Wodchis WP, Rosella LC. Smoking, drinking, diet and physical activity-modifiable lifestyle risk factors and their associations with age to first chronic disease. Int J Epidemiol. 2020 Feb 1;49(1):113-130. doi: 10.1093/ije/dyz078. PMID: 31329872; PMCID: PMC7124486. Al-Makki A, DiPette D, Whelton PK, Murad MH, Mustafa RA, Acharya S, Beheiry HM, Champagne B, Connell K, Cooney MT, Ezeigwe N, Gaziano TA, Gidio A, Lopez-Jaramillo P, Khan UI, Kumarapeli V, Moran AE, Silwimba MM, Rayner B, Sukonthasan A, Yu J, Saraffzadegan N, Reddy KS, Khan T. Hypertension Pharmacological Treatment in Adults: A World Health Organization Guideline Executive Summary. Hypertension. 2022 Jan;79(1):293-301. doi: 10.1161/HYPERTENSIONAHA.121.18192. Epub 2021 Nov 15. Medina-Chávez JH, Vázquez-Parrodi M, Mendoza-Martínez P, Ríos-Mejía ED, de Anda-Garay JC, Balandrán-Duarte DA. Protocolo de Atención Integral: prevención, diagnóstico y tratamiento de diabetes mellitus 2 [Integrated Care Protocol: Prevention, diagnosis and treatment of diabetes mellitus 2]. Rev Med Inst Mex Seguro Soc. 2022 Feb 7;60(Supl 1):S4-S18. Spanish. PMID: 35135039; PMCID: PMC10395976. Malachias MVB, Wijkman MO, Bertoluci MC. NT-proBNP as a predictor of death and cardiovascular events in patients with type 2 diabetes. Diabetol Metab Syndr. 2022 May 3;14(1):64. doi: 10.1186/s13098-022-00837-6. PMID: 35501909; PMCID: PMC9063067. Pandey KN. Molecular Signaling Mechanisms and Function of Natriuretic Peptide Receptor-A in the Pathophysiology of Cardiovascular Homeostasis. Front Physiol. 2021 Aug 19;12:693099. doi: 10.3389/fphys.2021.693099. PMID: 34489721; PMCID: PMC8416980. Natriuretic Peptides Studies Collaboration, Willeit P, Kaptoge S, Welsh P, Butterworth AS, Chowdhury R, Spackman SA, Pennells L, Gao P, Burgess S, Freitag DF, Sweeting M, Wood AM, Cook NR, Judd S, Trompet S, Nambi V, Olsen MH, Everett BM, Kee F, Ärnlöv J, Salomaa V, Levy D, Kauhanen J, Laukkanen JA, Kavousi M, Ninomiya T, Casas JP, Daniels LB, Lind L, Kistorp CN, Rosenberg J, Mueller T, Rubattu S, Panagiotakos DB, Franco OH, de Lemos JA, Luchner A, Kizer JR, Kiechl S, Salonen JT, Goya Wannamethee S, de Boer RA, Nordestgaard BG, Andersson J, Jørgensen T, Melander O, Ballantyne ChM, DeFilippi Ch, Ridker PM, Cushman M, Rosamond WD, Thompson SG, Gudnason V, Sattar N, Danesh J, Di Angelantonio E. Natriuretic peptides and integrated risk assessment for cardiovascular disease: an individual-participant-data meta-analysis. Lancet Diabetes Endocrinol. 2016 Oct;4(10):840-9. doi: 10.1016/S2213-8587(16)30196-6. Epub 2016 Sep 3. PMID: 27599814; PMCID: PMC5035346. Kerkelä R, Ulvila J, Magga J. Natriuretic Peptides in the Regulation of Cardiovascular Physiology and Metabolic Events. J Am Heart Assoc. 2015 Oct 27;4(10):e002423. doi: 10.1161/JAHA.115.002423. PMID: 26508744; PMCID: PMC4845118. Liu HH, Cao YX, Jin JL, Guo YL, Zhu CG, Wu NQ, Gao Y, Zhang Y, Xu RX, Dong Q, Li JJ. Prognostic value of NT-proBNP in patients with chronic coronary syndrome and normal left ventricular systolic function according to glucose status: a prospective cohort study. Cardiovasc Diabetol. 2021 Apr 22;20(1):84. doi: 10.1186/s12933-021-01271-0. PMID: 33888145; PMCID: PMC8063320. Gupta DK, Wang TJ. Natriuretic Peptides and Cardiometabolic Health. Circ J. 2015;79(8):1647-55. doi: 10.1253/circj.CJ-15-0589. Epub 2015 Jun 23. PMID: 26103984; PMCID: PMC4893202. Costa MA, Arranz CT. New aspects of the interactions between the cardiovascular nitric oxide system and natriuretic peptides. Biochem Biophys Res Commun. 2011 Mar 11;406(2):161-4. doi: 10.1016/j.bbrc.2011.02.044. Epub 2011 Feb 15. PMID: 21329665. Del Ry S, Andreassi MG, Clerico A, Biagini A, Giannessi D. Endothelin-1, endothelin-1 receptors and cardiac natriuretic peptides in failing human heart. Life Sci. 2001 May 4;68(24):2715-30. doi: 10.1016/s0024-3205(01)01076-1. PMID: 11400914. Cosson E, Nguyen MT, Pham I, Pontet M, Nitenberg A, Valensi P. N-terminal pro-B-type natriuretic peptide: an independent marker for coronary artery disease in asymptomatic diabetic patients. Diabet Med. 2009 Sep;26(9):872-9. doi: 10.1111/j.1464-5491.2009.02788.x. PMID: 19719707. Cosentino F, Grant PJ, Aboyans V, Bailey CJ, Ceriello A, Delgado V, Federici M, Filippatos G, Grobbee DE, Hansen TB, Huikuri HV, Johansson I, Jüni P, Lettino M, Marx N, Mellbin LG, Östgren CJ, Rocca B, Roffi M, Sattar N, Seferović PM, Sousa-Uva M, Valensi P, Wheeler DC; ESC Scientific Document Group. 2019 ESC Guidelines on diabetes, pre-diabetes, and cardiovascular diseases developed in collaboration with the EASD. Eur Heart J. 2020 Jan 7;41(2):255-323. doi: 10.1093/eurheartj/ehz486. Erratum in: Eur Heart J. 2020 Dec 1;41(45):4317. PMID: 31497854. Liu HH, Cao YX, Li S, Guo YL, Zhu CG, Wu NQ, Gao Y, Dong QT, Zhao X, Zhang Y, Sun D, Li JJ. Impacts of Prediabetes Mellitus Alone or Plus Hypertension on the Coronary Severity and Cardiovascular Outcomes. Hypertension. 2018 Jun;71(6):1039-1046. doi: 10.1161/HYPERTENSIONAHA.118.11063. Epub 2018 Apr 18. PMID: 29669793. Birukov A, Eichelmann F, Kuxhaus O, Polemiti E, Fritsche A, Wirth J, Boeing H, Weikert C, Schulze MB. Opposing Associations of NT-proBNP With Risks of Diabetes and Diabetes-Related Complications. Diabetes Care. 2020 Dec;43(12):2930-2937. doi: 10.2337/dc20-0553. Epub 2020 Aug 17. PMID: 32816995; PMCID: PMC7770272. Lazo M, Young JH, Brancati FL, Coresh J, Whelton S, Ndumele CE, Hoogeveen R, Ballantyne CM, Selvin E. NH2-terminal pro-brain natriuretic peptide and risk of diabetes. Diabetes. 2013 Sep;62(9):3189-93. doi: 10.2337/db13-0478. Epub 2013 Jun 3. PMID: 23733199; PMCID: PMC3749338. Redfield MM, Rodeheffer RJ, Jacobsen SJ, Mahoney DW, Bailey KR, Burnett JC Jr. Plasma brain natriuretic peptide concentration: impact of age and gender. J Am Coll Cardiol. 2002 Sep 4;40(5):976-82. doi: 10.1016/s0735-1097(02)02059-4. PMID: 12225726. Nurmohamed NS, Bom MJ, Jukema RA, de Groot RJ, Driessen RS, van Diemen PA, de Winter RW, Gaillard EL, Sprengers RW, Stroes ESG, Min JK, Earls JP, Cardoso R, Blankstein R, Danad I, Choi AD, Knaapen P. AI-Guided Quantitative Plaque Staging Predicts Long-Term Cardiovascular Outcomes in Patients at Risk for Atherosclerotic CVD. JACC Cardiovasc Imaging. 2023 Jul 7:S1936-878X(23)00277-2. doi: 10.1016/j.jcmg.2023.05.020. Epub ahead of print. PMID: 37480907. Föllmer B, Williams MC, Dey D, Arbab-Zadeh A, Maurovich-Horvat P, Volleberg RHJA, Rueckert D, Schnabel JA, Newby DE, Dweck MR, Guagliumi G, Falk V, Vázquez Mézquita AJ, Biavati F, Išgum I, Dewey M. Roadmap on the use of artificial intelligence for imaging of vulnerable atherosclerotic plaque in coronary arteries. Nat Rev Cardiol. 2023 Jul 18. doi: 10.1038/s41569-023-00900-3. Epub ahead of print. PMID: 37464183. Gan L, Feng C, Liu C, Tian S, Song X, Yang L. Association between serum N-terminal pro-B-type natriuretic peptide levels and characteristics of coronary atherosclerotic plaque detected by coronary computed tomography angiography. Exp Ther Med. 2016 Aug;12(2):667-675. doi: 10.3892/etm.2016.3371. Epub 2016 May 19. PMID: 27446259; PMCID: PMC4950222. Salama RH, El-Moniem AE, El-Hefney N, Samor T. N-TerminaL PRO-BNP in Acute Coronary Syndrome Patients with ST Elevation Versus Non ST Elevation in Qassim Region of Saudi Arabia. Int J Health Sci (Qassim). 2011 Jul;5(2):136-45. PMID: 23267291; PMCID: PMC3521832. Min JK, Lin FY, Dunning AM, Delago A, Egan J, Shaw LJ, Berman DS, Callister TQ. Incremental prognostic significance of left ventricular dysfunction to coronary artery disease detection by 64-detector row coronary computed tomographic angiography for the prediction of all-cause mortality: results from a two-centre study of 5330 patients. Eur Heart J. 2010 May;31(10):1212-9. doi: 10.1093/eurheartj/ehq020. Epub 2010 Mar 2. PMID: 20197423. Tables Table 1 Baseline clinical characteristics according to NT-proBNP Characteristics Total T1 N =1,687 T2 N =1,710 T3 N =1,696 P- value Age 60 3632(71.3) 987(58.5) 1320(77.2) 1325(78.1) Sex <0.001 Male 2597(51.0) 867(51.4) 775(45.2) 955(56.3) Female 2490(49.0) 820(48.6) 935(54.7) 741(43.7) SBP (mmHg) 140.0(130.0,160.0) 140.0(130.0,160.0) 143.0(130.0,160.0) 140.0(127.0,160.0) 0.061 DBP (mmHg) 81.0(80.0,90.0) 85.0(80.0,93.0) 80.0(80.0,90.0) 80.0(76.0,90.0) <0.001 Drinking (%) 1242(24.4) 459(27.2) 398(23.3) 385(22.7) 0.004 Smoking (%) 2105(41.3) 728(43.2) 687(40.2) 690(40.7) 0.170 Hypertension (%) 3915(76.9) 1336(79.2) 1372(80.2) 1207(71.2) <0.001 Hyperlipidemia (%) 902(17.7) 402(23.8) 324(18.9) 176(10.4) <0.001 HbA1c (%) 6.1(5.6,7.1) 6.0(5.5,6.9) 6.1(5.6,7.1) 6.3(5.6,7.5) <0.001 FBG (mmol/L) 6.2(5.2,8.3) 6.3(5.3,8.3) 6.2(5.2,8.1) 6.4(5.2,8.7) 0.019 Glucose regulation state (%) 0.021 NG 1807(35.5) 583(34.6) 647(37.8) 577(34.0) Pre-DM 1325(26.0) 472(28.0) 427(25.0) 426(25.1) DM 1961(38.5) 632(37.5) 636(37.2) 693(40.9) Current antihypertensive medication (%) 3126(61.4) 1012(60.0) 1069(62.5) 1045(61.6) 0.309 Current antilipidemic medication (%) 3808(74.8) 1282(76.0) 1305(76.3) 1221(72.0) 0.005 CIMT (mm) 0.1(0.09,0.1) 0.1(0.08,0.1) 0.1(0.09,0.11) 0.1(0.09,0.11) <0.001 Carotid artery plaque (%) 4177(82.0) 1270(75.3) 1408(82.3) 1499(88.4) <0.001 Number of carotid artery plaque (%) <0.001 0 916(18.0) 417(24.7) 302(17.7) 197(11.6) 1 399(7.8) 160(9.5) 146(8.5) 93(5.5) ≥2 3778(74.2) 1110(65.8) 1262(73.8) 1406(82.9) Carotid artery plaque echo property (%) <0.001 Hypoechoic plaque 192(3.8) 86(5.1) 49(2.9) 57(3.4) Isoechoic plaque 379(7.4) 148(8.8) 128(7.5) 103(6.1) Hyperechoic plaque 2413(47.4) 691(41.0) 809(47.3) 913(53.8) Mixture plaque 1200(23.6) 348(20.6) 423(24.7) 429(25.3) Data are presented as median (interquartile) or number (proportion, %) SBP, systolic blood pressure; DBP, diastolic blood pressure; HbA1c, glycated hemoglobin; FBG, fasting blood glucose; NG, normoglycemic; Pre-DM, pre-diabetes; DM, diabetes; CIMT, carotid intima-media thickness Table 2 Association between the NT-proBNP and the risk of carotid artery plaque Variables Carotid artery plaques OR (95% CI) a P- value OR (95% CI) b P- value OR (95% CI) c P- value NT-proBNP Total 1.76(1.42-2.17) <0.001 1.38(1.14-1.68) 0.001 1.50(1.18-1.91) 0.001 T1 Reference Reference Reference T2 1.53(1.30-1.81) <0.001 1.10(0.92-1.32) 0.297 1.07(0.88-1.29) 0.522 T3 2.50(2.08-3.01) <0.001 1.58(1.29-1.92) <0.001 1.69(1.37-2.10) <0.001 P trend <0.001 <0.001 <0.001 a Model 1: unadjusted b Model 2: adjusted for age, sex c Model 3: adjusted for age, sex, SBP, DBP, smoking, drinking, hypertension, hyperlipidemia, use of antihypertensives, and use of antilipidemic Table 3 Association between the NT-proBNP and the risk of carotid artery plaque according to sex Variables Carotid artery plaques OR (95% CI) a P- value OR (95% CI) b P- value OR (95% CI) c P- value NT-proBNP Males Total 1.26(1.03-1.55) 0.023 1.10(0.92-1.31) 0.316 1.26(0.96-1.66) 0.098 T1 Reference Reference Reference T2 1.48(1.13-1.94) 0.004 1.11(0.83-1.47) 0.488 1.04(0.77-1.41) 0.779 T3 2.12(1.62-2.80) <0.001 1.49(1.12-1.99) 0.007 1.68(1.22-2.30) 0.001 P trend <0.001 0.007 0.001 Females Total 3.04(1.96-4.72) <0.001 1.12(1.11-1.14) <0.001 1.91(1.26-2.90) 0.002 T1 Reference Reference Reference T2 1.68(1.25-2.08) <0.001 1.08(0.86-1.37) 0.508 1.06(0.83-1.37) 0.634 T3 2.76(2.14-3.57) <0.001 1.62(1.23-2.14) 0.001 1.68(1.25-2.26) 0.001 P trend <0.001 <0.001 <0.001 a Model 1: unadjusted b Model 2: adjusted for age c Model 3: adjusted for age, SBP, DBP, smoking, drinking, hypertension, hyperlipidemia, use of antihypertensives, and use of antilipidemic Table 4 Association between the NT-proBNP and the risk of carotid artery plaque according to age Variables Carotid artery plaques OR (95% CI) a P- value OR (95% CI) b P- value OR (95% CI) c P- value NT-proBNP ≤60 Total 1.41(1.11-1.80) 0.005 1.41(1.10-1.81) 0.006 1.31(1.01-1.71) 0.042 T1 Reference Reference Reference T2 1.36(1.04-1.76) 0.023 1.32(1.01-1.72) 0.042 1.20(0.90-1.61) 0.233 T3 2.01(1.51-2.67) <0.001 1.87(1.40-2.49) <0.001 1.82(1.33-2.49) <0.001 P trend <0.001 <0.001 <0.001 ＞60 Total 2.11(1.50-2.98) <0.001 1.53(1.12-2.09) 0.008 1.74(1.17-2.60) 0.006 T1 Reference Reference Reference T2 1.12(0.89-1.42) 0.332 1.18(0.93.1.50) 0.170 1.10(0.85.1.32) 0.459 T3 1.97(1.51-2.56) <0.001 1.90(1.46-2.48) <0.001 1.94(1.45-2.59) <0.001 P trend <0.001 <0.001 <0.001 a Model 1: unadjusted b Model 2: adjusted for sex c Model 3: adjusted for sex, SBP, DBP, smoking, drinking, hypertension, hyperlipidemia, use of antihypertensives, and use of antilipidemic Table 5 Association between the NT-proBNP and the risk of carotid artery plaques according to glucose regulation state Variables Carotid artery plaques OR (95% CI) a P- value OR (95% CI) b P- value OR (95% CI) c P- value Normal glucose regulation Total 2.99(1.91-4.68) <0.001 2.98(2.29-3.87) <0.001 1.98(1.24-2.14) 0.004 T1 Reference Reference Reference T2 1.39(1.07-1.79) 0.012 1.00(0.75-1.32) 0.971 0.88(0.65-1.20) 0.431 T3 2.45(1.83-3.29) <0.001 1.43(1.03-1.97) 0.031 1.41(1.00-2.00) 0.054 P trend <0.001 0.015 0.011 Prediabetes Total 1.25(0.98-1.60) 0.070 1.02(1.83-1.25) 0.875 1.14(0.80-1.62) 0.467 T1 Reference Reference Reference T2 1.85(1.34-2.57) <0.001 1.26(0.88-1.79) 0.205 1.35(0.92-1.98) 0.121 T3 2.71(1.90-3.85) <0.001 1.48(1.10-2.20) 0.045 1.80(1.19-2.73) 0.005 P trend <0.001 0.089 0.015 Diabetes Total 1.63(1.13-2.35) 0.009 1.40(0.99-1.98) 0.057 1.48(0.96-2.27) 0.074 T1 Reference Reference Reference T2 1.551(1.14-2.12) 0.005 1.16(0.84-1.60) 0.376 1.12(0.79-1.58) 0.529 T3 2.34(1.68-3.25) <0.001 1.69(1.20-2.39) 0.003 1.84(1.26-2.69) 0.002 P trend <0.001 0.003 0.001 a Model 1: unadjusted b Model 2: adjusted for age, sex c Model 3: adjusted for age, sex, SBP, DBP, smoking, drinking, hypertension, hyperlipidemia, use of antihypertensives, and use of antilipidemic Table 6 Association between the NT-proBNP and the risk of carotid artery plaques according to different glucose regulation state and sex Variables Carotid artery plaques OR (95% CI) a P- value OR (95% CI) b P- value OR (95% CI) c P- value Male Normal glucose regulation Total 1.99(1.22-3.24) 0.006 1.74(1.08-2.80) 0.023 2.06(1.16-3.67) 0.014 T1 Reference Reference Reference T2 1.37(0.90-2.08) 0.144 0.93(0.59-1.47) 0.742 0.74(0.45-1.21) 0.230 T3 2.06(1.33-3.21) 0.001 1.35(0.82-2.20) 0.224 1.45(0.86-2.45) 0.164 P trend 0.004 0.131 0.040 Prediabetes Total 0.98(0.8-1.21) 0.864 0.83(0.67-1.03) 0.090 0.90(0.68-1.17) 0.420 T1 Reference Reference Reference T2 1.93(1.13-3.31) 0.017 1.26(0.71-2.23) 0.430 1.23(0.67-2.28) 0.502 T3 2.49(2.47-3.21) 0.001 1.47(0.84-2.58) 0.182 1.63(0.87-3.07) 0.130 P trend 0.007 0.255 0.167 Diabetes Total 1.26(0.86-1.83) 0.237 1.16(0.82-1.64) 0.409 1.28(0.75-2.18) 0.366 T1 Reference Reference Reference T2 1.41(0.88-2.24) 0.153 1.22(0.76-2.00) 0.421 1.20(0.72-2.10) 0.477 T3 1.98(2.24-3.16) 0.004 1.65(1.02-2.67) 0.043 1.92(1.12-3.30) 0.018 P trend 0.012 0.061 0.022 Female Normal glucose regulation Total 4.429(1.92-9.96) <0.001 2.60(1.29-4.27) 0.008 1.96(0.91-4.22) 0.085 T1 Reference Reference Reference T2 1.49(1.07-2.07) 0.018 1.04(0.73-1.49) 0.816 0.92(0.62-1.37) 0.677 T3 2.64(1.77-3.94) <0.001 1.50(0.97-2.32) 0.068 1.38(-.85-2.23) 0.191 P trend <0.001 P trend 0.053 P trend 0.090 Prediabetes Total 2.01(1.04-3.89) 0.038 1.79(0.98-3.25) 0.056 2.97(0.98-3.97) 0.058 T1 Reference Reference Reference T2 1.92(1.27-2.90) 0.002 1.25(0.80-1.97) 0.335 1.41(0.87-2.31) 0.166 T3 2.62(1.61-4.26) <0.001 1.51(0.87-2.56) 0.130 1.94(1.12-3.40) 0.021 P trend 0.002 0.197 0.046 Diabetes Total 2.71(1.29-5.71) 0.009 2.00(1.03-3.90) 0.042 1.68(0.83-3.43) 0.152 T1 Reference Reference Reference T2 1.79(1.19-2.70) 0.005 1.01(0.64-1.58) 0.979 0.94(0.58-1.53) 0.805 T3 2.76(1.74-4.38) <0.001 1.62(0.99-2.66) 0.056 1.60(0.93-2.74) 0.090 P trend <0.001 0.033 0.043 a Model 1: unadjusted b Model 2: adjusted for age c Model 3: adjusted for age, SBP, DBP, smoking, drinking, hypertension, hyperlipidemia, use of antihypertensives, and use of antilipidemic Table 7 Association between the NT-proBNP and the risk of carotid artery plaques according to different glucose regulation state and age Variables Carotid artery plaques OR (95% CI) a P- value OR (95% CI) b P- value OR (95% CI) c P- value ≤60 Normal glucose regulation Total 1.42(1.08-1.86) 0.012 1.34(1.03-1.75) 0.030 1.33(0.96-1.84) 0.085 T1 Reference Reference Reference T2 1.11(0.74-1.65) 0.621 1.08(0.72-1.62) 0.727 0.84(0.53-1.32) 0.447 T3 1.76(1.10-2.83) 0.020 1.54(0.95-2.51) 0.082 1.51(0.87-2.60) 0.141 P trend 0.021 0.082 0.075 Prediabetes Total 0.95(0.77-1.17) 0.637 0.94(0.77-1.16) 0.581 0.89(1.72-1.10) 0.279 T1 Reference Reference Reference T2 1.71(0.98-2.96) 0.058 1.81(1.02-3.20) 0.043 1.92(1.01-3.66) 0.046 T3 1.79(1.04-3.11) 0.037 1.62(0.92-2.86) 0.095 1.66(0.88-3.11) 0.117 P trend 0.093 0.224 0.389 Diabetes Total 3.41(1.30-8.93) 0.013 3.27(1.26-8.53) 0.015 2.94(1.04-8.28) 0.042 T1 Reference Reference Reference T2 1.66(1.02-2.68) 0.041 1.55(0.95-2.53) 0.078 1.50(0.88-2.57) 0.141 T3 2.23(1.38-3.60) 0.001 2.18(1.34-3.54) 0.002 2.01(1.19-3.41) 0.009 P trend 0.005 0.005 0.023 ＞60 Normal glucose regulation Total 2.71(1.79-7.74) <0.001 3.12(1.51-6.44) 0.002 2.52(1.14-5.59) 0.023 T1 Reference Reference Reference T2 1.16(0.80-1.67) 0.432 1.23(0.85-1.78) 0.280 1.10(0.74-1.65) 0.636 T3 1.94(1.30-2.90) 0.001 1.85(1.23-2.78) 0.003 1.75(1.12-2.73) 0.014 P trend 0.001 0.005 0.008 Prediabetes Total 1.61(0.97-2.68) 0.066 1.45(0.91-2.30) 0.121 7.04(1.92-25.77) 0.003 T1 Reference Reference Reference T2 1.24(0.80-1.93) 0.342 1.23(0.79-1.93) 0.359 1.28(0.80-2.10) 0.306 T3 2.33(1.40-3.87) 0.001 2.08(1.25-3.48) 0.005 2.46(1.40-4.32) 0.002 P trend 0.001 0.007 0.002 Diabetes Total 1.15(0.76-1.75) 0.501 1.18(0.86-1.63) 0.304 1.15(0.76-1.75) 0.051 T1 Reference Reference Reference T2 1.01(0.65-1.56) 0.979 1.05(0.67-1.63) 0.829 0.93(0.58-1.48) 0.746 T3 1.74(1.07-2.81) 0.025 1.75(1.08-2.83) 0.023 1.81(1.05-3.13) 0.033 P trend 0.009 0.012 0.009 a Model 1: unadjusted b Model 2: adjusted for sex c Model 3: adjusted for sex, SBP, DBP, smoking, drinking, hypertension, hyperlipidemia, use of antihypertensives, and use of antilipidemic Additional Declarations No competing interests reported. Supplementary Files Graphicalabstract.pdf Supplementarymaterial20230826.docx Cite Share Download PDF Status: Published Journal Publication published 02 Nov, 2023 Read the published version in Cardiovascular Diabetology → Version 1 posted Editorial decision: Major revision 21 Sep, 2023 Reviews received at journal 30 Aug, 2023 Reviewers agreed at journal 30 Aug, 2023 Reviewers invited by journal 30 Aug, 2023 Editor assigned by journal 28 Aug, 2023 Submission checks completed at journal 28 Aug, 2023 First submitted to journal 26 Aug, 2023 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3298912","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":229391540,"identity":"24fb9db7-3d5f-4940-bdb9-299cc8f4cfd4","order_by":0,"name":"Tong Yang","email":"","orcid":"","institution":"Tianjin University of Traditional Chinese Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Tong","middleName":"","lastName":"Yang","suffix":""},{"id":229391541,"identity":"17c58591-2771-4d52-ad10-ece14dab5351","order_by":1,"name":"Hongmei Zheng","email":"","orcid":"","institution":"Tianjin Medical University General Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Hongmei","middleName":"","lastName":"Zheng","suffix":""},{"id":229391542,"identity":"d620a62a-ebf1-420e-957b-ceaf9a635b2f","order_by":2,"name":"Fengmin Liu","email":"","orcid":"","institution":"Tianjin University of Traditional Chinese Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Fengmin","middleName":"","lastName":"Liu","suffix":""},{"id":229391543,"identity":"d9f567fc-98a3-476a-909b-b840c7762b33","order_by":3,"name":"Ruiying Guo","email":"","orcid":"","institution":"Tianjin University of Traditional