High-Sensitivity Cardiac Troponin T is A Risk Factor for Major Adverse Cardiovascular Events and All-Cause Mortality: 9.5 Year Follow-up Study

High-Sensitivity Cardiac Troponin T is A Risk Factor for Major Adverse Cardiovascular Events and All-Cause Mortality: 9.5 Year Follow-up Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research High-Sensitivity Cardiac Troponin T is A Risk Factor for Major Adverse Cardiovascular Events and All-Cause Mortality: 9.5 Year Follow-up Study Xiaona Wang, Ruihua Cao, Xu Yang, Wenkai Xiao, Yun Zhang, Ping Ye This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-68461/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: The relationship between high-sensitivity cardiac troponin T (hs-cTnT) and different cardiovascular events has been observed in several large community studies, and the results have been controversial. However, there is currently no cross-sectional or longitudinal follow-up study on hs-cTnT in the Chinese population. Methods: We analyzed the association of plasma hs-cTnT levels with major adverse cardiovascular events and all-cause mortality in 1325 subjects from a longitudinal follow-up community-based population in Beijing, China. Results: In the Cox proportional hazards models analysis, the risk of MACE increased with the increase of hs-cTnT levels (HR, 1.223, 95% CI, 1.054–1.418, P = 0.008). Increased hs-cTnT levels were associated with coronary events (HR, 1.391, 95% CI, 1.106–1.749, P = 0.005) in Model 4. Cox proportional risk regression model analysis revealed that increased hs-cTnT levels were associated with an increased risk of mortality (HR, 1.763, 95% CI, 1.224–2.540, P = 0.002), even after adjusting hs-CRP and NT-proBNP. The area under the ROC curve for predicting MACE was 0.559 (95% CI, 0.523–0.595, P = 0.001). The areas under the ROC curve for predicting coronary events and mortality were 0.629 (95% CI, 0.580–0.678, P < 0.001) and 0.644 (95% CI, 0.564–0.725, P < 0.001), respectively. Conclusions: Our findings in the Chinese cohort support that hs-cTnT is a risk factor for major adverse cardiovascular events and all-cause mortality. Translational Medicine hs-cTnT major adverse cardiovascular events all-cause mortality Figures Figure 1 Figure 2 Background It is extremely challenging to predict cardiovascular events in the general population; while the general population is unlikely to become a target of preventive measures, such measures have become a mainstay of how society addresses cardiovascular disease [ 1 ]. Traditional risk factors such as hypertension, diabetes, hyperlipidemia, smoking, and obesity play important roles in the occurrence and development of cardiovascular disease. They can also be used as important prognostic factors for risk stratification and the prognostic evaluation of patients, but these traditional risk factors cannot be used to explain all prognostic risks. In recent years, new biomarkers, such as high-sensitivity cardiac troponin T (hs-cTnT) and nitrogen-terminated B-type brain key precursors (NT-proBNP), have been widely used in clinical practice and have been proven to be risk factors for cardiovascular disease independent of traditional risk factors. cTnT can be effectively used to discover subclinical heart disease and assess the risk of future cardiovascular disease, but the low detection rate of standard measurement methods limits its clinical application [ 2 ]. It is possible to measure cTnT with high sensitivity with the advancement of science and technology, and high-sensitivity cardiac troponin T (hs-cTnT) has emerged. Studies have found that the concentration of hs-cTnT in patients with stable coronary heart disease is significantly related to cardiovascular death or congestive heart failure [ 3 , 4 ]. Subsequently, the prognostic value of hs-cTnT in the general population was also confirmed. A study involving 4,221 elderly community residents with a median follow-up time of 11.8 years found that baseline hs-cTnT and the change of hs-cTnT was significantly related to the incidence of heart failure and cardiovascular death [ 5 ]. Since then, the relationship between hs-cTnT and different cardiovascular events has been observed in several large community studies, and the results have been controversial [ 6 – 8 ]. However, there is currently no cross-sectional or longitudinal follow-up study on hs-cTnT in the Chinese population. Therefore, the current study examined the relationship of hs-cTnT with mortality and cardiovascular events by investigating a community population without definite cardiovascular disease to ascertain the following: (1) the predictive relationship between the hs-cTnT level and mortality and cardiovascular events and (2) the predictive relationship between the change in hs-cTnT level and mortality and cardiovascular events in a large community-based longitudinal sample from China. Methods Subjects After a routine health check-up between September 2007 and January 2009, a total of 1,680 subjects were initially eligible for cross-sectional analysis. During the follow-up from February 2013 to September 2013, 181 people were lost to follow-up due to various reasons. Finally, 1,499 subjects with complete data were included in the 5-year follow-up analysis (follow-up rate 89.2%). During the follow-up from June 2017 to September 2018, 174 people were lost to follow-up, and eventually 1,325 subjects completed the follow-up (follow-up rate 89.2%). The research protocol of this project was approved by the ethics committee of the General Hospital of the PLA, and each subject provided informed written consent. The median follow-up interval for the original subjects was 9.5 years. During these visits, all participants received a questionnaire survey. Physical examinations, biochemical indicators, and biomarkers (including hs-cTnT) were reviewed at the same time. Any event reported was subject to verification through medical records, death certificates, pathological autopsy results and objective coronary angiographic examination results and were jointly judged by two clinically experienced doctors. Clinical data collection Height (cm) and weight (kg) were measured. Systolic and diastolic blood pressures (SBP and DBP) were measured in the right arm twice in a sitting position after 5 min of rest. Blood samples were collected from participants after an overnight fast. Concentrations of fasting blood glucose (FBG), total cholesterol (TC), triglycerides (TGs), low density lipoprotein cholesterol (LDL-C), high density lipoprotein cholesterol (HDL-C), homocysteine (Hcy), and uric acid The Roche Diagnostics GmbH (Roche Diagnostics GmbH, Mannheim, Germany) was used to detect on the Roche Diagnostics, Indianapolis, Indiana; the serum creatinine (Scr) was tested on the Hitachi 7600 by the Roche Diagnostics GmbH (Roche Diagnostics GmbH). (Hitachi, Tokyo, Japan); high-sensitivity C-reactive protein (hs-CRP) was detected on a Diension RxL Max analyzer (Siemens Healthcare Diagnostic) using an immunoassay method kit (Siemens Healthcare Diagnostic, USA, IN); N-terminal B-type brain natriuretic peptide (NT-proBNP) was detected on an autoanalyzer using the electroluminescence method using a Roche Diagnostics GmbH (Roche Diagnostics GmbH); hs-cTnT) was measured using the Elecsys Troponin T high-sensitivity kit (Roche Diagnostics GmbH, Mannheim, Germany) by a laboratory professional using an electroluminescence immunoassay method on a Modular Analytics E170 (Roche Diagnostics) instrument. The concentration unit of hs-cTnT is pg/mL, and the coefficient of variation between batches is 8% at 10 pg/mL and 2.5% at 100 pg/mL [9]. Definition of variables Body mass index (BMI): weight (kg)/height2 (m2). Hypertension: SBP ≥ 