Chinese Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ruiying","middleName":"","lastName":"Guo","suffix":""},{"id":229391544,"identity":"e2322b1e-71e1-4dd2-b63b-4c9866886a69","order_by":4,"name":"Guangwei Pan","email":"","orcid":"","institution":"Tianjin University of Traditional Chinese Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Guangwei","middleName":"","lastName":"Pan","suffix":""},{"id":229391545,"identity":"1414a4f8-dc76-4542-bc7b-62cf7b0d0462","order_by":5,"name":"Shengyuan Liu","email":"","orcid":"","institution":"Tianjin University of Traditional Chinese Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Shengyuan","middleName":"","lastName":"Liu","suffix":""},{"id":229391546,"identity":"441234bb-64d0-4a08-9c93-33272dfae08f","order_by":6,"name":"Shuang Tao","email":"","orcid":"","institution":"Tianjin University of Traditional Chinese Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Shuang","middleName":"","lastName":"Tao","suffix":""},{"id":229391547,"identity":"b0c90039-9479-40ab-8973-7fbe8124f85e","order_by":7,"name":"Lin Li","email":"","orcid":"","institution":"Tianjin University of Traditional Chinese Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Lin","middleName":"","lastName":"Li","suffix":""},{"id":229391548,"identity":"d96d1627-d5bd-42e6-9679-a32f079ed0fb","order_by":8,"name":"Rongrong Yang","email":"","orcid":"","institution":"Tianjin University of Traditional Chinese Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Rongrong","middleName":"","lastName":"Yang","suffix":""},{"id":229391549,"identity":"3dba2793-0d5e-4ea1-a96b-2d3c16858489","order_by":9,"name":"Chunquan Yu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3ElEQVRIiWNgGAWjYLACCRDB3vgARPHwEaGBsUGCwQCo9rABWAsbUVoYQFokksFaGAhqMTh+ePsDy7Y/8rozHzM+5s2xk2FjYH746AY+LWfSChsk2wwMt91OZjacuS0Z6DA2Y+McfFpu8BiCtCSY3c4/JvFxGzNQCw+bNHFabh5mk0jcVk+KlhvMbEBbDhPWIgn0ywyJc8aG286A/XKch42ZgF/4jh/e8FmiTE7e7PhhYIhtq7bnZ29++BifFoUDDAbMEihCzHiUg4B8A4MB4wcCikbBKBgFo2CEAwB56kS5sRONFgAAAABJRU5ErkJggg==","orcid":"","institution":"Tianjin University of Traditional Chinese Medicine","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Chunquan","middleName":"","lastName":"Yu","suffix":""}],"badges":[],"createdAt":"2023-08-26 16:14:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3298912/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3298912/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12933-023-02015-y","type":"published","date":"2023-11-02T15:01:34+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":42459416,"identity":"fe8cd10a-475e-42bb-ae8d-2621e6cc4c74","added_by":"auto","created_at":"2023-08-31 23:35:26","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":184926,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of patient recruitment\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-3298912/v1/e1e8a10e9c55d6077b1bee01.png"},{"id":42459413,"identity":"ecc4dbd2-602d-4aef-86e3-b7b507cf8f6f","added_by":"auto","created_at":"2023-08-31 23:35:26","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":122717,"visible":true,"origin":"","legend":"\u003cp\u003eDiagnostic value of NT-proBNP for the risk of carotid artery plaques in patients with CHD\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-3298912/v1/61aa9de2a573b2bda4f4ba03.png"},{"id":45942983,"identity":"046054e5-c446-4fa8-9394-0a8740100476","added_by":"auto","created_at":"2023-11-06 15:07:04","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":880312,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3298912/v1/912d4177-81d4-43cc-85f8-736814bffb69.pdf"},{"id":42459415,"identity":"bd10ccea-d3cc-4c26-8d38-0e7b1ec60e96","added_by":"auto","created_at":"2023-08-31 23:35:26","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":124009,"visible":true,"origin":"","legend":"","description":"","filename":"Graphicalabstract.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3298912/v1/b847da893a327317854d4121.pdf"},{"id":42459414,"identity":"a26d4528-b5ec-4755-847d-79f66ee99232","added_by":"auto","created_at":"2023-08-31 23:35:26","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":18448,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial20230826.docx","url":"https://assets-eu.researchsquare.com/files/rs-3298912/v1/920e349b09de88baa9b282a8.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Relationship between the circulating N-terminal pro B-type natriuretic peptide and the risk of carotid artery plaque in different glucose metabolic states in patients with coronary heart disease: a CSCD-TCM plus study in China","fulltext":[{"header":"Background","content":"\u003cp\u003eCoronary heart disease (CHD) is a chronic noncommunicable disease whose morbidity and mortality are increasing with each passing year, representing a major public health burden worldwide [1], and whose pathogenesis is closely linked to the presence of endothelial dysfunction [2]. On the other hand, diabetes mellitus (DM) is another important risk factor independent of traditional risk factors such as hypertension, hyperlipidemia and smoking, and is associated with a 2-3-fold increase in the incidence of CHD, myocardial infarction (MI) and CHD mortality [3]. In addition, CHD often occurs in combination with DM, possibly because risk factors for both include abnormal inflammatory response or abnormal lipid metabolism [4]. Early detection of atherosclerosis, especially when CHD patients are asymptomatic, is crucial. Carotid artery plaque (CAP), as an atherosclerotic lesion, is thought to be able to predict poor outcome from CHD and can be used as a surrogate for atherosclerotic disease, and previous studies have found that abnormalities in glucose metabolism are strongly associated with the risk of developing carotid artery plaque [5-6].\u003c/p\u003e\n\u003cp\u003eCirculating N-terminal pro B-type natriuretic peptide (NT-proBNP), a typical diagnostic and prognostic marker for heart failure, is an inactive amino acid fragment of brain natriuretic peptide (BNP), released by cardiomyocytes in response to volume or pressure overload and promotes vasodilation and natriuretic effect [7], which is a better indicator of diabetes mellitus than traditional risk factors, may better differentiate the risk of death from cardiovascular disease (CVD) in diabetic patients [8], and NT-proBNP may also be useful in monitoring diabetes-related micro- and macrovascular complications [9-10], but there are no studies on the association between NT-proBNP and CVD risk in different glucose metabolism states, and the association between NT-proBNP and CAP in the CHD population is also unknown.\u003c/p\u003e\n\u003cp\u003eTherefore, the aim of this study was to compare the correlation between NT-proBNP and the risk of CAP in the Chinese population with CHD, and to confirm the relationship NT-proBNP for the development of CAP in different glucose metabolism conditions, age and gender.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eSubjects\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe conducted a large, multicenter cohort study called \u003cem\u003eRetrospective Cohort Study on Cohort Study on Treatment of Cardiovascular Diseases with Traditional Chinese Medicine (CSCD-TCM \u003csup\u003eplus\u003c/sup\u003e)\u003c/em\u003e.\u0026nbsp;During the study, we established a CAD database, which included 214,717 inpatients with CAD from 6 hospitals in Tianjin, including First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin Medical University General Hospital, Tianjin Hospital of ITCWM Nankai Hospital, Tianjin Chest Hospital, Tianjin Academy of Traditional Chinese Medicine Affiliated Hospital, Second Teaching Hospital of Tianjin University of Traditional Chinese Medicine from 1 January 2014 to 30 June 2022.\u0026nbsp;Patients with oncological, infectious, or serious liver or renal diseases or lacking\u0026nbsp;data on\u0026nbsp;fasting blood glucose\u0026nbsp;(FBG)\u0026nbsp;and\u0026nbsp;carotid ultrasound measurements\u0026nbsp;were excluded.\u0026nbsp;Ultimately, 5,093 eligible subjects were enrolled in the final analysis. The flow chart of patient recruitment\u0026nbsp;is shown in Fig\u0026nbsp;1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eData collection\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe following data for the analysis of this study were obtained from the CAD database, data that came from the medical records: clinical history, anthropometric data, blood analysis, and medical imaging data. Anthropometric data, including blood pressure, and personal information such as age, sex, smoking status, drinking status, current antihypertensive treatment, and current antilipid treatment were recorded. Fasting venous blood samples were obtained from all subjects on the second day of hospitalization.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCarotid artery ultrasound examinations were performed by a certified professional technician using a diagnostic ultrasound system. In B-mode imaging, the common carotid artery, internal carotid artery, and carotid artery bifurcation were scanned and visualized. The carotid artery intima-media thickness (CIMT) was determined as the average of the IMT of the left and right common carotid arteries [11]. The professional physicians analyzed the color of the carotid artery based on the Doppler ultrasound results and recorded the number of CAPs and ultrasonographic features. CAP cases were divided into single (n = 1) and multiple (n\u0026nbsp;\u0026ge;\u0026nbsp;2). The echogenic properties of CAP were categorized as hypoechogenic, isoechogenic, hyperechogenic, and mixed types. Strict quality control procedures were used in this study to maintain consistency in the acquisition and analysis of monitoring and test images. Interlaboratory quality was assessed by licensed experimenters [12].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eDefinitions\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSmokers smoke at least 100 cigarettes in their lifetime [13]. Drinkers are defined as consuming alcohol at least 1 time per week [14]. Hypertension is defined as systolic blood pressure (SBP)\u0026nbsp;\u0026ge;\u0026nbsp;140 mmHg and/or diastolic blood pressure (DBP)\u0026nbsp;\u0026ge;\u0026nbsp;90 mmHg or current use of antihypertensive medication [15]. Diabetic status includes normoglycemia (NG) (FBG5.6mmol/L \u0026lt; 5.7%), Pre-DM (5.6\u0026nbsp;\u0026le;FBG\u0026nbsp;\u0026le;6.9mmol/L), DM (FBG\u0026nbsp;\u0026ge;\u0026nbsp;7.0mmol/L) [16].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eStatistical analysis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe NT-proBNP terms are T1 (NT-proBNP \u0026lt; 56ng/L), T2 (56\u0026nbsp;\u0026le;\u0026nbsp;NT-proBNP\u0026nbsp;\u0026le;\u0026nbsp;480ng/L) and T3 (NT-proBNP \u0026gt; 480ng/L). The Kolmogorov-Smirnov test was used to check the data for normality. Demographic differences between groups were assessed using the Kruskal-Wallis H test. Logistic regression models, estimated using odds ratios (ORs) and 95% confidence intervals (95% CIs), were used to examine the association of NT-proBNP with CAP. Two-sided \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05 was considered statistically significant. Collinearity of the different models was tested before logistic regression. Diagnostic value of NT-proBNP measurement using ROC curves for the risk of developing CAP. Missing values were imputed using the multiple imputation method. All statistical analyses were performed using the Statistical Package for the Social Sciences, version 24.0 (IBM Corp, New York, NY, USA).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eSubject Characteristics\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 5,093 participants were included in this study, of whom 2,490 (49.0%) were female and 2,597 (51.0%) were male, with a median age of 65 years, and 3,632 (71.3%) were over the age of 60 years. 1,807 (35.5%), 1,325 (26.0%), and 1,961 (38.5%) were NG, pre-DM, and DM, respectively. Subjects were divided into three groups according to the tertile level of the NT-proBNP index including T1, T2 and T3 group. Compared to T1 group, the distribution of FBG and HbA1c levels was higher in T2 and T3 group.\u0026nbsp;CAP\u0026nbsp;incidence (88.4%) was highest in the T3 group. Baseline characteristics of participants according to NT-proBNP are shown in Table 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAssociation between the NT-proBNP and the risk of carotid artery plaques\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs shown in Table 2, multivariate logistic regression analyses showed that, when NT-proBNP was used as a continuous variable, it was strongly associated with the risk of CAP development (OR: 1.50; 95% CI: 1.18-1.91). In the unadjusted model, when T1 was used as the reference, NT-proBNP levels at T2 and T3 were associated with an increased OR for CAP, with the highest correlation in T3 group (OR: 2.50; 95% CI: 2.08-3.01). Adjustment for potential confounders in models b and c showed that, when T1 was used as the reference, NT-proBNP levels at T3 were associated with an increased OR for CAP (OR: 1.58; 95% CI: 1.29-1.92), (OR: 1.69; 95% CI: 1.37-2.10). When NT-proBNP was used as a continuous variable, the NT-proBNP index (tertiles) was consistent with \u003cem\u003eP\u003c/em\u003e for trend with CAP (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.001).\u003c/p\u003e\n\u003cp\u003eAs shown in Table 3 and Table 4, after correcting for potential confounders, this association was equally significant across sex and age groups. Using T1 as a reference, the risk of CAP development was significantly associated with NT-proBNP at the level as T3, in both males and females, or in patients \u0026le;60 and \u0026gt;60 years of age.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAssociation between the\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003eNT-proBNP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;and the risk of carotid artery plaques according to glucose regulation state\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs shown in Table 5, after multivariate adjustment. NT-proBNP as a continuous variable was correlated with CAP occurrence (OR: 1.98; 95% CI: 1.24-2.14). Using T1 as a reference, T3 was significantly associated with an increased risk of CAP in both the pre-DM and DM states, which with the highest OR in the DM state (OR: 1.84; 95% CI: 1.26-2.69).\u003c/p\u003e\n\u003cp\u003eAs shown in Table 6, after adjusting for possible confounders, in male CHD patients with NG status, NT-proBNP is associated with the risk of CAP development (OR: 2.06; 95% CI: 1.16-3.67). Using T1 as a reference, T3 was associated with the risk of developing CAP in men with DM state in men (OR: 1.92; 95% CI: 1.12-3.30). In contrast, women in the pre-DM state had a significantly increased risk of CAP at T3 (OR: 1.94; 95% CI: 1.12-3.40).\u003c/p\u003e\n\u003cp\u003eAs shown in Table 7, after adjusting for possible confounders, in CHD patients \u0026le;60 years of age, NT-proBNP in the DM state was associated with the occurrence of CAP (OR: 2.94; 95% CI: 1.04-8.28), and conversely, in CHD patients \u0026gt;60 years of age, the correlation between continuous NT-proBNP and CAP was statistically significant in the NG and pre-DM states (OR: 2.52; 95% CI. 95% CI: 1.14-5.59), (OR: 7.04; 95% CI: 1.02-25.77). T3 was associated with the risk of developing CAP in CHD patients \u0026le;60 years of age in the DM state, using T1 as a reference (OR: 2.01; 95% CI: 1.19-3.41). When CHD patients were \u0026gt;60 years of age in the both of pre-DM and DM state, there had a significantly increased risk of developing CAP with T3 (OR: 1.94; 95% CI: 1.12-3.40), (OR: 1.81; 95% CI: 1.05-3.13).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eDiagnostic value of NT-proBNP in\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003ethe risk of carotid artery plaques\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs shown in Figure 2, we measured the diagnostic accuracy of CAP occurrence in NT-proBNP in CHD patients. The critical values were 182ng/l, with an AUC value of 0.627(95% CI: 0.592-0.631), sensitivity 50.7%, specificity 68.0%.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAssociation between the\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003eNT-proBNP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;and\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003ethe\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003eecho property\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;and number of\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003ecarotid artery plaques\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs shown in Supplementary material, when NT-proBNP was used as a continuous variable, it was strongly associated with multiple CAP. When T1 was used as a reference, T3 levels of NT-proBNP were associated with the development of \u0026ge;2 numbers of CAP. As shown in Supplementary Table 2, NT-proBNP was more likely to develop hypoechoic plaques and mixed plaques at the T1 level, whereas NT-proBNP was more likely to develop hypoechoic plaques at the T3 level.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study demonstrates the relationship between NT-proBNP and CAP in Chinese CHD patients with different glucose metabolic states. The association was also evaluated according to gender, age and glucose metabolism status. NT-proBNP was also measured to have some diagnostic significance for CAP.\u003c/p\u003e\n\u003cp\u003eMyocardial NT-proBNP synthesis and release is a counterregulatory response to increased myocardial wall stress, sympathetic nerve tension and vasoconstriction, acting in different systems and affecting different biological processes [17]. NT-proBNP and its receptors not only coordinate cardiovascular homeostasis and health by regulating blood pressure, blood volume and sodium balance, but are also involved in glucose and lipid metabolism in adipose and muscle tissues [18], and NT-proBNP has been shown to make an additional contribution to the prediction of long-term CHD risk compared with conventional risk factors, including smoking, alcohol consumption, and hypertension [19], but is also strongly associated with the risk of DM [20], and in addition, NT-proBNP levels are significantly associated with adverse cardiovascular events in patients with chronic CHD in the pre-diabetic state [21]. In the present study, we found that whether NT-proBNP was used as a continuous or categorical variable, its elevation was associated with the risk of CHD, and it also had some validity to be used as a diagnostic marker of CHD occurrence, which may be related to the close association between NT-proBNP and endothelial dysfunction: The biological action of the natriuretic peptides represented by NT-proBNP is mediated through the guanylate cyclase (GC) receptor, which is present in many tissues, including vascular endothelium and smooth muscle. Binding of NT-proBNP to the GC receptor increases cyclic guanosine monophosphate (cGMP), whose activation of protein kinase G phosphorylation is involved in smooth muscle contraction [22]; secondly, it has been shown to induce nitric oxide (NO) synthase, leading to higher levels of NO, a potent vasodilator and marker of endothelial function [23]; furthermore, another possible mechanism is that it may inhibit the production and secretion of endothelin-1, a potent vasoconstrictor [24].