140 mmHg and/or DBP 90 mmHg or those who have taken antihypertensive drugs. Diabetes mellitus: fasting venous blood glucose ≥ 7.0 mmol/L, OGTT test with 2 h blood glucose ≥ 11.1 mmol/L, symptoms of hyperglycemia and random blood glucose ≥ 11.1 mmol/L, or receiving hypoglycemic treatment [10]. Estimated glomerular filtration rate (eGFR): 141 × min (Scr/k,1)α × max (Scr/k,1)-1.209 × 0.993Age × 1.018 (if female) × 1.159 (if Black), where Scr is plasma creatinine (mg/dL), k is 0.7 for females and 0.9 for males, α is -0.329 for females and -0.411 for males, min indicates the minimum of Scr/k or 1, and max indicates the maximum of Scr/k or 1. hs-cTnT: The lowest detectable concentration according to the kit instructions is 3 pg/mL (according to the reagent instructions), which is used as the cut-off value in this study. Therefore, hs-cTNT levels ≥ 3 pg/mL are considered measurable. The 99th percentile for the hs-cTnT of a group of healthy people aged 20 to 70 years was 14 pg/mL [11], and hs-cTnT concentrations ≥ 14 pg/mL were generally considered to be elevated. All-cause mortality was determined by review of death certificates. The definition of MACE comprised nonfatal myocardial infarction, newly diagnosed CHD (identified by coronary artery imaging or receiving coronary revascularization), stroke (ischemic or hemorrhagic) and cardiovascular mortality. Cardiovascular death was defined as deaths related to atherosclerotic heart disease (fatal myocardial infarction and definite fatal coronary heart disease), cerebrovascular disease deaths (fatal stroke), and causes including heart failure death from other atherosclerotic and cardiovascular diseases [12]. Cardiovascular events were defined as cardiovascular death, nonfatal myocardial infarction, coronary revascularization, coronary heart disease and stroke confirmed by coronary imaging [13]. Coronary heart disease events were defined as coronary heart disease death, nonfatal myocardial infarction, coronary revascularization, and coronary heart disease diagnosed by coronary imaging. Stroke was defined as acute, focal damage to the central nervous system caused by vascular causes, which results in neurological deficits, including cerebral infarction (rather than internal hemorrhage) and subarachnoid hemorrhage [14]. Statistical analyses Continuous variables are expressed as the mean or median (interquartile range) ± standard deviation (SD) and dichotomous variables are expressed as percentages. Analysis of continuous variables was performed by t test, and analysis of categorical variables was performed by χ2 test. Pearson regression analysis and stepwise multivariate linear regression analysis were performed to evaluate the associations between baseline hs-cTnT and baseline traditional cardiovascular risk factors. For the analyses of hs-cTnT as a categorical variable, subjects were divided into three groups according to their baseline hs-cTnT level: hs-cTnT < 3 pg/mL, hs-cTnT between 3 and 14 pg/mL, and hs-cTnT ≥ 14 pg/mL. The relationship between baseline hs-cTnT levels and MACE and coronary events and mortality were analyzed by Cox proportional hazards models. Models were defined as follows: model 1 = adjusted for age and gender; model 2 = adjusted for model 1 + presence of hypertension or diabetes mellitus, current smoking status, SBP, postprandial blood glucose, TC, HDL-C, antihypertensive medication use and antidiabetic medication use; model 3 = adjusted for model 2 + eGFR; model 4 = adjusted for model 3 + hs-CRP and NT-proBNP (both after logarithmic transformation). We also analyzed the relationship between hs-cTnT as a continuous variable and endpoints, in which values of cTnT that were below the detection limit were assigned to 1.5 ng/L (i.e., one-half of the lower limit of detection). SPSS17.0 software was used for all statistical analyses. P value < 0.05 was considered statistically significant. Results General characteristics of the study population The general characteristics of the population are shown in Table 1 . In this population, the average age was 59.10 ± 9.8 years, and 51.5% were females. Of these 1,325 people, 736 had a detectable level of hs-cTnT (> 3.0 pg/ml ), accounting for 55.54% of the total, as shown in Table 1 . The distribution range of hs-cTnT was from 3.03 pg/ml to 176.4 pg/ml, and the median was 7.36 pg/ml (25% and 75% digits were 4.76 pg/ml and 15.58 pg/ml, respectively). There were 143 people with an increased hs-cTnT (≥ 14.0 pg/ml), accounting for 10.79% of the total. Comparing the three groups, the cardiovascular risk factors in the middle and increased groups were significantly higher than those in the lower group. In addition, cardiovascular risk factors, such as being male, history of diabetes, history of hypertension, FBG, uric acid, Hcy, etc., in the increased group were significantly different from those in the other two groups. Table 1 Baseline characteristics and laboratory test results of subjects Hs-TnT group(ng/L) Group 1 (n = 589) Group 2 (n = 593) Group 3 (n = 143) P值 Age (岁) 58.4 ± 12.3 60.7 ± 11.7 62.7 ± 11.1 0.010 Male (n(%)) 155(26.31%) 308(51.94%) 89(62.2%) 0.001 BMI (kg/m2) 25.4 ± 3.8 25.5 ± 3.6 25.6 ± 3.7 0.872 Smoking (n(%)) 51(8.66%) 108(18.21%) 30(20.97%) 0.022 SBP (mmHg) 131.8 ± 15.6 132.7 ± 17.4 134.7 ± 16.5 0.893 DBP (mmHg) 77.2 ± 12.6 77.1 ± 11.7 77.6 ± 12.5 0.275 TC (mmol/L) 5.05 ± 0.91 5.03 ± 0.93 4.89 ± 0.92 0.259 TG (mmol/L) 1.92 ± 1.69 1.76 ± 1.09 2.07 ± 1.83 0.018 HDL-C(mmol/L) 1.34 ± 0.45 1.38 ± 0.36 1.33 ± 0.32 0.298 LDL-C(mmol/L) 2.96 ± 0.75 2.92 ± 0.71 2.86 ± 0.74 0.512 FBG (mmol/L) 5.63 ± 2.24 5.29 ± 1.46 5.85 ± 2.22 <.001 Scr(mmol/L) 67.61 ± 15.91 66.27 ± 16.48 73.49 ± 20.29 <.001 eGFR(ml/min − 1 /1.73 m 2 ) 90.57 ± 14.83 87.38 ± 13.73 85.14 ± 13.51 <.001 hsCRP(mg/L) 2.4(1.3, 3.4) 2.3(1.4, 3.4) 2.4(1.6, 3.6) 0.365 BNP 35.51 ± 15.03 38.32 ± 10.95 48.55 ± 18.37 <.001 Hs-TnT 5.24 ± 2.79 26.06 ± 17.94 <.001 TC, total cholesterol; HDL-C, high-density lipoprotein cholesterol; TG, triglyceride; LDL-C, low-density lipoprotein cholesterol; SBP, systolic blood pressure; DBP, diastolic blood pressure; BMI, body mass index; FBG, fast blood glucose; eGFR, estimated glomerular filtration rate;HR, heart rate༛Scr, Serum creatinine; Hs-CRP, High sensitive C-reactive protein; BNP, Brain Natriuretic Peptide; Hs-TnT, High sensitivity troponin T Clinical factors affecting hs-cTnT At baseline, univariate analysis showed positive correlations between being elderly and male, smoking, hypertension, Scr, FBG, BNP, hs-CRP and hs-cTnT (after natural logarithmic conversion); eGFR was negatively correlated with hs-cTnT. Multivariate linear regression analysis showed that only being female, hypertension, FBG, TC and LDL-C were positively correlated with hs-cTnT, while eGFR was negatively correlated with hs-cTnT (Table 2 ). Table 2 ． Clinical factors affecting Hs-TnT Hs-TnT Hs-TnT r P-value β CI P-value All subjects(n=1325) Age 0.085 0.004 0.002 -0.002～0.007 0.308 Male 0.120 <0.001 0.725 0.340～1.110 <0.001 Smoking 0.110 0.001 0.320 -0.086～0.725 0.122 Diabetes 0.144 <0.001 0.232 0.058～0.522 0.117 Hypertension 0.278 <0.001 0.883 0.501～1.265 <0.001 TG* 0.013 0.646 0.044 -0.002～0.091 0.063 HDL-C* 0.029 0.321 0.100 -0.070～0.270 0.250 LDL-C 0.019 0.515 0.222 0.077～0.367 0.003 TC 0.049 0.092 0.227 0.102～0.351 <0.001 SBP 0.011 0.702 0.046 0.033～0.060 0.872 DBP 0.049 0.093 0.002 0.000～0.003 0.010 BMI 0.033 0.258 0.010 -0.002～0.007 0.003 FBG 0.065 0.026 0.064 0.037～0.091 <0.001 Cr 0.101 0.001 0.231 0.098～0.391 0.565 eGFR* -0.091 0.002 -0.596 -0.915～-0.277 <0.001 Hs-CRP 0.087 0.005 0.044 -0.009～0.097 0.105 BNP 0.119 <0.001 0.016 -0.022～0.055 0.411 TC, total cholesterol; HDL-C, high- density lipoprotein cholesterol; TG, triglyceride; LDL-C, low-density