\u003c/p\u003e\n\u003cp\u003eNT-proBNP is also a good predictor of CHD outcome in certain patient groups. For example, E. Cosson et al. found that in diabetic patients NT-proBNP is a useful biomarker in the diagnosis of CHD [25]. Given the increasing prevalence of atrial disease and DM and the increasing cardiovascular burden [26-27], there is growing interest in thoroughly investigating patients with impaired glucose metabolism and exploring more valuable prognostic biomarkers. Therefore, the present study for the first time examined the relationship between NT-proBNP and CAP in different glucose metabolism states and found that, when used as a continuous variable, this relationship existed only in the NG state, and when divided into tertiles, it was found to be significant in both the pre-DM and DM states, with the \u0026lt;56 ng/L group as the reference, the \u0026gt;480 ng/L group had a significantly higher risk of developing CAD, possibly arising from the possible presence of elevated NT-proBNP as a protective factor against DM, which has been shown in previous studies where NT-proBNP concentration was negatively associated with future risk of DM in a population-based cohort study [28]. Similar observations have been described in the Community Atherosclerosis Risk Study, where individuals with low NT-proBNP levels had a significantly higher risk of diabetes [29]. Therefore, it is possible that NT-proBNP in the NG state patients in the present study had high levels and was more strongly associated with carotid atherosclerosis. Whereas when low NT-proBNP levels were used as a reference, high NT-proBNP and high blood glucose acted together to induce inflammation, oxidative stress, and metabolic changes, and inflammation led to vascular endothelial damage and increased plaque incidence.\u003c/p\u003e\n\u003cp\u003eIn addition, Dr. Margaret M. Redfield states in a 2002 study. [30] that the concentration of NT-proBNP increases gradually with age, and is significantly higher in women than in men; therefore, given that the interpretation of NT-proBNP must account for specific differences in age and sex, the present study compared the association of NT-proBNP with CAP, as well as its association under different glucose metabolism, according to sex and age, and found that this association was most significant in the DM state in men and the pre-DM state in women, and significant in the DM state in patients\u0026nbsp;\u0026le;60 years and the pre-DM state and DM in patients \u0026gt;60 years. This result may be attributable to differences in endothelin and angiotensin-converting enzyme activity due to sex-dependent hormonal status and structural changes in the heart due to advancing age.\u003c/p\u003e\n\u003cp\u003eThe nature and quantity of plaques determine the process of rupture or erosion, which can cause adverse cardiovascular events in a short period of time [31-32]. Therefore, detection of the change in NT-proBNP level and atherosclerotic plaque characteristics in patients with CHD may have important clinical significance. In this study, a high NT-proBNP level was found to be associated with the formation of multiple CAPs, and the corresponding plaque property was more likely to be hyperechogenic plaque, which is consistent with the previously found high NT-proBNP level and a large number of calcified plaques [33]. Hyperechogenic plaques usually exhibit calcification characteristics, and the presence of multiple plaques may indicate more severe luminal stenosis. Calcified plaques often coexist with more severe luminal stenosis in the middle and late stages of the disease [34-35] and are associated with inflammatory processes. Once plaque rupture or erosion occurs, the extent of myocardial infarction may be more severe compared to early disease with non-calcified plaques. Therefore, factors such as NT-proBNP, which can predict and diagnose the risk of CAD, should be taken seriously by researchers.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStrengths and limitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study has some strengths. First, to our knowledge, this is the largest population-based study of the association of NT-proBNP with CAP in patients with CHD. The study also validated this association in different glucose metabolism, age, and sex to examine the characteristics of different populations. Second, the analyses also included possible confounders to exclude their interference with the results. In addition, we investigated the diagnostic value of NT-proBNP for CAP, which may be useful to expand the clinical application of this marker in relation to atherosclerosis. However, this study has some limitations. The most important of these is that, as an observational study, this study is not suitable for investigating the causal relationship between NT-proBNP and CAP. Second, because of missing data, currently used glucose-lowering medications and body mass index (BMI), as important confounders, were not included in the regression model. Finally, as this is a multicentre study, there may be some unavoidable between-centre bias.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eNT-proBNP is closely related to the risk of CAP formation in different glucose metabolic states patients with CHD, which is also significant in gender and age groups, especially in men with DM and women with pre-DM, or in patients \u0026le; 60 years old with DM status and patients \u0026gt;60 years old with pre-DM and DM status. High levels of NT-proBNP have a stronger correlation with high echogenicity and multiple CAP. The use of NT-proBNP in the diagnosis of CAP in CHD patients has a certain validity, and NT-proBNP may be considered as a risk factor for clinical use in measuring the occurrence of CAP, especially for patients with abnormal glucose metabolism.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eCHD, coronary heart disease\u003c/p\u003e\n\u003cp\u003eDM, diabetes mellitus\u003c/p\u003e\n\u003cp\u003eMI, myocardial infarction\u003c/p\u003e\n\u003cp\u003eCAP, carotid artery plaque\u003c/p\u003e\n\u003cp\u003eNT-proBNP, Circulating N-terminal pro B-type natriuretic peptide\u003c/p\u003e\n\u003cp\u003eBNP, brain natriuretic peptide\u003c/p\u003e\n\u003cp\u003eCAD, cardiovascular disease\u003c/p\u003e\n\u003cp\u003eFBG, fasting blood glucose\u003c/p\u003e\n\u003cp\u003eCIMT, carotid intima-media thickness\u003c/p\u003e\n\u003cp\u003eSBP, systolic blood pressure\u003c/p\u003e\n\u003cp\u003eDBP, diastolic blood pressure\u003c/p\u003e\n\u003cp\u003eNG, normoglycemic\u003c/p\u003e\n\u003cp\u003eORs, odds ratios\u003c/p\u003e\n\u003cp\u003eCI: confidence intervals\u003c/p\u003e\n\u003cp\u003eGC, guanylate cyclase\u003c/p\u003e\n\u003cp\u003ecGMP, cyclic guanosine monophosphate\u003c/p\u003e\n\u003cp\u003eNO, nitric oxide\u003c/p\u003e\n\u003cp\u003eBMI, body mass index\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEthics approval and consent to participate\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by the Ethics Committee of Tianjin University of Traditional Chinese Medicine (approval number TJUTCM-EC20210007) and certified by the Chinese Clinical Trials Registry on April 4, 2022 (registration number ChiCTR2200058296) and on March 25, 2022 by ClinicalTrials.gov (registration number\u0026nbsp;NCT05309343).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eConsent for publication\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAvailability of data and materials\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed in the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCompeting interests\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the National Natural Science Foundation of China (82074140, 82104565, 82204142), Tianjin Hongrentang Pharmaceutical Co., Ltd., Tianjin, China (No. HX2020-16), Shanghai Hutchison Pharmaceuticals Ltd. (No. HX2020-39).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAuthors\u0026rsquo; contributions\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTong Yang, Hongmei Zheng, Fengmin Liu designed the study, performed the experiments, analysed the data and wrote the manuscript, performed the experiments and edited the manuscript.\u003c/p\u003e\n\u003cp\u003eRuiying Guo, Guangwei Pan, Shengyuan Liu, Shuang Tao analysed the data and edited the manuscript.\u003c/p\u003e\n\u003cp\u003eLin Li, Rongrong Yang, Chunquan Yu provided patient samples, designed the study, analysed the data, provided funding and edited the manuscript.\u003c/p\u003e\n\u003cp\u003eAll authors reviewed the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAcknowledgments\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors would like to thank all collaborators of this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eVirani SS, Newby LK, Arnold SV, Bittner V, Brewer LC, Demeter SH, Dixon DL, Fearon WF, Hess B, Johnson HM, Kazi DS, Kolte D, Kumbhani DJ, LoFaso J, Mahtta D, Mark DB, Minissian M, Navar AM, Patel AR, Piano MR, Rodriguez F, Talbot AW, Taqueti VR, Thomas RJ, van Diepen S, Wiggins B, Williams MS. 2023 AHA/ACC/ACCP/ASPC/NLA/PCNA Guideline for the Management of Patients With Chronic Coronary Disease: A Report of the American Heart Association/American College of Cardiology Joint Committee on Clinical Practice Guidelines. Circulation. 2023 Jul 20. doi: 10.1161/CIR.0000000000001168. Epub ahead of print. PMID: 37471501.\u003c/li\u003e\n\u003cli\u003eTheofilis P, Oikonomou E, Chasikidis C, Tsioufis K, Tousoulis D. Pathophysiology of Acute Coronary Syndromes-Diagnostic and Treatment Considerations. Life (Basel). 2023 Jul 12;13(7):1543. doi: 10.3390/life13071543. PMID: 37511918; PMCID: PMC10381786.\u003c/li\u003e\n\u003cli\u003eEl-Andari R, Bozso SJ, Fialka NM, Kang JJH, Nagendran J, Nagendran J. Coronary Revascularization for Patients with Diabetes Mellitus: A Contemporary Systematic Review and Meta-Analysis. Ann Surg. 2022 Jun 1;275(6):1058-1066. doi: 10.1097/SLA.0000000000005391. Epub 2022 Jan 25. PMID: 35081569. \u003c/li\u003e\n\u003cli\u003eXu W, Tian M, Zhou Y. The relationship between insulin resistance, adiponectin and C-reactive protein and vascular endothelial injury in diabetic patients with coronary heart disease. Exp Ther Med. 2018 Sep;16(3):2022-2026. doi: 10.3892/etm.2018.6407. Epub 2018 Jul 4. PMID: 30186434; PMCID: PMC6122372.\u003c/li\u003e\n\u003cli\u003ePolak JF, Tracy R, Harrington A, Zavodni AE, O\u0026apos;Leary DH. Carotid artery plaque and progression of coronary artery calcium: the multi-ethnic study of atherosclerosis. J Am Soc Echocardiogr. 2013 May;26(5):548-55. doi: 10.1016/j.echo.2013.02.009. Epub 2013 Mar 21. PMID: 23522805; PMCID: PMC4084492. \u003c/li\u003e\n\u003cli\u003eXu R, Zhang T, Wan Y, Fan Z, Gao X. Prospective study of hemoglobin A1c and incident carotid artery plaque in Chinese adults without diabetes. Cardiovasc Diabetol. 2019 Nov 14;18(1):153. doi: 10.1186/s12933-019-0963-5. PMID: 31727070; PMCID: PMC6857319.\u003c/li\u003e\n\u003cli\u003eOremus M, McKelvie R, Don-Wauchope A, Santaguida PL, Ali U, Balion C, Hill S, Booth R, Brown JA, Bustamam A, Sohel N, Raina P. A systematic review of BNP and NT-proBNP in the management of heart failure: overview and methods. Heart Fail Rev. 2014 Aug;19(4):413-9. doi: 10.1007/s10741-014-9440-0. Erratum in: Heart Fail Rev. 2014 Aug;19(4):565. PMID: 24953975.\u003c/li\u003e\n\u003cli\u003eWijkman MO, Claggett BL, Malachias MVB, Vaduganathan M, Ballantyne CM, Kitzman DW, Mosley T, Matsushita K, Solomon SD, Pfeffer MA. Importance of NT-proBNP and conventional risk factors for prediction of death in older adults with and without diabetes mellitus- A report from the Atherosclerosis Risk in Communities (ARIC) study. Diabetes Res Clin Pract. 2022 Dec;194:110164. doi: 10.1016/j.diabres.2022.110164. Epub 2022 Nov 19. PMID: 36410558.\u003c/li\u003e\n\u003cli\u003eBirukov A, Eichelmann F, Kuxhaus O, Polemiti E, Fritsche A, Wirth J, Boeing H, Weikert C, Schulze MB. Opposing Associations of NT-proBNP With Risks of Diabetes and Diabetes-Related Complications. Diabetes Care. 2020 Dec;43(12):2930-2937. doi: 10.2337/dc20-0553. Epub 2020 Aug 17. PMID: 32816995; PMCID: PMC7770272.\u003c/li\u003e\n\u003cli\u003eWelsh P, Woodward M, Hillis GS, Li Q, Marre M, Williams B, Poulter N, Ryan L, Harrap S, Patel A, Chalmers J, Sattar N. Do cardiac biomarkers NT-proBNP and hsTnT predict microvascular events in patients with type 2 diabetes? Results from the ADVANCE trial. Diabetes Care. 2014 Aug;37(8):2202-10. doi: 10.2337/dc13-2625. Epub 2014 May 30. PMID: 24879844.\u003c/li\u003e\n\u003cli\u003eLiu Y, Zhu Y, Jia W, Sun D, Zhao L, Zhang C, Wang C, Chen G, Fu S, Bo Y, Xing Y. Association between lipid profiles and presence of carotid plaque. Sci Rep. 2019 Nov 29;9(1):18011. doi: 10.1038/s41598-019-54285-w. PMID: 31784590; PMCID: PMC6884522.\u003c/li\u003e\n\u003cli\u003eLi Z, He Y, Wang S, Li L, Yang R, Liu Y, Cheng Q, Yu L, Zheng Y, Zheng H, Gao S, Yu C. Association between triglyceride glucose index and carotid artery plaque in different glucose metabolic states in patients with coronary heart disease: a RCSCD-TCM study in China. Cardiovasc Diabetol. 2022 Mar 11;21(1):38. doi: 10.1186/s12933-022-01470-3. PMID: 35277186; PMCID: PMC8917731.\u003c/li\u003e\n\u003cli\u003eBarua RS, Rigotti NA, Benowitz NL, Cummings KM, Jazayeri MA, Morris PB, Ratchford EV, Sarna L, Stecker EC, Wiggins BS. 2018 ACC Expert Consensus Decision Pathway on Tobacco Cessation Treatment: A Report of the American College of Cardiology Task Force on Clinical Expert Consensus Documents. J Am Coll Cardiol. 2018 Dec 25;72(25):3332-3365. doi: 10.1016/j.jacc.2018.10.027. Epub 2018 Dec 5. PMID: 30527452.\u003c/li\u003e\n\u003cli\u003eNg R, Sutradhar R, Yao Z, Wodchis WP, Rosella LC. Smoking, drinking, diet and physical activity-modifiable lifestyle risk factors and their associations with age to first chronic disease. Int J Epidemiol. 2020 Feb 1;49(1):113-130. doi: 10.1093/ije/dyz078. PMID: 31329872; PMCID: PMC7124486.\u003c/li\u003e\n\u003cli\u003eAl-Makki A, DiPette D, Whelton PK, Murad MH, Mustafa RA, Acharya S, Beheiry HM, Champagne B, Connell K, Cooney MT, Ezeigwe N, Gaziano TA, Gidio A, Lopez-Jaramillo P, Khan UI, Kumarapeli V, Moran AE, Silwimba MM, Rayner B, Sukonthasan A, Yu J, Saraffzadegan N, Reddy KS, Khan T. Hypertension Pharmacological Treatment in Adults: A World Health Organization Guideline Executive Summary. Hypertension. 2022 Jan;79(1):293-301. doi: 10.1161/HYPERTENSIONAHA.121.18192. Epub 2021 Nov 15. \u003c/li\u003e\n\u003cli\u003eMedina-Ch\u0026aacute;vez JH, V\u0026aacute;zquez-Parrodi M, Mendoza-Mart\u0026iacute;nez P, R\u0026iacute;os-Mej\u0026iacute;a ED, de Anda-Garay JC, Balandr\u0026aacute;n-Duarte DA. Protocolo de Atenci\u0026oacute;n Integral: prevenci\u0026oacute;n, diagn\u0026oacute;stico y tratamiento de diabetes mellitus 2 [Integrated Care Protocol: Prevention, diagnosis and treatment of diabetes mellitus 2]. Rev Med Inst Mex Seguro Soc. 2022 Feb 7;60(Supl 1):S4-S18. Spanish. PMID: 35135039; PMCID: PMC10395976.\u003c/li\u003e\n\u003cli\u003eMalachias MVB, Wijkman MO, Bertoluci MC. NT-proBNP as a predictor of death and cardiovascular events in patients with type 2 diabetes. Diabetol Metab Syndr. 2022 May 3;14(1):64. doi: 10.1186/s13098-022-00837-6. PMID: 35501909; PMCID: PMC9063067.\u003c/li\u003e\n\u003cli\u003ePandey KN. Molecular Signaling Mechanisms and Function of Natriuretic Peptide Receptor-A in the Pathophysiology of Cardiovascular Homeostasis. Front Physiol. 2021 Aug 19;12:693099. doi: 10.3389/fphys.2021.693099. PMID: 34489721; PMCID: PMC8416980.\u003c/li\u003e\n\u003cli\u003eNatriuretic Peptides Studies Collaboration, Willeit P, Kaptoge S, Welsh P, Butterworth AS, Chowdhury R, Spackman SA, Pennells L, Gao P, Burgess S, Freitag DF, Sweeting M, Wood AM, Cook NR, Judd S, Trompet S, Nambi V, Olsen MH, Everett BM, Kee F, \u0026Auml;rnl\u0026ouml;v J, Salomaa V, Levy D, Kauhanen J, Laukkanen JA, Kavousi M, Ninomiya T, Casas JP, Daniels LB, Lind L, Kistorp CN, Rosenberg J, Mueller T, Rubattu S, Panagiotakos DB, Franco OH, de Lemos JA, Luchner A, Kizer JR, Kiechl S, Salonen JT, Goya Wannamethee S, de Boer RA, Nordestgaard BG, Andersson J, J\u0026oslash;rgensen T, Melander O, Ballantyne ChM, DeFilippi Ch, Ridker PM, Cushman M, Rosamond WD, Thompson SG, Gudnason V, Sattar N, Danesh J, Di Angelantonio E. Natriuretic peptides and integrated risk assessment for cardiovascular disease: an individual-participant-data meta-analysis. Lancet Diabetes Endocrinol. 2016 Oct;4(10):840-9. doi: 10.1016/S2213-8587(16)30196-6. Epub 2016 Sep 3. PMID: 27599814; PMCID: PMC5035346.\u003c/li\u003e\n\u003cli\u003eKerkel\u0026auml; R, Ulvila J, Magga J. Natriuretic Peptides in the Regulation of Cardiovascular Physiology and Metabolic Events. J Am Heart Assoc. 2015 Oct 27;4(10):e002423. doi: 10.1161/JAHA.115.002423. PMID: 26508744; PMCID: PMC4845118.\u003c/li\u003e\n\u003cli\u003eLiu HH, Cao YX, Jin JL, Guo YL, Zhu CG, Wu NQ, Gao Y, Zhang Y, Xu RX, Dong Q, Li JJ. Prognostic value of NT-proBNP in patients with chronic coronary syndrome and normal left ventricular systolic function according to glucose status: a prospective cohort study. Cardiovasc Diabetol. 2021 Apr 22;20(1):84. doi: 10.1186/s12933-021-01271-0. PMID: 33888145; PMCID: PMC8063320.\u003c/li\u003e\n\u003cli\u003eGupta DK, Wang TJ. Natriuretic Peptides and Cardiometabolic Health. Circ J. 2015;79(8):1647-55. doi: 10.1253/circj.CJ-15-0589. Epub 2015 Jun 23. PMID: 26103984; PMCID: PMC4893202.\u003c/li\u003e\n\u003cli\u003eCosta MA, Arranz CT. New aspects of the interactions between the cardiovascular nitric oxide system and natriuretic peptides. Biochem Biophys Res Commun. 2011 Mar 11;406(2):161-4. doi: 10.1016/j.bbrc.2011.02.044. Epub 2011 Feb 15. PMID: 21329665.\u003c/li\u003e\n\u003cli\u003eDel Ry S, Andreassi MG, Clerico A, Biagini A, Giannessi D. Endothelin-1, endothelin-1 receptors and cardiac natriuretic peptides in failing human heart. Life Sci. 2001 May 4;68(24):2715-30. doi: 10.1016/s0024-3205(01)01076-1. PMID: 11400914.\u003c/li\u003e\n\u003cli\u003eCosson E, Nguyen MT, Pham I, Pontet M, Nitenberg A, Valensi P. N-terminal pro-B-type natriuretic peptide: an independent marker for coronary artery disease in asymptomatic diabetic patients. Diabet Med. 2009 Sep;26(9):872-9. doi: 10.1111/j.1464-5491.2009.02788.x. PMID: 19719707.\u003c/li\u003e\n\u003cli\u003eCosentino F, Grant PJ, Aboyans V, Bailey CJ, Ceriello A, Delgado V, Federici M, Filippatos G, Grobbee DE, Hansen TB, Huikuri HV, Johansson I, J\u0026uuml;ni P, Lettino M, Marx N, Mellbin LG, \u0026Ouml;stgren CJ, Rocca B, Roffi M, Sattar N, Seferović PM, Sousa-Uva M, Valensi P, Wheeler DC; ESC Scientific Document Group. 2019 ESC Guidelines on diabetes, pre-diabetes, and cardiovascular diseases developed in collaboration with the EASD. Eur Heart J. 2020 Jan 7;41(2):255-323. doi: 10.1093/eurheartj/ehz486. Erratum in: Eur Heart J. 2020 Dec 1;41(45):4317. PMID: 31497854.\u003c/li\u003e\n\u003cli\u003eLiu HH, Cao YX, Li S, Guo YL, Zhu CG, Wu NQ, Gao Y, Dong QT, Zhao X, Zhang Y, Sun D, Li JJ. Impacts of Prediabetes Mellitus Alone or Plus Hypertension on the Coronary Severity and Cardiovascular Outcomes. Hypertension. 2018 Jun;71(6):1039-1046. doi: 10.1161/HYPERTENSIONAHA.118.11063. Epub 2018 Apr 18. PMID: 29669793.\u003c/li\u003e\n\u003cli\u003eBirukov A, Eichelmann F, Kuxhaus O, Polemiti E, Fritsche A, Wirth J, Boeing H, Weikert C, Schulze MB. Opposing Associations of NT-proBNP With Risks of Diabetes and Diabetes-Related Complications. Diabetes Care. 2020 Dec;43(12):2930-2937. doi: 10.2337/dc20-0553. Epub 2020 Aug 17. PMID: 32816995; PMCID: PMC7770272.\u003c/li\u003e\n\u003cli\u003eLazo M, Young JH, Brancati FL, Coresh J, Whelton S, Ndumele CE, Hoogeveen R, Ballantyne CM, Selvin E. NH2-terminal pro-brain natriuretic peptide and risk of diabetes. Diabetes. 2013 Sep;62(9):3189-93. doi: 10.2337/db13-0478. Epub 2013 Jun 3. PMID: 23733199; PMCID: PMC3749338.\u003c/li\u003e\n\u003cli\u003eRedfield MM, Rodeheffer RJ, Jacobsen SJ, Mahoney DW, Bailey KR, Burnett JC Jr. Plasma brain natriuretic peptide concentration: impact of age and gender. J Am Coll Cardiol. 