lipoprotein cholesterol; SBP, systolic blood pressure; DBP, diastolic blood pressure; BMI, body mass index; FBG, fast blood glucose; eGFR, estimated glomerular filtration rate；HR, heart rate; Scr, Serum creatinine; Hs-CRP, High sensitive C-reactive protein; BNP, Brain Natriuretic Peptide; Hs-TnT, High sensitivity troponin T *: natural logarithm transformed §: Covariates in the multiple-adjusted models included age, gender, hypertension, Diabetes, current smoking, levels of plasma TC, TG, LDL-C, HDL-C, SBP, DBP, FBG, BMI, Cr, eGFR, Hs-CRP and BNP. Associations of baseline hs-cTnT levels with major adverse cardiovascular events (MACE), coronary events and mortality During the follow-up period, a total of 191 participants experienced MACE, and the incidence increased from 11.05% in the lowest (hs-cTnT 14 pg/ml), as demonstrated by Kaplan-Meier survival analysis (P < 0.001) (Fig. 1 -A). In addition, after adjusting for multiple factors in the Cox proportional hazards models analysis (Model 4), the risk of MACE increased with the increase of hs-cTnT levels (HR, 1.223, 95% CI, 1.054–1.418, P = 0.008) (Table 3 ). Table 3 Cox proportional hazards models analysis for associations between baseline hs-cTnT Mace Coronary event All cause Mortality HR (95% CI) P值 HR (95% CI) P值 HR (95% CI) P值 Unjust 1.313(1.144–1.506) < 0.001 1.747(1.440–2.210) < 0.001 1.996(1.470–2.709) < 0.001 Model1 1.188(1.026–1.375) 0.021 1.395(1.122–1.734) 0.003 1.472(1.025–2.115) 0.036 Model1 1.183(1.016–1.377) 0.030 1.392(1.110–1.746) 0.004 1.657(1.145–2.397) 0.007 Model1 1.223(1.054–1.418) 0.008 1.391(1.106–1.749) 0.005 1.763(1.224–2.540) 0.002 Models are defined as follows: model 1 = adjusted for age and gender; model 2 = adjusted for model 1 + presence of hypertension or diabetes mellitus, current smoking status, systolic blood pressure, postprandial blood glucose, total cholesterol, high-density lipoprotein cholesterol, antihypertensive medication use and antidiabetic medication use; model 3 = adjusted for model 2 + estimated glomerular filtration rate; model 4 = adjusted for model 3 + high-sensitivity C reactive protein and N-terminal pro-B-type natriuretic peptide (both after logarithmic transformation). MACE, major adverse cardiovascular event. Similar trends were discovered for coronary events. A total of 121 participants experienced coronary events, and the risk for coronary events and all-cause mortality by baseline hs-cTnT level is shown in Fig. 1 -B. Increased hs-cTnT levels were associated with coronary events (HR, 1.391, 95% CI, 1.106–1.749, P = 0.005) in Model 4 (Table 3 ). During the median follow-up of 9.5 years, a total of 84 deaths occurred, and the mortality rate increased significantly from 8.08% in the lowest quartile group (hs-cTnT 14 pg/mL) (P < 0.01) (Fig. 1 -C). After adjusting for age, gender, blood pressure, blood lipids, renal function, and other traditional cardiovascular risk factors, Cox proportional risk regression model analysis revealed that increased hs-cTnT levels were associated with an increased risk of mortality (HR, 1.763, 95% CI, 1.224–2.540, P = 0.002), even after adjusting hs-CRP and NT-proBNP (Model 4) (Table 3 ). hs-cTnT predicts MACE and mortality The ROC curve was used to analyze the accuracy of hs-cTnT in predicting MACE (Fig. 2 -A, 2 -B, and 2 -C). The area under the ROC curve for predicting MACE was 0.559 (95% CI, 0.523–0.595, P = 0.001) (Fig. 2 -A). The best cut-off value of hs-cTnT for predicting MACE was 5.01 pg/mL. At the same time, the sensitivity and specificity were 53.8% and 59.1%, respectively. The areas under the ROC curve for predicting coronary events and mortality were 0.629 (95% CI, 0.580–0.678, P < 0.001) (Fig. 2 -B) and 0.644 (95% CI, 0.564–0.725, P < 0.001) (Fig. 2 -C), respectively. The best cut-off value of hs-cTnT for predicting coronary events and mortality were 4.05 pg/mL and 4.6 pg/mL, respectively. When the hs-cTnT value was 4.05 pg/mL, the sensitivity for predicting coronary events was 72.5% and the specificity was 49.8%. When the hs-cTnT value was 4.6 pg/mL, the sensitivity for predicting mortality was 74.1%, and the specificity was 51.4%. Discussion To our knowledge, this study is the first follow-up study to evaluate the predictive value of hs-cTnT for cardiovascular events and mortality conducted in China. Through the follow-up study of this community population for nearly 10 years, this study has some significance. First, we observed minor subclinical myocardial injury in the general population using the latest generation of hs-cTnT measurement methods, rather than only focusing on the elderly or high-risk population. We found that the hs-cTnT detection rate in this population was 54.70%. Second, the results confirmed that the baseline level of hs-cTnT can predict the mortality and cardiovascular event risk of the population of the community. Furthermore, we also found that increases in hs-cTnT also have predictive value for cardiovascular events and mortality. In recent years, highly sensitive methods for detecting hs-cTnT have increased in clinical practice. At present, there is not a very clear definition of hs-cTnT, which is mainly based on the analytical performance of the lowest detection limit and the measurement precision in the low cTnT concentration range. The definition of hs-cTnT is as follows [ 15 – 17 ]: high-sensitivity methods can detect cTnT (e.g., as low as 10 ng/L) levels that cannot be detected by current traditional methods; the cTnT with the minimum detection value of CV ≤ 10% and the 99th percentile value detected by the system or reagent that meets the requirements of the guidelines; or the cTnT can be detected in some or all of the surface healthy people and the 99th percentile value/CV ≤ 10%. In 2018, the International Federation of Clinical Chemistry and Laboratory Medicine released the latest specific quality standards for troponin detection [ 18 ], which proposed that high-sensitivity troponin should meet the detection rate of more than 50% in apparently healthy males and females. New research has also shown differences in hs-cTnT levels among different ethnic groups [ 19 , 20 ]. The results of this study suggest that the prevalence of hs-cTnT levels > 14.0 pg/mL was approximately 11.0%, which is slightly higher than the previous studies. In the current study, the detection rate of hs-cTnT in the general population in the Beijing community was 70.65% for males and 43.85% for females, respectively, which was similar to foreign studies. Our findings are slightly different from the previous study, probably because of the middle-aged and elderly in the study. Although there is no clear history of cardiovascular disease, traditional cardiovascular risk factors, such as hypertension, diabetes, and smoking, are still common in this group of middle-aged and elderly people. Undiagnosed resting myocardial ischemia and chronic changes in cardiac structure and function may still possibly be present. hs-cTnT has been recognized as the first marker of myocardial injury in the diagnosis, risk stratification and prognosis of acute coronary syndrome (ACS), and its value has been recognized at home and abroad [ 21 ]. Gareth L et al concluded that early elevation in plasma hs-cTnT within 24 h of elective noncardiac surgery precedes the subsequent development of noncardiac organ dysfunction[ 22 ].Because of its high sensitivity and specificity, hs-cTnT has been agreed upon by the European Heart Association and Chinese experts as the best basis for early diagnosis of acute myocardial infarction (AMI) and risk stratification of heart diseases [ 23 ]. In predicting future events, multiple trials have confirmed an association between elevated hs-cTnT levels