2002 Sep 4;40(5):976-82. doi: 10.1016/s0735-1097(02)02059-4. PMID: 12225726.\u003c/li\u003e\n\u003cli\u003eNurmohamed NS, Bom MJ, Jukema RA, de Groot RJ, Driessen RS, van Diemen PA, de Winter RW, Gaillard EL, Sprengers RW, Stroes ESG, Min JK, Earls JP, Cardoso R, Blankstein R, Danad I, Choi AD, Knaapen P. AI-Guided Quantitative Plaque Staging Predicts Long-Term Cardiovascular Outcomes in Patients at Risk for Atherosclerotic CVD. JACC Cardiovasc Imaging. 2023 Jul 7:S1936-878X(23)00277-2. doi: 10.1016/j.jcmg.2023.05.020. Epub ahead of print. PMID: 37480907.\u003c/li\u003e\n\u003cli\u003eF\u0026ouml;llmer B, Williams MC, Dey D, Arbab-Zadeh A, Maurovich-Horvat P, Volleberg RHJA, Rueckert D, Schnabel JA, Newby DE, Dweck MR, Guagliumi G, Falk V, V\u0026aacute;zquez M\u0026eacute;zquita AJ, Biavati F, I\u0026scaron;gum I, Dewey M. Roadmap on the use of artificial intelligence for imaging of vulnerable atherosclerotic plaque in coronary arteries. Nat Rev Cardiol. 2023 Jul 18. doi: 10.1038/s41569-023-00900-3. Epub ahead of print. PMID: 37464183.\u003c/li\u003e\n\u003cli\u003eGan L, Feng C, Liu C, Tian S, Song X, Yang L. Association between serum N-terminal pro-B-type natriuretic peptide levels and characteristics of coronary atherosclerotic plaque detected by coronary computed tomography angiography. Exp Ther Med. 2016 Aug;12(2):667-675. doi: 10.3892/etm.2016.3371. Epub 2016 May 19. PMID: 27446259; PMCID: PMC4950222.\u003c/li\u003e\n\u003cli\u003eSalama RH, El-Moniem AE, El-Hefney N, Samor T. N-TerminaL PRO-BNP in Acute Coronary Syndrome Patients with ST Elevation Versus Non ST Elevation in Qassim Region of Saudi Arabia. Int J Health Sci (Qassim). 2011 Jul;5(2):136-45. PMID: 23267291; PMCID: PMC3521832.\u003c/li\u003e\n\u003cli\u003eMin JK, Lin FY, Dunning AM, Delago A, Egan J, Shaw LJ, Berman DS, Callister TQ. Incremental prognostic significance of left ventricular dysfunction to coronary artery disease detection by 64-detector row coronary computed tomographic angiography for the prediction of all-cause mortality: results from a two-centre study of 5330 patients. Eur Heart J. 2010 May;31(10):1212-9. doi: 10.1093/eurheartj/ehq020. Epub 2010 Mar 2. PMID: 20197423.\u003cstrong\u003e \u003c/strong\u003e\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1 Baseline clinical characteristics\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eaccording to\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eNT-proBNP\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"997\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.432432432432435%\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.813813813813814%\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.214214214214214%\"\u003e\n \u003cp\u003eT1\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eN\u003c/em\u003e=1,687\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.115115115115115%\"\u003e\n \u003cp\u003eT2\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eN\u003c/em\u003e=1,710\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.914914914914915%\"\u003e\n \u003cp\u003eT3\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eN\u003c/em\u003e=1,696\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.50950950950951%\"\u003e\n \u003cp\u003e\u003cem\u003eP-\u003c/em\u003evalue\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"90.4809619238477%\" colspan=\"5\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519038076152304%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.432432432432435%\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.813813813813814%\"\u003e\n \u003cp\u003e65(60,71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.214214214214214%\"\u003e\n \u003cp\u003e62(56,67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.115115115115115%\"\u003e\n \u003cp\u003e66(61,70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.914914914914915%\"\u003e\n \u003cp\u003e67(61,71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.50950950950951%\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.432432432432435%\"\u003e\n \u003cp\u003e\u0026le;60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.813813813813814%\"\u003e\n \u003cp\u003e1461(28.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.214214214214214%\"\u003e\n \u003cp\u003e700(41.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.115115115115115%\"\u003e\n \u003cp\u003e390(22.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.914914914914915%\"\u003e\n \u003cp\u003e371(21.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.50950950950951%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.432432432432435%\"\u003e\n \u003cp\u003e\u0026gt;60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.813813813813814%\"\u003e\n \u003cp\u003e3632(71.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.214214214214214%\"\u003e\n \u003cp\u003e987(58.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.115115115115115%\"\u003e\n \u003cp\u003e1320(77.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.914914914914915%\"\u003e\n \u003cp\u003e1325(78.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.50950950950951%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"90.4809619238477%\" colspan=\"5\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519038076152304%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.432432432432435%\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.813813813813814%\"\u003e\n \u003cp\u003e2597(51.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.214214214214214%\"\u003e\n \u003cp\u003e867(51.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.115115115115115%\"\u003e\n \u003cp\u003e775(45.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.914914914914915%\"\u003e\n \u003cp\u003e955(56.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.50950950950951%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.432432432432435%\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.813813813813814%\"\u003e\n \u003cp\u003e2490(49.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.214214214214214%\"\u003e\n \u003cp\u003e820(48.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.115115115115115%\"\u003e\n \u003cp\u003e935(54.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.914914914914915%\"\u003e\n \u003cp\u003e741(43.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.50950950950951%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.432432432432435%\"\u003e\n \u003cp\u003eSBP (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.813813813813814%\"\u003e\n \u003cp\u003e140.0(130.0,160.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.214214214214214%\"\u003e\n \u003cp\u003e140.0(130.0,160.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.115115115115115%\"\u003e\n \u003cp\u003e143.0(130.0,160.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.914914914914915%\"\u003e\n \u003cp\u003e140.0(127.0,160.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.50950950950951%\"\u003e\n \u003cp\u003e0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.432432432432435%\"\u003e\n \u003cp\u003eDBP (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.813813813813814%\"\u003e\n \u003cp\u003e81.0(80.0,90.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.214214214214214%\"\u003e\n \u003cp\u003e85.0(80.0,93.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.115115115115115%\"\u003e\n \u003cp\u003e80.0(80.0,90.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.914914914914915%\"\u003e\n \u003cp\u003e80.0(76.0,90.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.50950950950951%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.432432432432435%\"\u003e\n \u003cp\u003eDrinking (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.813813813813814%\"\u003e\n \u003cp\u003e1242(24.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.214214214214214%\"\u003e\n \u003cp\u003e459(27.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.115115115115115%\"\u003e\n \u003cp\u003e398(23.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.914914914914915%\"\u003e\n \u003cp\u003e385(22.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.50950950950951%\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.432432432432435%\"\u003e\n \u003cp\u003eSmoking (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.813813813813814%\"\u003e\n \u003cp\u003e2105(41.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.214214214214214%\"\u003e\n \u003cp\u003e728(43.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.115115115115115%\"\u003e\n \u003cp\u003e687(40.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.914914914914915%\"\u003e\n \u003cp\u003e690(40.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.50950950950951%\"\u003e\n \u003cp\u003e0.170\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.432432432432435%\"\u003e\n \u003cp\u003eHypertension (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.813813813813814%\"\u003e\n \u003cp\u003e3915(76.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.214214214214214%\"\u003e\n \u003cp\u003e1336(79.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.115115115115115%\"\u003e\n \u003cp\u003e1372(80.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.914914914914915%\"\u003e\n \u003cp\u003e1207(71.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.50950950950951%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.432432432432435%\"\u003e\n \u003cp\u003eHyperlipidemia (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.813813813813814%\"\u003e\n \u003cp\u003e902(17.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.214214214214214%\"\u003e\n \u003cp\u003e402(23.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.115115115115115%\"\u003e\n \u003cp\u003e324(18.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.914914914914915%\"\u003e\n \u003cp\u003e176(10.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.50950950950951%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.432432432432435%\"\u003e\n \u003cp\u003eHbA1c (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.813813813813814%\"\u003e\n \u003cp\u003e6.1(5.6,7.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.214214214214214%\"\u003e\n \u003cp\u003e6.0(5.5,6.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.115115115115115%\"\u003e\n \u003cp\u003e6.1(5.6,7.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.914914914914915%\"\u003e\n \u003cp\u003e6.3(5.6,7.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.50950950950951%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.432432432432435%\"\u003e\n \u003cp\u003eFBG (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.813813813813814%\"\u003e\n \u003cp\u003e6.2(5.2,8.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.214214214214214%\"\u003e\n \u003cp\u003e6.3(5.3,8.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.115115115115115%\"\u003e\n \u003cp\u003e6.2(5.2,8.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.914914914914915%\"\u003e\n \u003cp\u003e6.4(5.2,8.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.50950950950951%\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"90.4809619238477%\" colspan=\"5\"\u003e\n \u003cp\u003eGlucose regulation state (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519038076152304%\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.432432432432435%\"\u003e\n \u003cp\u003eNG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.813813813813814%\"\u003e\n \u003cp\u003e1807(35.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.214214214214214%\"\u003e\n \u003cp\u003e583(34.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.115115115115115%\"\u003e\n \u003cp\u003e647(37.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.914914914914915%\"\u003e\n \u003cp\u003e577(34.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.50950950950951%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.432432432432435%\"\u003e\n \u003cp\u003ePre-DM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.813813813813814%\"\u003e\n \u003cp\u003e1325(26.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.214214214214214%\"\u003e\n \u003cp\u003e472(28.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.115115115115115%\"\u003e\n \u003cp\u003e427(25.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.914914914914915%\"\u003e\n \u003cp\u003e426(25.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.50950950950951%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.432432432432435%\"\u003e\n \u003cp\u003eDM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.813813813813814%\"\u003e\n \u003cp\u003e1961(38.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.214214214214214%\"\u003e\n \u003cp\u003e632(37.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.115115115115115%\"\u003e\n \u003cp\u003e636(37.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.914914914914915%\"\u003e\n \u003cp\u003e693(40.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.50950950950951%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.432432432432435%\"\u003e\n \u003cp\u003eCurrent antihypertensive medication (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.813813813813814%\"\u003e\n \u003cp\u003e3126(61.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.214214214214214%\"\u003e\n \u003cp\u003e1012(60.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.115115115115115%\"\u003e\n \u003cp\u003e1069(62.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.914914914914915%\"\u003e\n \u003cp\u003e1045(61.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.50950950950951%\"\u003e\n \u003cp\u003e0.309\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.432432432432435%\"\u003e\n \u003cp\u003eCurrent antilipidemic medication (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.813813813813814%\"\u003e\n \u003cp\u003e3808(74.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.214214214214214%\"\u003e\n \u003cp\u003e1282(76.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.115115115115115%\"\u003e\n \u003cp\u003e1305(76.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.914914914914915%\"\u003e\n \u003cp\u003e1221(72.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.50950950950951%\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.432432432432435%\"\u003e\n \u003cp\u003eCIMT (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.813813813813814%\"\u003e\n \u003cp\u003e0.1(0.09,0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.214214214214214%\"\u003e\n \u003cp\u003e0.1(0.08,0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.115115115115115%\"\u003e\n \u003cp\u003e0.1(0.09,0.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.914914914914915%\"\u003e\n \u003cp\u003e0.1(0.09,0.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.50950950950951%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.432432432432435%\"\u003e\n \u003cp\u003eCarotid artery plaque\u0026nbsp;(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.813813813813814%\"\u003e\n \u003cp\u003e4177(82.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.214214214214214%\"\u003e\n \u003cp\u003e1270(75.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.115115115115115%\"\u003e\n \u003cp\u003e1408(82.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.914914914914915%\"\u003e\n \u003cp\u003e1499(88.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.50950950950951%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"90.4809619238477%\" colspan=\"5\"\u003e\n \u003cp\u003eNumber of carotid artery plaque\u0026nbsp;(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519038076152304%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.432432432432435%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.813813813813814%\"\u003e\n \u003cp\u003e916(18.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.214214214214214%\"\u003e\n \u003cp\u003e417(24.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.115115115115115%\"\u003e\n \u003cp\u003e302(17.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.914914914914915%\"\u003e\n \u003cp\u003e197(11.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.50950950950951%\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.432432432432435%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.813813813813814%\"\u003e\n \u003cp\u003e399(7.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.214214214214214%\"\u003e\n \u003cp\u003e160(9.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.115115115115115%\"\u003e\n \u003cp\u003e146(8.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.914914914914915%\"\u003e\n \u003cp\u003e93(5.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.50950950950951%\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.432432432432435%\"\u003e\n \u003cp\u003e\u0026ge;2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.813813813813814%\"\u003e\n \u003cp\u003e3778(74.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.214214214214214%\"\u003e\n \u003cp\u003e1110(65.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.115115115115115%\"\u003e\n \u003cp\u003e1262(73.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.914914914914915%\"\u003e\n \u003cp\u003e1406(82.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.50950950950951%\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"90.4809619238477%\" colspan=\"5\"\u003e\n \u003cp\u003eCarotid artery plaque echo property\u0026nbsp;(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.519038076152304%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.432432432432435%\"\u003e\n \u003cp\u003eHypoechoic plaque\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.813813813813814%\"\u003e\n \u003cp\u003e192(3.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.214214214214214%\"\u003e\n \u003cp\u003e86(5.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.115115115115115%\"\u003e\n \u003cp\u003e49(2.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.914914914914915%\"\u003e\n \u003cp\u003e57(3.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.50950950950951%\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.432432432432435%\"\u003e\n \u003cp\u003eIsoechoic plaque\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.813813813813814%\"\u003e\n \u003cp\u003e379(7.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.214214214214214%\"\u003e\n \u003cp\u003e148(8.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.115115115115115%\"\u003e\n \u003cp\u003e128(7.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.914914914914915%\"\u003e\n \u003cp\u003e103(6.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.50950950950951%\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.432432432432435%\"\u003e\n \u003cp\u003eHyperechoic plaque\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.813813813813814%\"\u003e\n \u003cp\u003e2413(47.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.214214214214214%\"\u003e\n \u003cp\u003e691(41.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.115115115115115%\"\u003e\n \u003cp\u003e809(47.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.914914914914915%\"\u003e\n \u003cp\u003e913(53.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.50950950950951%\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.432432432432435%\"\u003e\n \u003cp\u003eMixture plaque\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.813813813813814%\"\u003e\n \u003cp\u003e1200(23.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.214214214214214%\"\u003e\n \u003cp\u003e348(20.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.115115115115115%\"\u003e\n \u003cp\u003e423(24.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.914914914914915%\"\u003e\n \u003cp\u003e429(25.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.50950950950951%\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eData are presented as\u0026nbsp;median (interquartile) or number (proportion, %)\u003c/p\u003e\n\u003cp\u003eSBP, systolic blood pressure; DBP, diastolic blood pressure; HbA1c, glycated hemoglobin; FBG, fasting blood glucose; NG, normoglycemic; Pre-DM, pre-diabetes; DM, diabetes; CIMT, carotid intima-media thickness\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2 Association between the NT-proBNP and the risk of\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ecarotid artery plaque\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"986\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.65922920892495%\" colspan=\"2\" rowspan=\"2\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"74.34077079107504%\" colspan=\"6\"\u003e\n \u003cp\u003eCarotid artery plaques\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.256130790190735%\"\u003e\n \u003cp\u003eOR (95% CI) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.713896457765667%\"\u003e\n \u003cp\u003e\u003cem\u003eP-\u003c/em\u003evalue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.80108991825613%\"\u003e\n \u003cp\u003eOR (95% CI) \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.713896457765667%\"\u003e\n \u003cp\u003e\u003cem\u003eP-\u003c/em\u003evalue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.80108991825613%\"\u003e\n \u003cp\u003eOR (95% CI) \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.713896457765667%\"\u003e\n \u003cp\u003e\u003cem\u003eP-\u003c/em\u003evalue\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.854251012145749%\" rowspan=\"5\"\u003e\n \u003cp\u003eNT-proBNP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.854251012145749%\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.562753036437247%\"\u003e\n \u003cp\u003e1.76(1.42-2.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.931174089068826%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.96761133603239%\"\u003e\n \u003cp\u003e1.38(1.14-1.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.931174089068826%\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.96761133603239%\"\u003e\n \u003cp\u003e1.50(1.18-1.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.931174089068826%\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.750290360046458%\"\u003e\n \u003cp\u003eT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.563298490127758%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.543554006968641%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.027874564459932%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.543554006968641%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.027874564459932%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.543554006968641%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.750290360046458%\"\u003e\n \u003cp\u003eT2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.563298490127758%\"\u003e\n \u003cp\u003e1.53(1.30-1.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.543554006968641%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.027874564459932%\"\u003e\n \u003cp\u003e1.10(0.92-1.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.543554006968641%\"\u003e\n \u003cp\u003e0.297\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.027874564459932%\"\u003e\n \u003cp\u003e1.07(0.88-1.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.543554006968641%\"\u003e\n \u003cp\u003e0.522\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.750290360046458%\"\u003e\n \u003cp\u003eT3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.563298490127758%\"\u003e\n \u003cp\u003e2.50(2.08-3.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.543554006968641%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.027874564459932%\"\u003e\n \u003cp\u003e1.58(1.29-1.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.543554006968641%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.027874564459932%\"\u003e\n \u003cp\u003e1.69(1.37-2.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.543554006968641%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.750290360046458%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u003csub\u003etrend\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.563298490127758%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.543554006968641%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.027874564459932%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.543554006968641%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.027874564459932%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.543554006968641%\"\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\u003c/div\u003e\n\u003cp\u003e\u003csup\u003ea\u0026nbsp;\u003c/sup\u003eModel 1: unadjusted\u003c/p\u003e\n\u003cp\u003e\u003csup\u003eb\u0026nbsp;\u003c/sup\u003eModel 2: adjusted for age, sex\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ec\u0026nbsp;\u003c/sup\u003eModel 3: adjusted for age, sex, SBP, DBP, smoking, drinking, hypertension, hyperlipidemia, use of antihypertensives, and use of antilipidemic\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3 Association between the NT-proBNP and the risk of\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ecarotid artery plaque\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eaccording to\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003esex\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"1068\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.42830365510778%\" colspan=\"3\" rowspan=\"2\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"70.57169634489222%\" colspan=\"6\"\u003e\n \u003cp\u003eCarotid artery plaques\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.48404255319149%\"\u003e\n \u003cp\u003eOR (95% CI) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.095744680851064%\"\u003e\n \u003cp\u003e\u003cem\u003eP-\u003c/em\u003evalue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.48404255319149%\"\u003e\n \u003cp\u003eOR (95% CI) \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.095744680851064%\"\u003e\n \u003cp\u003e\u003cem\u003eP-\u003c/em\u003evalue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.48404255319149%\"\u003e\n \u003cp\u003eOR (95% CI) \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.356382978723403%\"\u003e\n \u003cp\u003e\u003cem\u003eP-\u003c/em\u003evalue\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.101313320825517%\" rowspan=\"10\"\u003e\n \u003cp\u003eNT-proBNP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.195121951219512%\" rowspan=\"5\"\u003e\n \u003cp\u003eMales\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.159474671669794%\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.039399624765478%\"\u003e\n \u003cp\u003e1.26(1.03-1.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.943714821763603%\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.039399624765478%\"\u003e\n \u003cp\u003e1.10(0.92-1.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.943714821763603%\"\u003e\n \u003cp\u003e0.316\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.039399624765478%\"\u003e\n \u003cp\u003e1.26(0.96-1.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.538461538461538%\"\u003e\n \u003cp\u003e0.098\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.815365551425031%\"\u003e\n \u003cp\u003eT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.224287484510533%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.135068153655514%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.224287484510533%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.135068153655514%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.224287484510533%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.241635687732343%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.815365551425031%\"\u003e\n \u003cp\u003eT2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.224287484510533%\"\u003e\n \u003cp\u003e1.48(1.13-1.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.135068153655514%\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.224287484510533%\"\u003e\n \u003cp\u003e1.11(0.83-1.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.135068153655514%\"\u003e\n \u003cp\u003e0.488\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.224287484510533%\"\u003e\n \u003cp\u003e1.04(0.77-1.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.241635687732343%\"\u003e\n \u003cp\u003e0.779\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.815365551425031%\"\u003e\n \u003cp\u003eT3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.224287484510533%\"\u003e\n \u003cp\u003e2.12(1.62-2.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.135068153655514%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.224287484510533%\"\u003e\n \u003cp\u003e1.49(1.12-1.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.135068153655514%\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.224287484510533%\"\u003e\n \u003cp\u003e1.68(1.22-2.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.241635687732343%\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.815365551425031%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u003csub\u003etrend\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.224287484510533%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.135068153655514%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.224287484510533%\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.135068153655514%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.224287484510533%\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.241635687732343%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.874066168623266%\" rowspan=\"5\"\u003e\n \u003cp\u003eFemales\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.869797225186766%\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.834578441835646%\"\u003e\n \u003cp\u003e3.04(1.96-4.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.312700106723586%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.834578441835646%\"\u003e\n \u003cp\u003e1.12(1.11-1.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.312700106723586%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.834578441835646%\"\u003e\n \u003cp\u003e1.91(1.26-2.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.12700106723586%\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.815365551425031%\"\u003e\n \u003cp\u003eT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.224287484510533%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.135068153655514%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.224287484510533%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.135068153655514%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.224287484510533%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.241635687732343%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.815365551425031%\"\u003e\n \u003cp\u003eT2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.224287484510533%\"\u003e\n \u003cp\u003e1.68(1.25-2.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.135068153655514%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.224287484510533%\"\u003e\n \u003cp\u003e1.08(0.86-1.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.135068153655514%\"\u003e\n \u003cp\u003e0.508\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.224287484510533%\"\u003e\n \u003cp\u003e1.06(0.83-1.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.241635687732343%\"\u003e\n \u003cp\u003e0.634\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.815365551425031%\"\u003e\n \u003cp\u003eT3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.224287484510533%\"\u003e\n \u003cp\u003e2.76(2.14-3.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.135068153655514%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.224287484510533%\"\u003e\n \u003cp\u003e1.62(1.23-2.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.135068153655514%\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.224287484510533%\"\u003e\n \u003cp\u003e1.68(1.25-2.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.241635687732343%\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.815365551425031%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u003csub\u003etrend\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.224287484510533%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.135068153655514%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.224287484510533%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.135068153655514%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.224287484510533%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.241635687732343%\"\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\u003c/div\u003e\n\u003cp\u003e\u003csup\u003ea\u0026nbsp;\u003c/sup\u003eModel 1: unadjusted\u003c/p\u003e\n\u003cp\u003e\u003csup\u003eb\u0026nbsp;\u003c/sup\u003eModel 2: adjusted for age\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ec\u0026nbsp;\u003c/sup\u003eModel 3: adjusted for age, SBP, DBP, smoking, drinking, hypertension, hyperlipidemia, use of antihypertensives, and use of antilipidemic\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4 Association between the NT-proBNP and the risk of\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ecarotid artery plaque\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eaccording to\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eage\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"1068\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.42830365510778%\" colspan=\"3\" rowspan=\"2\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"70.57169634489222%\" colspan=\"6\"\u003e\n \u003cp\u003eCarotid artery plaques\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.459495351925632%\"\u003e\n \u003cp\u003eOR (95% CI) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.334661354581673%\"\u003e\n \u003cp\u003e\u003cem\u003eP-\u003c/em\u003evalue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.92828685258964%\"\u003e\n \u003cp\u003eOR (95% CI) \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.741035856573705%\"\u003e\n \u003cp\u003e\u003cem\u003eP-\u003c/em\u003evalue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.12350597609562%\"\u003e\n \u003cp\u003eOR (95% CI) \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.413014608233732%\"\u003e\n \u003cp\u003e\u003cem\u003eP-\u003c/em\u003evalue\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.089971883786317%\" rowspan=\"10\"\u003e\n \u003cp\u003eNT-proBNP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.18369259606373%\" rowspan=\"5\"\u003e\n \u003cp\u003e\u0026le;60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.154639175257732%\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.02717900656045%\"\u003e\n \u003cp\u003e1.41(1.11-1.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.527647610121837%\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.652296157450797%\"\u003e\n \u003cp\u003e1.41(1.10-1.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.402999062792878%\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.495782567947517%\"\u003e\n \u003cp\u003e1.31(1.01-1.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.465791940018745%\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.806930693069307%\"\u003e\n \u003cp\u003eT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.202970297029704%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.222772277227723%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.707920792079207%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.737623762376238%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.821782178217823%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.806930693069307%\"\u003e\n \u003cp\u003eT2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.202970297029704%\"\u003e\n \u003cp\u003e1.36(1.04-1.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.222772277227723%\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.707920792079207%\"\u003e\n \u003cp\u003e1.32(1.01-1.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.737623762376238%\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.821782178217823%\"\u003e\n \u003cp\u003e1.20(0.90-1.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e0.233\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.806930693069307%\"\u003e\n \u003cp\u003eT3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.202970297029704%\"\u003e\n \u003cp\u003e2.01(1.51-2.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.222772277227723%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.707920792079207%\"\u003e\n \u003cp\u003e1.87(1.40-2.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.737623762376238%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.821782178217823%\"\u003e\n \u003cp\u003e1.82(1.33-2.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.806930693069307%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u003csub\u003etrend\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.202970297029704%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.222772277227723%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.707920792079207%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.737623762376238%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.821782178217823%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.859275053304904%\" rowspan=\"5\"\u003e\n \u003cp\u003e＞60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.863539445628998%\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.818763326226012%\"\u003e\n \u003cp\u003e2.11(1.50-2.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.113006396588487%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.392324093816631%\"\u003e\n \u003cp\u003e1.53(1.12-2.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.83368869936034%\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.35181236673774%\"\u003e\n \u003cp\u003e1.74(1.17-2.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.767590618336888%\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.806930693069307%\"\u003e\n \u003cp\u003eT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.202970297029704%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.222772277227723%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.707920792079207%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.737623762376238%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.821782178217823%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.806930693069307%\"\u003e\n \u003cp\u003eT2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.202970297029704%\"\u003e\n \u003cp\u003e1.12(0.89-1.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.222772277227723%\"\u003e\n \u003cp\u003e0.332\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.707920792079207%\"\u003e\n \u003cp\u003e1.18(0.93.1.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.737623762376238%\"\u003e\n \u003cp\u003e0.170\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.821782178217823%\"\u003e\n \u003cp\u003e1.10(0.85.1.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e0.459\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.806930693069307%\"\u003e\n \u003cp\u003eT3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.202970297029704%\"\u003e\n \u003cp\u003e1.97(1.51-2.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.222772277227723%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.707920792079207%\"\u003e\n \u003cp\u003e1.90(1.46-2.