and mortality or cardiovascular death in some populations at high risk for cardiovascular disease. In addition, few studies have reported the association of hs-cTnT levels with cardiovascular events in individuals from a general population, and the results have been controversial. The Atherosclerosis Risk in Communities (ARIC) Study found an association between detectable cTnT and a highly sensitive assay was associated with incident CHD, mortality, and HF in individuals without known CHD/stroke [ 24 ]. Evidence from a prospective study of the cTnT detected with a highly sensitive assay supports a stronger association with structural heart disease and subsequent risk for all-cause mortality [ 25 ]. Changes in cTnT levels measured with a highly sensitive assay were found to be significantly associated with incident HF and cardiovascular death [ 26 ]. Welsh did not identify a significant relationship between baseline cTnT levels and some CVD outcomes, whereas they reported that cTnT is more strongly associated with the risk of non-CVD death [ 27 ]. Although hs-cTnT as a continuous variable is significantly related to MACE and mortality, when used as a categorical variable, the clear correlation between hs-cTnT and mortality was only present in the highest group (> 14 pg/mL).In the Cox proportional hazard model analysis, the correlation between hs-cTnT and MACE and mortality did not decrease significantly after adjusting for traditional risk factors, renal function, and hs-CRP. However, when the NT-proBNP level was further adjusted, the risk of MACE and mortality was significantly reduced, suggesting that NT-proBNP and hs-cTnT partially overlap information about abnormalities in cardiac structure and function. Few studies have reported the optimal hs-cTnT cut-off for predicting MACE and mortality in the general population. Use of an equivalent cut-off for hs-cTnT merits further discussion, and universally using a cut-off of 0.014 µg/L may result in the improper diagnosis of acute CVD [ 28 ]. An individual patient data meta-analysis reported that the optimal cut-off value for all-cause death was 18 ng/L in a chronic heart failure population, whereas the optimal cut-off values increased progressively with worse renal function[ 29 ].In our study, the ROC curve analysis predicted the area under the ROC curve for MACE and mortality was 0.559 and 0.644, respectively. When the hs-cTnT value was 5.01 pg/mL, the sensitivity for predicting MACE was 53.8% and the specificity was 59.1%. When the hs-cTnT value was 4.6 pg/mL, the sensitivity for predicting MACE was 74.1% and the specificity was 51.4%. Obviously, specialized research should determine whether patient management guided by the same cut-off value is beneficial to prognosis and cost-effective. The mechanisms of small elevations of hs-cTnT in apparently healthy subjects are not fully understood. It is well known that serum elevation of cTnT is associated with myocardial ischemia, and a positive correlation between elevated hs-cTnT levels and new or suspected ECG ST-T changes or new left bundle branch block was recently described [ 30 ]. However, the study found that concentrations of hs-cTnT remained unchanged after exercise in patients with and without detectable ischemia[ 31 ], supporting that factors (e.g., coronary microvascular dysfunction[ 32 ], apoptosis[ 33 , 34 ], or subclinical abnormalities of cardiac structure or function [ 35 ] could induce troponin release) in addition to ischemia contribute significantly to the risk associated with cTnT. Therefore, there may be several mechanisms underlying elevated troponin levels, and it is unclear which mechanisms are related to the outcomes observed in our study. A new study shows that incorporating hs-cTnI testing into risk algorithms for patients with ASCVD provides enhanced risk stratification and leads to the reclassification of about 12% of patients into a more appropriate risk group[ 36 ]. Troponin levels should not be viewed just as a marker for myocardial injury and diagnosis of MI in acute coronary syndrome but should be used more frequently for assessing CVD risk in stable patients with ischemic heart disease. It is very meaningful to screen hs-cTnT in the general population. Elevated hs-cTnT levels in community populations may be an early warning device to reflect the risk of chronic cardiovascular disease or future cardiovascular disease. However, the increase of sensitivity may also lead to the decrease of diagnostic specificity, especially in diabetes, chronic kidney disease or elderly men, where an increase of the hs-cTnT level is very common. Therefore, it is necessary to establish appropriate hs-cTnT diagnostic thresholds according to different populations. The deficiency of this study is the lack of objective examination to evaluate the structure and function of the heart, such as echocardiography and imaging examinations of coronary arteries. Conclusions Our findings in the Chinese cohort support that hs-cTnT is a risk factor for major adverse cardiovascular events and all-cause mortality. Abbreviations hs-cTnT high-sensitivity cardiac troponin T; SBP:Systolic blood pressures; DBP:diastolic blood pressures; FBG:fasting blood glucose; TC:total cholesterol; TGs:triglycerides; LDL-C:low density lipoprotein cholesterol; HDL-C:high density lipoprotein cholesterol; Hcy:homocysteine; hs-CRP:high-sensitivity C-reactive protein; NT-proBNP:N-terminal B-type brain natriuretic peptide; MACE:major adverse cardiovascular events Declarations Ethics approval and consent to participate: The study was approved by the ethics committee of the People’s Liberation Army General Hospital, and each subject provided informed written consent. Consent for publication: Not applicable Availability of data and materials: Not applicable Competing interests: The authors declare that they have no competing interests Funding: This research is supported by the grant from the Key National Basic Research Program of China (2012CB517503, 2013CB530804) and the Key Science and Technology Foundation of China (2012ZX09303004-002) to Dr. Ping Ye. Authors' contributions: XW and PY designed the study; RC, XY, WX and YZ participated in acquisition of data; XW, PY and YB researched and evaluated the literature; XW undertook the statistical analysis and wrote the first draft of the manuscript. All authors read and approved the final manuscript Acknowledgements: Acknowledgments and disclosures: We thank colleagues at the Department of Laboratory Medicine, the PLA General Hospital for help with biochemical measurements. We are also grateful to all study participants for their participation in the study References Khot UN, Khot MB, Bajzer CT, et al. Prevalence of conventional risk factors in patients with coronary heart disease. JAMA. 2003;290(7):898–904. Mueller M, Vafaie M, Biener M, Giannitsis E, Katus HA. Cardiac troponin T: from diagnosis of myocardial infarction to cardiovascular risk prediction. Circ J. 2013; 77 (7):1653–1661. Omland T, de Lemos JA, Sabatine MS, et al. A sensitive cardiac troponin T assay in stable coronary artery disease. N Engl J Med. 2009;361(26):2538–2547. Aimo A, Januzzi JL Jr, Vergaro G, et al. Prognostic Value of High-Sensitivity Troponin T in Chronic Heart Failure: An Individual Patient Data Meta-Analysis. Circulation. 2018;137(3):286–297. deFilippi CR, de Lemos JA, Christenson RH, et al. Association of serial measures of cardiac troponin T using a sensitive assay with incident heart failure and cardiovascular mortality in older adults. JAMA. 2010;304(22):2494–2502. de Lemos JA, Drazner MH, Omland T, et al. Association of troponin T detected with a highly sensitive assay and cardiac structure and mortality risk in the general population [published correction appears in JAMA. 2011 Mar 23; 305 (12): 12 00]. JAMA. 2010;304(22):2503–2512. deFilippi CR, de Lemos JA, Christenson RH, et al. Association of serial measures of cardiac troponin T using a sensitive assay with incident heart failure and cardiovascular mortality in older adults. JAMA. 2010;304(22):2494–2502. Saunders JT, Nambi V, de Lemos JA, et al. Cardiac troponin T measured by a highly sensitive assay predicts coronary heart disease, heart failure, and mortality in the Atherosclerosis Risk in Communities Study. Circulation. 2011; 123 (13): 1367 –1376. Omland T, de Lemos JA, Sabatine MS, et al. A sensitive cardiac troponin T assay in stable coronary artery disease. N Engl J Med. 2009;361(26):2538–2547. Sacks DB, Arnold M, Bakris GL, et al. Position statement executive summary: guidelines and recommendations for laboratory analysis in the diagnosis and management of diabetes mellitus. Diabetes Care. 2011;34(6):1419–1423. Giannitsis E, Kurz K, Hallermayer K, Jarausch J, Jaffe AS, Katus HA. Analytical validation of a high-sensitivity cardiac troponin T assay. Clin Chem. 2010; 56 (2): 254 –261. Ives DG, Fitzpatrick AL, Bild DE, et al. Surveillance and ascertainment of cardiovascular events. The Cardiovascular Health Study. Ann Epidemiol. 1995; 5 (4): 278–285. Wang TJ, Gona P, Larson MG, et al. Multiple biomarkers for the prediction of first major cardiovascular events and death. N Engl J Med. 2006;355(25):2631–2639. Angeja BG, Shlipak MG, Go AS, et al. Hormone therapy and the risk of stroke after acute myocardial infarction in postmenopausal women. J Am Coll Cardiol. 2001; 38(5):1297–1301. Vasile VC, Saenger AK, Kroning JM, Jaffe AS. Biological and analytical variability of a novel high-sensitivity cardiac troponin T assay. Clin Chem. 2010; 56 (7):1086–1090. Apple FS, Collinson PO; IFCC Task Force on Clinical Applications of Cardiac Biomarkers. Analytical characteristics of high-sensitivity cardiac troponin assays [published correction appears in Clin Chem. 2012 Apr;58(4):796]. Clin Chem. 2012; 58(1):54–61. Korley F K, Jaffe A S. Preparing the United States for High-Sensitivity Cardiac Troponin Assays[J]. Journal of the American College of Cardiology, 2013, 61 (17): 17 53-1758. Wu AHB, Christenson RH, Greene DN, et al. Clinical Laboratory Practice Recommendations for the Use of Cardiac Troponin in Acute Coronary Syndrome: Expert Opinion from the Academy of the American Association for Clinical Chemistry and the Task Force on Clinical Applications of Cardiac Bio-Markers of the International Federation of Clinical Chemistry and Laboratory Medicine. Clin Chem. 2018;64(4):645–655. Pretorius CJ, Tate JR, Wilgen U, Cullen L, Ungerer JPJ. A critical evaluation of the Beckman Coulter Access hsTnI: Analytical performance, reference interval and concordance. Clin Biochem. 2018;55:49–55. Gunsolus IL, Jaffe AS, Sexter A, et al. Sex-specific 99th percentiles derived from the AACC Universal Sample Bank for the Roche Gen 5 cTnT assay: Comorbidities and statistical methods influence derivation of reference limits. Clin Biochem. 2017; 50(18):1073–1077. Chenevier-Gobeaux C, Bonnefoy-Cudraz É, Charpentier S, et al. High-sensitivity cardiac troponin assays: answers to frequently asked questions [published correction appears in Arch Cardiovasc Dis. 2015 May;108(5):331-2]. Arch Cardiovasc Dis. 2015; 108(2):132–149. Ackland GL, Abbott TEF, Jones TF, et al. Early elevation in plasma high-sensitivity troponin T and morbidity after elective noncardiac surgery: prospective multicentre observational cohort study. Br J Anaesth. 2020;124(5):535-543. Agewall S, Giannitsis E, Jernberg T, et al. Troponin elevation in coronary vs. non-coronary disease[J]. European Heart Journal (4):4. Saunders J T, Nambi V, De Lemos J A, et al. Cardiac Troponin T Measured by a Highly Sensitive Assay Predicts Coronary Heart Disease, Heart Failure, and Mortality in the Atherosclerosis Risk in Communities Study[J]. Circulation, 2011, 123 (13): 1367-1376. De Lemos J A, Drazner M H, Omland T, et al. Association of Troponin T Detected With a Highly Sensitive Assay and Cardiac Structure and Mortality Risk in the General Population[J]. Jama, 2010, 304(22):2503. McEvoy JW, Chen Y, Ndumele CE, et al. Six-year change in high-sensitivity cardiac troponin T and risk of subsequent coronary heart disease, heart failure, and death. JAMA Cardiol. 2016; 1:519–28. Welsh P, Preiss D, Hayward C, et al. Cardiac Troponin T and Troponin I in the General Population. Circulation. 2019;139(24):2754–2764. Gore MO, Seliger SL, Defilippi CR, et al. Age-and sex-dependent upper reference limits for the high-sensitivity cardiac troponin T assay. J Am Coll Cardiol. 2014; 63: 1441–8. Aimo A, Januzzi JL Jr, Vergaro G, et al. Prognostic Value of High-Sensitivity Troponin T in Chronic Heart Failure: An Individual Patient Data Meta-Analysis. Circulation. 2018;137(3):286–297. Král, Michal, ?aňák, Daniel, Veverka, Tomá, et al. Troponin T in Acute Ischemic Stroke[J]. American Journal of Cardiology, 2013, 112(1):117-121. Kurz K, Giannitsis E, Zehelein J, Katus HA. Highly sensitive cardiac troponin T values remain constant after brief exercise- or pharmacologic induced reversible myocardial ischemia. Clin Chem. 2008;54: 1234–1238. Camici PG, Crea F. Coronary microvascular dysfunction. N Engl J Med. 2007; 356:830–840. Bergmann O, Bhardwaj R D, Bernard S , et al. Evidence for Cardiomyocyte Renewal in Humans[J]. ence, 2009, 324(5923):98-102. Anversa P, Leri A. Innate Regeneration in the Aging Heart: Healing?From Within[J]. Mayo Clinic Proceedings, 2013, 88(8):871-883. Sato Y, Fujiwara H, Takatsu Y. Cardiac troponin and heart failure in the era of high-sensitivity assays[J]. Journal of Cardiology, 2012, 60(3):160-167. Marston NA, Bonaca MP, Jarolim P, et al. Clinical Application of High-Sensitivity Troponin Testing in the Atherosclerotic Cardiovascular Disease Framework of the Current Cholesterol Guidelines [published online ahead of print, 2020 Aug 5]. JAMA Cardiol. Cite Share Download PDF Status: Posted Version 1 posted 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-68461","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research","associatedPublications":[],"authors":[{"id":2570914,"identity":"0e9f694a-d3e6-469f-8ffc-d386d6653d03","order_by":0,"name":"Xiaona Wang","email":"","orcid":"","institution":"Chinese PLA General Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xiaona","middleName":"","lastName":"Wang","suffix":""},{"id":2570915,"identity":"7bb5b326-0fae-4182-bc79-78776a61b03a","order_by":1,"name":"Ruihua Cao","email":"","orcid":"","institution":"Chinese PLA General Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ruihua","middleName":"","lastName":"Cao","suffix":""},{"id":2570916,"identity":"2073e0a3-035e-495c-adb2-56ebcddb10ca","order_by":2,"name":"Xu Yang","email":"","orcid":"","institution":"Chinese PLA General Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xu","middleName":"","lastName":"Yang","suffix":""},{"id":2570917,"identity":"dc5223c3-6d06-4e22-aae0-2ae3a5c383ae","order_by":3,"name":"Wenkai Xiao","email":"","orcid":"","institution":"Chinese PLA General Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Wenkai","middleName":"","lastName":"Xiao","suffix":""},{"id":2570918,"identity":"3ef6fec7-6cff-4893-a3fc-1793ea2fa79c","order_by":4,"name":"Yun Zhang","email":"","orcid":"","institution":"Chinese PLA General Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yun","middleName":"","lastName":"Zhang","suffix":""},{"id":2570919,"identity":"5028938b-1d63-497a-ac0c-1c852399bf4b","order_by":5,"name":"Ping