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.737623762376238%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.821782178217823%\"\u003e\n \u003cp\u003e1.94(1.45-2.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.806930693069307%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u003csub\u003etrend\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.202970297029704%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.222772277227723%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.707920792079207%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.737623762376238%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.821782178217823%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\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\u003c/div\u003e\n\u003cp\u003e\u003csup\u003ea\u0026nbsp;\u003c/sup\u003eModel 1: unadjusted\u003c/p\u003e\n\u003cp\u003e\u003csup\u003eb\u0026nbsp;\u003c/sup\u003eModel 2: adjusted for sex\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ec\u0026nbsp;\u003c/sup\u003eModel 3: adjusted for sex, SBP, DBP, smoking, drinking, hypertension, hyperlipidemia, use of antihypertensives, and use of antilipidemic\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eAssociation between the\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eNT-proBNP\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;and the risk of carotid artery plaques according to glucose regulation state\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"951\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.597266035751844%\" colspan=\"2\" rowspan=\"2\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"67.40273396424816%\" colspan=\"6\"\u003e\n \u003cp\u003eCarotid artery plaques\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.875%\"\u003e\n \u003cp\u003eOR (95% CI) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.875%\"\u003e\n \u003cp\u003e\u003cem\u003eP-\u003c/em\u003evalue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.625%\"\u003e\n \u003cp\u003eOR (95% CI) \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.875%\"\u003e\n \u003cp\u003e\u003cem\u003eP-\u003c/em\u003evalue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.625%\"\u003e\n \u003cp\u003eOR (95% CI) \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.125%\"\u003e\n \u003cp\u003e\u003cem\u003eP-\u003c/em\u003evalue\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.526315789473685%\" rowspan=\"5\"\u003e\n \u003cp\u003eNormal glucose regulation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.105263157894737%\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e2.99(1.91-4.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.894736842105264%\"\u003e\n \u003cp\u003e2.98(2.29-3.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.894736842105264%\"\u003e\n \u003cp\u003e1.98(1.24-2.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.842105263157896%\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.30945558739255%\"\u003e\n \u003cp\u003eT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.05730659025788%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.888252148997134%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.911174785100286%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.888252148997134%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.911174785100286%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.034383954154729%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.30945558739255%\"\u003e\n \u003cp\u003eT2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.05730659025788%\"\u003e\n \u003cp\u003e1.39(1.07-1.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.888252148997134%\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.911174785100286%\"\u003e\n \u003cp\u003e1.00(0.75-1.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.888252148997134%\"\u003e\n \u003cp\u003e0.971\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.911174785100286%\"\u003e\n \u003cp\u003e0.88(0.65-1.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.034383954154729%\"\u003e\n \u003cp\u003e0.431\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.30945558739255%\"\u003e\n \u003cp\u003eT3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.05730659025788%\"\u003e\n \u003cp\u003e2.45(1.83-3.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.888252148997134%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.911174785100286%\"\u003e\n \u003cp\u003e1.43(1.03-1.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.888252148997134%\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.911174785100286%\"\u003e\n \u003cp\u003e1.41(1.00-2.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.034383954154729%\"\u003e\n \u003cp\u003e0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.30945558739255%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u003csub\u003etrend\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.05730659025788%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.888252148997134%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.911174785100286%\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.888252148997134%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.911174785100286%\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.034383954154729%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.526315789473685%\" rowspan=\"5\"\u003e\n \u003cp\u003ePrediabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.105263157894737%\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e1.25(0.98-1.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8%\"\u003e\n \u003cp\u003e0.070\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.894736842105264%\"\u003e\n \u003cp\u003e1.02(1.83-1.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8%\"\u003e\n \u003cp\u003e0.875\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.894736842105264%\"\u003e\n \u003cp\u003e1.14(0.80-1.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.842105263157896%\"\u003e\n \u003cp\u003e0.467\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.30945558739255%\"\u003e\n \u003cp\u003eT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.05730659025788%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.888252148997134%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.911174785100286%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.888252148997134%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.911174785100286%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.034383954154729%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.30945558739255%\"\u003e\n \u003cp\u003eT2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.05730659025788%\"\u003e\n \u003cp\u003e1.85(1.34-2.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.888252148997134%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.911174785100286%\"\u003e\n \u003cp\u003e1.26(0.88-1.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.888252148997134%\"\u003e\n \u003cp\u003e0.205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.911174785100286%\"\u003e\n \u003cp\u003e1.35(0.92-1.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.034383954154729%\"\u003e\n \u003cp\u003e0.121\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.30945558739255%\"\u003e\n \u003cp\u003eT3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.05730659025788%\"\u003e\n \u003cp\u003e2.71(1.90-3.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.888252148997134%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.911174785100286%\"\u003e\n \u003cp\u003e1.48(1.10-2.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.888252148997134%\"\u003e\n \u003cp\u003e0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.911174785100286%\"\u003e\n \u003cp\u003e1.80(1.19-2.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.034383954154729%\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.30945558739255%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u003csub\u003etrend\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.05730659025788%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.888252148997134%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.911174785100286%\"\u003e\n \u003cp\u003e0.089\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.888252148997134%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.911174785100286%\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.034383954154729%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.526315789473685%\" rowspan=\"5\"\u003e\n \u003cp\u003eDiabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.105263157894737%\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.736842105263158%\"\u003e\n \u003cp\u003e1.63(1.13-2.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8%\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.894736842105264%\"\u003e\n \u003cp\u003e1.40(0.99-1.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8%\"\u003e\n \u003cp\u003e0.057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.894736842105264%\"\u003e\n \u003cp\u003e1.48(0.96-2.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.842105263157896%\"\u003e\n \u003cp\u003e0.074\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.30945558739255%\"\u003e\n \u003cp\u003eT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.05730659025788%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.888252148997134%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.911174785100286%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.888252148997134%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.911174785100286%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.034383954154729%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.30945558739255%\"\u003e\n \u003cp\u003eT2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.05730659025788%\"\u003e\n \u003cp\u003e1.551(1.14-2.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.888252148997134%\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.911174785100286%\"\u003e\n \u003cp\u003e1.16(0.84-1.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.888252148997134%\"\u003e\n \u003cp\u003e0.376\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.911174785100286%\"\u003e\n \u003cp\u003e1.12(0.79-1.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.034383954154729%\"\u003e\n \u003cp\u003e0.529\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.30945558739255%\"\u003e\n \u003cp\u003eT3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.05730659025788%\"\u003e\n \u003cp\u003e2.34(1.68-3.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.888252148997134%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.911174785100286%\"\u003e\n \u003cp\u003e1.69(1.20-2.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.888252148997134%\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.911174785100286%\"\u003e\n \u003cp\u003e1.84(1.26-2.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.034383954154729%\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.30945558739255%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u003csub\u003etrend\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.05730659025788%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.888252148997134%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.911174785100286%\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.888252148997134%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.911174785100286%\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.034383954154729%\"\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\u003c/div\u003e\n\u003cp\u003e\u003csup\u003ea\u0026nbsp;\u003c/sup\u003eModel 1: unadjusted\u003c/p\u003e\n\u003cp\u003e\u003csup\u003eb\u0026nbsp;\u003c/sup\u003eModel 2: adjusted for age, sex\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ec\u0026nbsp;\u003c/sup\u003eModel 3: adjusted for age, sex, SBP, DBP, smoking, drinking, hypertension, hyperlipidemia, use of antihypertensives, and use of antilipidemic\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eAssociation between the\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eNT-proBNP\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;and the risk of carotid artery plaques according to different glucose regulation state and sex\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"984\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.739837398373986%\" colspan=\"3\" rowspan=\"2\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"66.26016260162602%\" colspan=\"6\"\u003e\n \u003cp\u003eCarotid artery plaques\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.858895705521473%\"\u003e\n \u003cp\u003eOR (95% CI) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.423312883435583%\"\u003e\n \u003cp\u003e\u003cem\u003eP-\u003c/em\u003evalue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.779141104294478%\"\u003e\n \u003cp\u003eOR (95% CI) \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.656441717791411%\"\u003e\n \u003cp\u003e\u003cem\u003eP-\u003c/em\u003evalue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.245398773006134%\"\u003e\n \u003cp\u003eOR (95% CI) \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.036809815950921%\"\u003e\n \u003cp\u003e\u003cem\u003eP-\u003c/em\u003evalue\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.416243654822335%\" rowspan=\"15\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.604060913705585%\" rowspan=\"5\"\u003e\n \u003cp\u003eNormal glucose regulation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.786802030456853%\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.807106598984772%\"\u003e\n \u003cp\u003e1.99(1.22-3.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.223350253807107%\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.416243654822335%\"\u003e\n \u003cp\u003e1.74(1.08-2.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.715736040609137%\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.401015228426395%\"\u003e\n \u003cp\u003e2.06(1.16-3.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.629441624365482%\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.039492242595205%\"\u003e\n \u003cp\u003eT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.181946403385048%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.424541607898448%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.02820874471086%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.719322990126939%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.61777150916784%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.988716502115656%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.039492242595205%\"\u003e\n \u003cp\u003eT2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.181946403385048%\"\u003e\n \u003cp\u003e1.37(0.90-2.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.424541607898448%\"\u003e\n \u003cp\u003e0.144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.02820874471086%\"\u003e\n \u003cp\u003e0.93(0.59-1.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.719322990126939%\"\u003e\n \u003cp\u003e0.742\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.61777150916784%\"\u003e\n \u003cp\u003e0.74(0.45-1.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.988716502115656%\"\u003e\n \u003cp\u003e0.230\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.039492242595205%\"\u003e\n \u003cp\u003eT3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.181946403385048%\"\u003e\n \u003cp\u003e2.06(1.33-3.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.424541607898448%\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.02820874471086%\"\u003e\n \u003cp\u003e1.35(0.82-2.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.719322990126939%\"\u003e\n \u003cp\u003e0.224\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.61777150916784%\"\u003e\n \u003cp\u003e1.45(0.86-2.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.988716502115656%\"\u003e\n \u003cp\u003e0.164\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.039492242595205%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u003csub\u003etrend\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.181946403385048%\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.424541607898448%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.02820874471086%\"\u003e\n \u003cp\u003e0.131\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.719322990126939%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.61777150916784%\"\u003e\n \u003cp\u003e0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.988716502115656%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.895610913404507%\" rowspan=\"5\"\u003e\n \u003cp\u003ePrediabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.761565836298932%\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.132858837485173%\"\u003e\n \u003cp\u003e0.98(0.8-1.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.608540925266905%\"\u003e\n \u003cp\u003e0.864\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.844602609727165%\"\u003e\n \u003cp\u003e0.83(0.67-1.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.015421115065243%\"\u003e\n \u003cp\u003e0.090\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.658362989323843%\"\u003e\n \u003cp\u003e0.90(0.68-1.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.083036773428233%\"\u003e\n \u003cp\u003e0.420\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.039492242595205%\"\u003e\n \u003cp\u003eT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.181946403385048%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.424541607898448%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.02820874471086%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.719322990126939%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.61777150916784%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.988716502115656%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.039492242595205%\"\u003e\n \u003cp\u003eT2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.181946403385048%\"\u003e\n \u003cp\u003e1.93(1.13-3.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.424541607898448%\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.02820874471086%\"\u003e\n \u003cp\u003e1.26(0.71-2.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.719322990126939%\"\u003e\n \u003cp\u003e0.430\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.61777150916784%\"\u003e\n \u003cp\u003e1.23(0.67-2.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.988716502115656%\"\u003e\n \u003cp\u003e0.502\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.039492242595205%\"\u003e\n \u003cp\u003eT3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.181946403385048%\"\u003e\n \u003cp\u003e2.49(2.47-3.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.424541607898448%\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.02820874471086%\"\u003e\n \u003cp\u003e1.47(0.84-2.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.719322990126939%\"\u003e\n \u003cp\u003e0.182\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.61777150916784%\"\u003e\n \u003cp\u003e1.63(0.87-3.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.988716502115656%\"\u003e\n \u003cp\u003e0.130\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.039492242595205%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u003csub\u003etrend\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.181946403385048%\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.424541607898448%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.02820874471086%\"\u003e\n \u003cp\u003e0.255\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.719322990126939%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.61777150916784%\"\u003e\n \u003cp\u003e0.167\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.988716502115656%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.895610913404507%\" rowspan=\"5\"\u003e\n \u003cp\u003eDiabetes\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.761565836298932%\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.132858837485173%\"\u003e\n \u003cp\u003e1.26(0.86-1.