Ye","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1klEQVRIie2PMQrCQBBFRwJrMzGdJCiJR1hJ62FiL5IDpEgVG01t8DKRgaSJ1oKFioWNnSAiIu42lm7sBPdVM/AffwZAo/lRcoAIWTOWQ32lcFuYf6EIDN+1g5pZXs6WFN7ZMHFOBwoj8Ky2ooxX64CytDtMOiNO8wL62UJRxzciaU6ZVICQQcC3KmV3loohDiv3hM86ygY54c3wmQ3CTWooTjUOyIwLl6G8MLXVv7TKFV3wEaE3KY8XvA48q6NQejkCNJL3bn+OS7xYKPBQBzUajeaPeQHGUkboYA3mYwAAAABJRU5ErkJggg==","orcid":"","institution":"Chinese PLA General Hospital","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Ping","middleName":"","lastName":"Ye","suffix":""}],"badges":[],"createdAt":"2020-08-29 12:01:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-68461/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-68461/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":2556545,"identity":"0479ba43-0956-419b-a0e0-bfe61b10905c","added_by":"auto","created_at":"2020-09-23 14:24:48","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":47455,"visible":true,"origin":"","legend":"Risk for cardiovascular events and all-cause mortality by baseline hs-cTnT level. Kaplan-Meier survival curves indicating cumulative incidence of major adverse cardiovascular events (A), coronary event (B), and all-cause mortality (C) across baseline hs-cTnT categories.","description":"","filename":"Fig1.JPG","url":"https://assets-eu.researchsquare.com/files/rs-68461/v1/Fig1.JPG"},{"id":2556546,"identity":"bc9600bb-df8c-42b1-a6a3-4abf8ebc34c5","added_by":"auto","created_at":"2020-09-23 14:24:48","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":41054,"visible":true,"origin":"","legend":"Hs-TnT predicts the ROC curve of major adverse cardiovascular events (A), coronary event (B), and all-cause mortality (C). The area under curves (AUC) is：(A) 0.559 (95% confidence interval: 0.523-0.595 P =0.001)；(B) 0.629 (95% confidence interval: 0.580-0.678, P \u003c0.001)；(C) 0.644 (95% confidence interval: 0.564-0.725, P \u003c0.001) respectively.","description":"","filename":"Fig2.JPG","url":"https://assets-eu.researchsquare.com/files/rs-68461/v1/Fig2.JPG"},{"id":13595916,"identity":"65991013-6629-4945-b00a-1d0fa09aeeda","added_by":"auto","created_at":"2021-09-17 05:26:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":483310,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-68461/v1/3037acf6-30ad-41d7-bd51-6b70c5ed84c9.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003eHigh-Sensitivity Cardiac Troponin T is A Risk Factor for Major Adverse Cardiovascular Events\u0026nbsp;and All-Cause Mortality:\u0026nbsp;9.5 Year Follow-up Study\u003c/p\u003e","fulltext":[{"header":"Background","content":" \u003cp\u003eIt is extremely challenging to predict cardiovascular events in the general population; while the general population is unlikely to become a target of preventive measures, such measures have become a mainstay of how society addresses cardiovascular disease [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Traditional risk factors such as hypertension, diabetes, hyperlipidemia, smoking, and obesity play important roles in the occurrence and development of cardiovascular disease. They can also be used as important prognostic factors for risk stratification and the prognostic evaluation of patients, but these traditional risk factors cannot be used to explain all prognostic risks. In recent years, new biomarkers, such as high-sensitivity cardiac troponin T (hs-cTnT) and nitrogen-terminated B-type brain key precursors (NT-proBNP), have been widely used in clinical practice and have been proven to be risk factors for cardiovascular disease independent of traditional risk factors.\u003c/p\u003e \u003cp\u003ecTnT can be effectively used to discover subclinical heart disease and assess the risk of future cardiovascular disease, but the low detection rate of standard measurement methods limits its clinical application [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. It is possible to measure cTnT with high sensitivity with the advancement of science and technology, and high-sensitivity cardiac troponin T (hs-cTnT) has emerged. Studies have found that the concentration of hs-cTnT in patients with stable coronary heart disease is significantly related to cardiovascular death or congestive heart failure [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Subsequently, the prognostic value of hs-cTnT in the general population was also confirmed. A study involving 4,221 elderly community residents with a median follow-up time of 11.8\u0026nbsp;years found that baseline hs-cTnT and the change of hs-cTnT was significantly related to the incidence of heart failure and cardiovascular death [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Since then, the relationship between hs-cTnT and different cardiovascular events has been observed in several large community studies, and the results have been controversial [\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. However, there is currently no cross-sectional or longitudinal follow-up study on hs-cTnT in the Chinese population.\u003c/p\u003e \u003cp\u003eTherefore, the current study examined the relationship of hs-cTnT with mortality and cardiovascular events by investigating a community population without definite cardiovascular disease to ascertain the following: (1) the predictive relationship between the hs-cTnT level and mortality and cardiovascular events and (2) the predictive relationship between the change in hs-cTnT level and mortality and cardiovascular events in a large community-based longitudinal sample from China.\u003c/p\u003e "},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eSubjects\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter a routine health check-up between September 2007 and January 2009, a total of 1,680 subjects were initially eligible for cross-sectional analysis. During the follow-up from February 2013 to September 2013, 181 people were lost to follow-up due to various reasons. Finally, 1,499 subjects with complete data were included in the 5-year follow-up analysis (follow-up rate 89.2%). During the follow-up from June 2017 to September 2018, 174 people were lost to follow-up, and eventually 1,325 subjects completed the follow-up (follow-up rate 89.2%). The research protocol of this project was approved by the ethics committee of the General Hospital of the PLA, and each subject provided informed written consent. The median follow-up interval for the original subjects was 9.5 years. During these visits, all participants received a questionnaire survey. Physical examinations, biochemical indicators, and biomarkers (including hs-cTnT) were reviewed at the same time. Any event reported was subject to verification through medical records, death certificates, pathological autopsy results and objective coronary angiographic examination results and were jointly judged by two clinically experienced doctors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eClinical data collection\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHeight (cm) and weight (kg) were measured. Systolic and diastolic blood pressures (SBP and DBP) were measured in the right arm twice in a sitting position after 5 min of rest. Blood samples were collected from participants after an overnight fast. Concentrations of fasting blood glucose (FBG), total cholesterol (TC), triglycerides (TGs), low density lipoprotein cholesterol (LDL-C), high density lipoprotein cholesterol (HDL-C), homocysteine (Hcy), and uric acid The Roche Diagnostics GmbH (Roche Diagnostics GmbH, Mannheim, Germany) was used to detect on the Roche Diagnostics, Indianapolis, Indiana; the serum creatinine (Scr) was tested on the Hitachi 7600 by the Roche Diagnostics GmbH (Roche Diagnostics GmbH). (Hitachi, Tokyo, Japan); high-sensitivity C-reactive protein (hs-CRP) was detected on a Diension RxL Max analyzer (Siemens Healthcare Diagnostic) using an immunoassay method kit (Siemens Healthcare Diagnostic, USA, IN); N-terminal B-type brain natriuretic peptide (NT-proBNP) was detected on an autoanalyzer using the electroluminescence method using a Roche Diagnostics GmbH (Roche Diagnostics GmbH); hs-cTnT) was measured using the Elecsys Troponin T high-sensitivity kit (Roche Diagnostics GmbH, Mannheim, Germany) by a laboratory professional using an electroluminescence immunoassay method on a Modular Analytics E170 (Roche Diagnostics) instrument. The concentration unit of hs-cTnT is pg/mL, and the coefficient of variation between batches is 8% at 10 pg/mL and 2.5% at 100 pg/mL [9].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eDefinition of variables\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBody mass index (BMI): weight (kg)/height2 (m2).\u003c/p\u003e\n\u003cp\u003eHypertension: SBP \u0026ge; 140 mmHg and/or DBP 90 mmHg or those who have taken antihypertensive drugs.\u003c/p\u003e\n\u003cp\u003eDiabetes mellitus: fasting venous blood glucose \u0026ge; 7.0 mmol/L, OGTT test with 2 h blood glucose \u0026ge; 11.1 mmol/L, symptoms of hyperglycemia and random blood glucose \u0026ge; 11.1 mmol/L, or receiving hypoglycemic treatment [10].\u003c/p\u003e\n\u003cp\u003eEstimated glomerular filtration rate (eGFR): 141 \u0026times; min (Scr/k,1)\u0026alpha; \u0026times; max (Scr/k,1)-1.209 \u0026times; 0.993Age \u0026times; 1.018 (if female) \u0026times; 1.159 (if Black), where Scr is plasma creatinine (mg/dL), k is 0.7 for females and 0.9 for males, \u0026alpha; is -0.329 for females and -0.411 for males, min indicates the minimum of Scr/k or 1, and max indicates the maximum of Scr/k or 1.\u003c/p\u003e\n\u003cp\u003ehs-cTnT: The lowest detectable concentration according to the kit instructions is 3 pg/mL (according to the reagent instructions), which is used as the cut-off value in this study. Therefore, hs-cTNT levels \u0026ge; 3 pg/mL are considered measurable. The 99th percentile for the hs-cTnT of a group of healthy people aged 20 to 70 years was 14 pg/mL [11], and hs-cTnT concentrations \u0026ge; 14 pg/mL were generally considered to be elevated.\u003c/p\u003e\n\u003cp\u003eAll-cause mortality was determined by review of death certificates. The definition of MACE comprised nonfatal myocardial infarction, newly diagnosed CHD (identified by coronary artery imaging or receiving coronary revascularization), stroke (ischemic or hemorrhagic) and cardiovascular mortality.\u003c/p\u003e\n\u003cp\u003eCardiovascular death was defined as deaths related to atherosclerotic heart disease (fatal myocardial infarction and definite fatal coronary heart disease), cerebrovascular disease deaths (fatal stroke), and causes including heart failure death from other atherosclerotic and cardiovascular diseases [12].\u003c/p\u003e\n\u003cp\u003eCardiovascular events were defined as cardiovascular death, nonfatal myocardial infarction, coronary revascularization, coronary heart disease and stroke confirmed by coronary imaging [13].\u003c/p\u003e\n\u003cp\u003eCoronary heart disease events were defined as coronary heart disease death, nonfatal myocardial infarction, coronary revascularization, and coronary heart disease diagnosed by coronary imaging.\u003c/p\u003e\n\u003cp\u003eStroke was defined as acute, focal damage to the central nervous system caused by vascular causes, which results in neurological deficits, including cerebral infarction (rather than internal hemorrhage) and subarachnoid hemorrhage [14].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eStatistical analyses\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eContinuous variables are expressed as the mean or median (interquartile range) \u0026plusmn; standard deviation (SD) and dichotomous variables are expressed as percentages. Analysis of continuous variables was performed by t test, and analysis of categorical variables was performed by \u0026chi;2 test.\u003c/p\u003e\n\u003cp\u003ePearson regression analysis and stepwise multivariate linear regression analysis were performed to evaluate the associations between baseline hs-cTnT and baseline traditional cardiovascular risk factors.\u003c/p\u003e\n\u003cp\u003eFor the analyses of hs-cTnT as a categorical variable, subjects were divided into three groups according to their baseline hs-cTnT level: hs-cTnT \u0026lt; 3 pg/mL, hs-cTnT between 3 and 14 pg/mL, and hs-cTnT \u0026ge; 14 pg/mL. The relationship between baseline hs-cTnT levels and MACE and coronary events and mortality were analyzed by Cox proportional hazards models. Models were defined as follows: model 1 = adjusted for age and gender; model 2 = adjusted for model 1 + presence of hypertension or diabetes mellitus, current smoking status, SBP, postprandial blood glucose, TC, HDL-C, antihypertensive medication use and antidiabetic medication use; model 3 = adjusted for model 2 + eGFR; model 4 = adjusted for model 3 + hs-CRP and NT-proBNP (both after logarithmic transformation).\u003c/p\u003e\n\u003cp\u003eWe also analyzed the relationship between hs-cTnT as a continuous variable and endpoints, in which values of cTnT that were below the detection limit were assigned to 1.5 ng/L (i.e., one-half of the lower limit of detection).\u003c/p\u003e\n\u003cp\u003eSPSS17.0 software was used for all statistical analyses. P value \u0026lt; 0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eGeneral characteristics of the study population\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e \u003cp\u003eThe general characteristics of the population are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. In this population, the average age was 59.10\u0026thinsp;\u0026plusmn;\u0026thinsp;9.8 years, and 51.5% were females. Of these 1,325 people, 736 had a detectable level of hs-cTnT (\u0026gt;\u0026thinsp;3.0\u0026nbsp;pg/ml ), accounting for 55.54% of the total, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The distribution range of hs-cTnT was from 3.03\u0026nbsp;pg/ml to 176.4\u0026nbsp;pg/ml, and the median was 7.36\u0026nbsp;pg/ml (25% and 75% digits were 4.76\u0026nbsp;pg/ml and 15.58\u0026nbsp;pg/ml, respectively). There were 143 people with an increased hs-cTnT (\u0026ge;\u0026thinsp;14.0\u0026nbsp;pg/ml), accounting for 10.79% of the total. Comparing the three groups, the cardiovascular risk factors in the middle and increased groups were significantly higher than those in the lower group. In addition, cardiovascular risk factors, such as being male, history of diabetes, history of hypertension, FBG, uric acid, Hcy, etc., in the increased group were significantly different from those in the other two groups.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics and laboratory test results of subjects\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eHs-TnT group(ng/L)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eGroup 1 (n\u0026thinsp;=\u0026thinsp;589)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eGroup 2\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(n\u0026thinsp;=\u0026thinsp;593)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eGroup 3\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(n\u0026thinsp;=\u0026thinsp;143)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eP值\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (岁)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58.4\u0026thinsp;\u0026plusmn;\u0026thinsp;12.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60.7\u0026thinsp;\u0026plusmn;\u0026thinsp;11.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e62.7\u0026thinsp;\u0026plusmn;\u0026thinsp;11.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale (n(%))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e155(26.31%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e308(51.94%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e89(62.