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.608540925266905%\"\u003e\n \u003cp\u003e0.237\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.844602609727165%\"\u003e\n \u003cp\u003e1.16(0.82-1.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.015421115065243%\"\u003e\n \u003cp\u003e0.409\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.658362989323843%\"\u003e\n \u003cp\u003e1.28(0.75-2.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.083036773428233%\"\u003e\n \u003cp\u003e0.366\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.039492242595205%\"\u003e\n \u003cp\u003eT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.181946403385048%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.424541607898448%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.02820874471086%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.719322990126939%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.61777150916784%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.988716502115656%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.039492242595205%\"\u003e\n \u003cp\u003eT2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.181946403385048%\"\u003e\n \u003cp\u003e1.41(0.88-2.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.424541607898448%\"\u003e\n \u003cp\u003e0.153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.02820874471086%\"\u003e\n \u003cp\u003e1.22(0.76-2.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.719322990126939%\"\u003e\n \u003cp\u003e0.421\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.61777150916784%\"\u003e\n \u003cp\u003e1.20(0.72-2.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.988716502115656%\"\u003e\n \u003cp\u003e0.477\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.039492242595205%\"\u003e\n \u003cp\u003eT3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.181946403385048%\"\u003e\n \u003cp\u003e1.98(2.24-3.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.424541607898448%\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.02820874471086%\"\u003e\n \u003cp\u003e1.65(1.02-2.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.719322990126939%\"\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.61777150916784%\"\u003e\n \u003cp\u003e1.92(1.12-3.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.988716502115656%\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.039492242595205%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u003csub\u003etrend\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.181946403385048%\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.424541607898448%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.02820874471086%\"\u003e\n \u003cp\u003e0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.719322990126939%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.61777150916784%\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.988716502115656%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.416243654822335%\" rowspan=\"15\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.604060913705585%\" rowspan=\"5\"\u003e\n \u003cp\u003eNormal glucose regulation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.786802030456853%\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.807106598984772%\"\u003e\n \u003cp\u003e4.429(1.92-9.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.223350253807107%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.416243654822335%\"\u003e\n \u003cp\u003e2.60(1.29-4.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.715736040609137%\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.401015228426395%\"\u003e\n \u003cp\u003e1.96(0.91-4.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.629441624365482%\"\u003e\n \u003cp\u003e0.085\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.039492242595205%\"\u003e\n \u003cp\u003eT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.181946403385048%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.424541607898448%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.02820874471086%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.719322990126939%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.61777150916784%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.988716502115656%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.039492242595205%\"\u003e\n \u003cp\u003eT2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.181946403385048%\"\u003e\n \u003cp\u003e1.49(1.07-2.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.424541607898448%\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.02820874471086%\"\u003e\n \u003cp\u003e1.04(0.73-1.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.719322990126939%\"\u003e\n \u003cp\u003e0.816\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.61777150916784%\"\u003e\n \u003cp\u003e0.92(0.62-1.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.988716502115656%\"\u003e\n \u003cp\u003e0.677\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.039492242595205%\"\u003e\n \u003cp\u003eT3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.181946403385048%\"\u003e\n \u003cp\u003e2.64(1.77-3.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.424541607898448%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.02820874471086%\"\u003e\n \u003cp\u003e1.50(0.97-2.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.719322990126939%\"\u003e\n \u003cp\u003e0.068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.61777150916784%\"\u003e\n \u003cp\u003e1.38(-.85-2.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.988716502115656%\"\u003e\n \u003cp\u003e0.191\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.039492242595205%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u003csub\u003etrend\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.181946403385048%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.424541607898448%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u003csub\u003etrend\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.02820874471086%\"\u003e\n \u003cp\u003e0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.719322990126939%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u003csub\u003etrend\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.61777150916784%\"\u003e\n \u003cp\u003e0.090\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.988716502115656%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.895610913404507%\" rowspan=\"5\"\u003e\n \u003cp\u003ePrediabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.761565836298932%\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.132858837485173%\"\u003e\n \u003cp\u003e2.01(1.04-3.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.608540925266905%\"\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.844602609727165%\"\u003e\n \u003cp\u003e1.79(0.98-3.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.015421115065243%\"\u003e\n \u003cp\u003e0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.658362989323843%\"\u003e\n \u003cp\u003e2.97(0.98-3.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.083036773428233%\"\u003e\n \u003cp\u003e0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.039492242595205%\"\u003e\n \u003cp\u003eT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.181946403385048%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.424541607898448%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.02820874471086%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.719322990126939%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.61777150916784%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.988716502115656%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.039492242595205%\"\u003e\n \u003cp\u003eT2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.181946403385048%\"\u003e\n \u003cp\u003e1.92(1.27-2.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.424541607898448%\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.02820874471086%\"\u003e\n \u003cp\u003e1.25(0.80-1.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.719322990126939%\"\u003e\n \u003cp\u003e0.335\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.61777150916784%\"\u003e\n \u003cp\u003e1.41(0.87-2.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.988716502115656%\"\u003e\n \u003cp\u003e0.166\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.039492242595205%\"\u003e\n \u003cp\u003eT3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.181946403385048%\"\u003e\n \u003cp\u003e2.62(1.61-4.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.424541607898448%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.02820874471086%\"\u003e\n \u003cp\u003e1.51(0.87-2.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.719322990126939%\"\u003e\n \u003cp\u003e0.130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.61777150916784%\"\u003e\n \u003cp\u003e1.94(1.12-3.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.988716502115656%\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.039492242595205%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u003csub\u003etrend\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.181946403385048%\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.424541607898448%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.02820874471086%\"\u003e\n \u003cp\u003e0.197\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.719322990126939%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.61777150916784%\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.988716502115656%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.895610913404507%\" rowspan=\"5\"\u003e\n \u003cp\u003eDiabetes\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.761565836298932%\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.132858837485173%\"\u003e\n \u003cp\u003e2.71(1.29-5.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.608540925266905%\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.844602609727165%\"\u003e\n \u003cp\u003e2.00(1.03-3.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.015421115065243%\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.658362989323843%\"\u003e\n \u003cp\u003e1.68(0.83-3.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.083036773428233%\"\u003e\n \u003cp\u003e0.152\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.039492242595205%\"\u003e\n \u003cp\u003eT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.181946403385048%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.424541607898448%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.02820874471086%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.719322990126939%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.61777150916784%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.988716502115656%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.039492242595205%\"\u003e\n \u003cp\u003eT2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.181946403385048%\"\u003e\n \u003cp\u003e1.79(1.19-2.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.424541607898448%\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.02820874471086%\"\u003e\n \u003cp\u003e1.01(0.64-1.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.719322990126939%\"\u003e\n \u003cp\u003e0.979\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.61777150916784%\"\u003e\n \u003cp\u003e0.94(0.58-1.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.988716502115656%\"\u003e\n \u003cp\u003e0.805\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.039492242595205%\"\u003e\n \u003cp\u003eT3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.181946403385048%\"\u003e\n \u003cp\u003e2.76(1.74-4.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.424541607898448%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.02820874471086%\"\u003e\n \u003cp\u003e1.62(0.99-2.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.719322990126939%\"\u003e\n \u003cp\u003e0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.61777150916784%\"\u003e\n \u003cp\u003e1.60(0.93-2.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.988716502115656%\"\u003e\n \u003cp\u003e0.090\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.039492242595205%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u003csub\u003etrend\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.181946403385048%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.424541607898448%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.02820874471086%\"\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.719322990126939%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.61777150916784%\"\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.988716502115656%\"\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\u003c/div\u003e\n\u003cp\u003e\u003csup\u003ea\u0026nbsp;\u003c/sup\u003eModel 1: unadjusted\u003c/p\u003e\n\u003cp\u003e\u003csup\u003eb\u0026nbsp;\u003c/sup\u003eModel 2: adjusted for age\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ec\u0026nbsp;\u003c/sup\u003eModel 3: adjusted for age, SBP, DBP, smoking, drinking, hypertension, hyperlipidemia, use of antihypertensives, and use of antilipidemic\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e7\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eAssociation between the\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eNT-proBNP\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;and the risk of carotid artery plaques according to different glucose regulation state and age\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"974\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.93429158110883%\" colspan=\"3\" rowspan=\"2\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"64.06570841889118%\" colspan=\"6\"\u003e\n \u003cp\u003eCarotid artery plaques\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.28%\"\u003e\n \u003cp\u003eOR (95% CI) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.16%\"\u003e\n \u003cp\u003e\u003cem\u003eP-\u003c/em\u003evalue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.12%\"\u003e\n \u003cp\u003eOR (95% CI) \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.16%\"\u003e\n \u003cp\u003e\u003cem\u003eP-\u003c/em\u003evalue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.12%\"\u003e\n \u003cp\u003eOR (95% CI) \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.16%\"\u003e\n \u003cp\u003e\u003cem\u003eP-\u003c/em\u003evalue\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.564102564102564%\" rowspan=\"15\"\u003e\n \u003cp\u003e\u0026le;60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.35897435897436%\" rowspan=\"5\"\u003e\n \u003cp\u003eNormal glucose regulation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.9743589743589745%\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.64102564102564%\"\u003e\n \u003cp\u003e1.42(1.08-1.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.794871794871795%\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.538461538461538%\"\u003e\n \u003cp\u003e1.34(1.03-1.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.794871794871795%\"\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.538461538461538%\"\u003e\n \u003cp\u003e1.33(0.96-1.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.794871794871795%\"\u003e\n \u003cp\u003e0.085\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.812409812409813%\"\u003e\n \u003cp\u003eT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.19191919191919%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.966810966810966%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.047619047619047%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.966810966810966%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.047619047619047%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.966810966810966%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.812409812409813%\"\u003e\n \u003cp\u003eT2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.19191919191919%\"\u003e\n \u003cp\u003e1.11(0.74-1.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.966810966810966%\"\u003e\n \u003cp\u003e0.621\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.047619047619047%\"\u003e\n \u003cp\u003e1.08(0.72-1.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.966810966810966%\"\u003e\n \u003cp\u003e0.727\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.047619047619047%\"\u003e\n \u003cp\u003e0.84(0.53-1.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.966810966810966%\"\u003e\n \u003cp\u003e0.447\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.812409812409813%\"\u003e\n \u003cp\u003eT3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.19191919191919%\"\u003e\n \u003cp\u003e1.76(1.10-2.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.966810966810966%\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.047619047619047%\"\u003e\n \u003cp\u003e1.54(0.95-2.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.966810966810966%\"\u003e\n \u003cp\u003e0.082\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.047619047619047%\"\u003e\n \u003cp\u003e1.51(0.87-2.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.966810966810966%\"\u003e\n \u003cp\u003e0.141\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.812409812409813%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u003csub\u003etrend\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.19191919191919%\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.966810966810966%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.047619047619047%\"\u003e\n \u003cp\u003e0.082\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.966810966810966%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.047619047619047%\"\u003e\n \u003cp\u003e0.075\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.966810966810966%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.80672268907563%\" rowspan=\"5\"\u003e\n \u003cp\u003ePrediabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.966386554621849%\"\u003e\n \u003cp\u003e0.95(0.77-1.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.123649459783914%\"\u003e\n \u003cp\u003e0.637\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.846338535414166%\"\u003e\n \u003cp\u003e0.94(0.77-1.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.123649459783914%\"\u003e\n \u003cp\u003e0.581\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.846338535414166%\"\u003e\n \u003cp\u003e0.89(1.72-1.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.123649459783914%\"\u003e\n \u003cp\u003e0.279\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.812409812409813%\"\u003e\n \u003cp\u003eT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.19191919191919%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.966810966810966%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.047619047619047%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.966810966810966%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.047619047619047%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.966810966810966%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.812409812409813%\"\u003e\n \u003cp\u003eT2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.19191919191919%\"\u003e\n \u003cp\u003e1.71(0.98-2.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.966810966810966%\"\u003e\n \u003cp\u003e0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.047619047619047%\"\u003e\n \u003cp\u003e1.81(1.02-3.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.966810966810966%\"\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.047619047619047%\"\u003e\n \u003cp\u003e1.92(1.01-3.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.966810966810966%\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.812409812409813%\"\u003e\n \u003cp\u003eT3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.19191919191919%\"\u003e\n \u003cp\u003e1.79(1.04-3.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.966810966810966%\"\u003e\n \u003cp\u003e0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.047619047619047%\"\u003e\n \u003cp\u003e1.62(0.92-2.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.966810966810966%\"\u003e\n \u003cp\u003e0.095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.047619047619047%\"\u003e\n \u003cp\u003e1.66(0.88-3.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.966810966810966%\"\u003e\n \u003cp\u003e0.117\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.812409812409813%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u003csub\u003etrend\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.19191919191919%\"\u003e\n \u003cp\u003e0.093\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.966810966810966%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.047619047619047%\"\u003e\n \u003cp\u003e0.224\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.966810966810966%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.047619047619047%\"\u003e\n \u003cp\u003e0.389\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.966810966810966%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.80672268907563%\" rowspan=\"5\"\u003e\n \u003cp\u003eDiabetes\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.966386554621849%\"\u003e\n \u003cp\u003e3.41(1.30-8.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.123649459783914%\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.846338535414166%\"\u003e\n \u003cp\u003e3.27(1.26-8.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.123649459783914%\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.846338535414166%\"\u003e\n \u003cp\u003e2.94(1.04-8.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.123649459783914%\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.812409812409813%\"\u003e\n \u003cp\u003eT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.19191919191919%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.966810966810966%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.047619047619047%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.966810966810966%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.047619047619047%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.966810966810966%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.812409812409813%\"\u003e\n \u003cp\u003eT2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.19191919191919%\"\u003e\n \u003cp\u003e1.66(1.02-2.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.966810966810966%\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.047619047619047%\"\u003e\n \u003cp\u003e1.55(0.95-2.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.966810966810966%\"\u003e\n \u003cp\u003e0.078\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.047619047619047%\"\u003e\n \u003cp\u003e1.50(0.88-2.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.966810966810966%\"\u003e\n \u003cp\u003e0.141\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.812409812409813%\"\u003e\n \u003cp\u003eT3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.19191919191919%\"\u003e\n \u003cp\u003e2.23(1.38-3.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.966810966810966%\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.047619047619047%\"\u003e\n \u003cp\u003e2.18(1.34-3.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.966810966810966%\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.047619047619047%\"\u003e\n \u003cp\u003e2.01(1.19-3.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.966810966810966%\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.812409812409813%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u003csub\u003etrend\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.19191919191919%\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.966810966810966%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.047619047619047%\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.966810966810966%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.047619047619047%\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.966810966810966%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.564102564102564%\" rowspan=\"15\"\u003e\n \u003cp\u003e＞60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.35897435897436%\" rowspan=\"5\"\u003e\n \u003cp\u003eNormal glucose regulation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.9743589743589745%\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.64102564102564%\"\u003e\n \u003cp\u003e2.71(1.79-7.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.794871794871795%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.538461538461538%\"\u003e\n \u003cp\u003e3.12(1.51-6.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.794871794871795%\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.538461538461538%\"\u003e\n \u003cp\u003e2.52(1.14-5.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.794871794871795%\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.812409812409813%\"\u003e\n \u003cp\u003eT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.19191919191919%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.966810966810966%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.047619047619047%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.966810966810966%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.047619047619047%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.966810966810966%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.812409812409813%\"\u003e\n \u003cp\u003eT2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.19191919191919%\"\u003e\n \u003cp\u003e1.16(0.80-1.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.966810966810966%\"\u003e\n \u003cp\u003e0.432\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.047619047619047%\"\u003e\n \u003cp\u003e1.23(0.85-1.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.966810966810966%\"\u003e\n \u003cp\u003e0.280\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.047619047619047%\"\u003e\n \u003cp\u003e1.10(0.74-1.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.966810966810966%\"\u003e\n \u003cp\u003e0.636\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.812409812409813%\"\u003e\n \u003cp\u003eT3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.19191919191919%\"\u003e\n \u003cp\u003e1.94(1.30-2.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.966810966810966%\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.047619047619047%\"\u003e\n \u003cp\u003e1.85(1.23-2.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.966810966810966%\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.047619047619047%\"\u003e\n \u003cp\u003e1.75(1.12-2.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.966810966810966%\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.812409812409813%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u003csub\u003etrend\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.19191919191919%\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.966810966810966%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.047619047619047%\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.966810966810966%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.047619047619047%\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.966810966810966%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.80672268907563%\" rowspan=\"5\"\u003e\n \u003cp\u003ePrediabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.966386554621849%\"\u003e\n \u003cp\u003e1.61(0.97-2.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.123649459783914%\"\u003e\n \u003cp\u003e0.066\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.846338535414166%\"\u003e\n \u003cp\u003e1.45(0.91-2.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.123649459783914%\"\u003e\n \u003cp\u003e0.121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.846338535414166%\"\u003e\n \u003cp\u003e7.04(1.92-25.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.123649459783914%\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.812409812409813%\"\u003e\n \u003cp\u003eT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.19191919191919%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.966810966810966%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.047619047619047%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.966810966810966%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.047619047619047%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.966810966810966%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.812409812409813%\"\u003e\n \u003cp\u003eT2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.19191919191919%\"\u003e\n \u003cp\u003e1.24(0.80-1.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.966810966810966%\"\u003e\n \u003cp\u003e0.342\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.047619047619047%\"\u003e\n \u003cp\u003e1.23(0.79-1.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.966810966810966%\"\u003e\n \u003cp\u003e0.359\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.047619047619047%\"\u003e\n \u003cp\u003e1.28(0.80-2.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.966810966810966%\"\u003e\n \u003cp\u003e0.306\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.812409812409813%\"\u003e\n \u003cp\u003eT3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.19191919191919%\"\u003e\n \u003cp\u003e2.33(1.40-3.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.966810966810966%\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.047619047619047%\"\u003e\n \u003cp\u003e2.08(1.25-3.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.966810966810966%\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.047619047619047%\"\u003e\n \u003cp\u003e2.46(1.40-4.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.966810966810966%\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.812409812409813%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u003csub\u003etrend\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.19191919191919%\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.966810966810966%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.047619047619047%\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.966810966810966%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.047619047619047%\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.966810966810966%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.80672268907563%\" rowspan=\"5\"\u003e\n \u003cp\u003eDiabetes\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.966386554621849%\"\u003e\n \u003cp\u003e1.15(0.76-1.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.123649459783914%\"\u003e\n \u003cp\u003e0.501\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.846338535414166%\"\u003e\n \u003cp\u003e1.18(0.86-1.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.123649459783914%\"\u003e\n \u003cp\u003e0.304\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.846338535414166%\"\u003e\n \u003cp\u003e1.15(0.76-1.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.123649459783914%\"\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.812409812409813%\"\u003e\n \u003cp\u003eT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.19191919191919%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.966810966810966%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.047619047619047%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.966810966810966%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.047619047619047%\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.966810966810966%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.812409812409813%\"\u003e\n \u003cp\u003eT2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.19191919191919%\"\u003e\n \u003cp\u003e1.01(0.65-1.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.966810966810966%\"\u003e\n \u003cp\u003e0.979\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.047619047619047%\"\u003e\n \u003cp\u003e1.05(0.67-1.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.966810966810966%\"\u003e\n \u003cp\u003e0.829\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.047619047619047%\"\u003e\n \u003cp\u003e0.93(0.58-1.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.966810966810966%\"\u003e\n \u003cp\u003e0.746\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.812409812409813%\"\u003e\n \u003cp\u003eT3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.19191919191919%\"\u003e\n \u003cp\u003e1.74(1.07-2.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.966810966810966%\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.047619047619047%\"\u003e\n \u003cp\u003e1.75(1.08-2.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.966810966810966%\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.047619047619047%\"\u003e\n \u003cp\u003e1.81(1.05-3.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.966810966810966%\"\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.812409812409813%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u003csub\u003etrend\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.19191919191919%\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.966810966810966%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.047619047619047%\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.966810966810966%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.047619047619047%\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.966810966810966%\"\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\u003c/div\u003e\n\u003cp\u003e\u003csup\u003ea\u0026nbsp;\u003c/sup\u003eModel 1: unadjusted\u003c/p\u003e\n\u003cp\u003e\u003csup\u003eb\u0026nbsp;\u003c/sup\u003eModel 2: adjusted for sex\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ec\u0026nbsp;\u003c/sup\u003eModel 3: adjusted for sex, SBP, DBP, smoking, drinking, hypertension, hyperlipidemia, use of antihypertensives, and use of antilipidemic\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"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":"circulating N-terminal pro B-type natriuretic peptide, carotid artery plaque, glucose metabolic states, coronary heart disease","lastPublishedDoi":"10.21203/rs.3.rs-3298912/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3298912/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eObjective:\u003c/strong\u003e\u003c/em\u003e Circulating N-terminal pro B-type natriuretic peptide(NT-proBNP) is often used as a marker of heart failure in patients with coronary heart disease (CHD), and it is associated with glycaemic abnormalities. Studies on the association and diagnostic value of NT-proBNP with carotid plaque (CAP) in patients with CHD are limited.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e \u003c/strong\u003eThis study included 5,093 patients diagnosed with CHD. Their NT-proBNP levels, blood glucose levels, the occurrence of CAP, and the number and nature of CAP were measured, and glucose metabolic status was defined as normoglycaemic (NG), prediabetes (Pre-DM), and diabetes mellitus (DM); logistic regression analyses were used to compare the relationship between NT-proBNP and the risk of CAP occurrence, and the number and nature of CAP. Patients were divided into three groups according to NT-proBNP tertiles. The diagnostic value of NT-proBNP for CAP risk was measured using ROC curves. There was a significant correlation between NT-proBNP and the risk of CAP in CHD patients in different glucose metabolic states.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eResults:\u003c/strong\u003e\u003c/em\u003eMultiple logistic regression results showed that consecutive NT-proBNP was associated with risk of CAP progression. When NT-proBNP levels at T1 was used as the reference, T3 were associated with an increased OR for CAP (OR: 1.69; 95% CI: 1.37-2.10). This association was also present in the in both the pre-DM and DM states, which with the highest OR in the DM state (OR: 1.84; 95% CI: 1.26-2.69). This association was significant in DM status in men and pre-DM in women, and was significant in DM status in patients ≤60 years of age and in pre-DM and DM in patients \u0026gt;60 years of age. We also measured the diagnostic accuracy of CAP occurrence in NT-proBNP in CHD patients. The critical values were 182ng/l, with an AUC value of 0.627(95% CI: 0.592-0.631).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e \u003c/strong\u003eThe increase in NT-proBNP is significantly associated with the risk of CAP in patients with CHD, and exhibits specific characteristics under different glucose metabolism states, this association exists between different sexes and ages.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTrial registration: \u003c/strong\u003eThe study was approved by the Ethics Committee of Tianjin University of Traditional Chinese Medicine (approval number TJUTCM-EC20210007) and certified by the Chinese Clinical Trials Registry on April 4, 2022 (registration number ChiCTR2200058296) and on March 25, 2022 by ClinicalTrials.gov (registration number NCT05309343).\u003c/p\u003e","manuscriptTitle":"Relationship between the circulating N-terminal pro B-type natriuretic peptide and the risk of carotid artery plaque in different glucose metabolic states in patients with coronary heart disease: a CSCD-TCM plus study in China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-08-31 23:35:22","doi":"10.21203/rs.3.rs-3298912/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2023-09-21T17:14:54+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2023-08-30T13:57:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"22477e56-00c1-4a3a-b517-f3b73dfe9c48","date":"2023-08-30T08:47:44+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2023-08-30T06:59:09+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2023-08-28T14:20:27+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2023-08-28T14:20:26+00:00","index":"","fulltext":""},{"type":"submitted","content":"Cardiovascular Diabetology","date":"2023-08-26T16:04:46+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":"5ca0dcfc-229f-422a-bc9d-ffb1bf743501","owner":[],"postedDate":"August 31st, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2023-11-06T15:04:07+00:00","versionOfRecord":{"articleIdentity":"rs-3298912","link":"https://doi.org/10.1186/s12933-023-02015-y","journal":{"identity":"cardiovascular-diabetology","isVorOnly":false,"title":"Cardiovascular Diabetology"},"publishedOn":"2023-11-02 15:01:34","publishedOnDateReadable":"November 2nd, 2023"},"versionCreatedAt":"2023-08-31 23:35:22","video":"","vorDoi":"10.1186/s12933-023-02015-y","vorDoiUrl":"https://doi.org/10.1186/s12933-023-02015-y","workflowStages":[]},"version":"v1","identity":"rs-3298912","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3298912","identity":"rs-3298912","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}