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.4\u0026thinsp;\u0026plusmn;\u0026thinsp;3.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.5\u0026thinsp;\u0026plusmn;\u0026thinsp;3.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.6\u0026thinsp;\u0026plusmn;\u0026thinsp;3.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.872\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking (n(%))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51(8.66%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e108(18.21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30(20.97%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBP (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e131.8\u0026thinsp;\u0026plusmn;\u0026thinsp;15.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e132.7\u0026thinsp;\u0026plusmn;\u0026thinsp;17.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e134.7\u0026thinsp;\u0026plusmn;\u0026thinsp;16.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.893\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBP (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e77.2\u0026thinsp;\u0026plusmn;\u0026thinsp;12.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77.1\u0026thinsp;\u0026plusmn;\u0026thinsp;11.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e77.6\u0026thinsp;\u0026plusmn;\u0026thinsp;12.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.275\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.89\u0026thinsp;\u0026plusmn;\u0026thinsp;0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.259\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.92\u0026thinsp;\u0026plusmn;\u0026thinsp;1.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.76\u0026thinsp;\u0026plusmn;\u0026thinsp;1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.07\u0026thinsp;\u0026plusmn;\u0026thinsp;1.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-C(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.34\u0026thinsp;\u0026plusmn;\u0026thinsp;0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.298\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-C(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.96\u0026thinsp;\u0026plusmn;\u0026thinsp;0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.92\u0026thinsp;\u0026plusmn;\u0026thinsp;0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.86\u0026thinsp;\u0026plusmn;\u0026thinsp;0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.512\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFBG (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.63\u0026thinsp;\u0026plusmn;\u0026thinsp;2.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.29\u0026thinsp;\u0026plusmn;\u0026thinsp;1.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.85\u0026thinsp;\u0026plusmn;\u0026thinsp;2.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003c.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScr(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e67.61\u0026thinsp;\u0026plusmn;\u0026thinsp;15.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66.27\u0026thinsp;\u0026plusmn;\u0026thinsp;16.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e73.49\u0026thinsp;\u0026plusmn;\u0026thinsp;20.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003c.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeGFR(ml/min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e/1.73\u0026nbsp;m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90.57\u0026thinsp;\u0026plusmn;\u0026thinsp;14.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e87.38\u0026thinsp;\u0026plusmn;\u0026thinsp;13.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e85.14\u0026thinsp;\u0026plusmn;\u0026thinsp;13.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003c.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsCRP(mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.4(1.3, 3.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.3(1.4, 3.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.4(1.6, 3.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.365\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBNP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35.51\u0026thinsp;\u0026plusmn;\u0026thinsp;15.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38.32\u0026thinsp;\u0026plusmn;\u0026thinsp;10.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48.55\u0026thinsp;\u0026plusmn;\u0026thinsp;18.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003c.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHs-TnT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.24\u0026thinsp;\u0026plusmn;\u0026thinsp;2.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26.06\u0026thinsp;\u0026plusmn;\u0026thinsp;17.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003c.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eTC, total cholesterol; HDL-C, high-density lipoprotein cholesterol; TG, triglyceride; LDL-C, low-density lipoprotein cholesterol; SBP, systolic blood pressure; DBP, diastolic blood pressure; BMI, body mass index; FBG, fast blood glucose; eGFR, estimated glomerular filtration rate;HR, heart rate༛Scr, Serum creatinine; Hs-CRP, High sensitive C-reactive protein; BNP, Brain Natriuretic Peptide; Hs-TnT, High sensitivity troponin T\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cp\u003e\u003cstrong\u003e\u003cem\u003eClinical factors affecting hs-cTnT\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eAt baseline, univariate analysis showed positive correlations between being elderly and male, smoking, hypertension, Scr, FBG, BNP, hs-CRP and hs-cTnT (after natural logarithmic conversion); eGFR was negatively correlated with hs-cTnT. Multivariate linear regression analysis showed that only being female, hypertension, FBG, TC and LDL-C were positively correlated with hs-cTnT, while eGFR was negatively correlated with hs-cTnT (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e\u003cstrong\u003e．\u003c/strong\u003e\u003cstrong\u003eClinical factors affecting Hs-TnT\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable style=\"width: 718px;\" border=\"1\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 314px;\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 514px;\" colspan=\"6\"\u003e\n\u003cp style=\"text-align: center;\"\u003e\u003cstrong\u003eHs-TnT\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Hs-TnT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003er\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; \u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u003c/strong\u003e\u003cstrong\u003e\u0026beta;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; \u003c/strong\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;CI\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 324px;\" colspan=\"3\"\u003e\n\u003cp\u003e\u003cstrong\u003eAll subjects(n=1325)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 104px;\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 87px;\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 60px;\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 136px;\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 117px;\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 144px;\"\u003e\n\u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 284px;\" colspan=\"3\"\u003e\n\u003cp\u003e0.085\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 87px;\"\u003e\n\u003cp\u003e0.004\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 60px;\"\u003e\n\u003cp\u003e\u0026nbsp;0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 136px;\"\u003e\n\u003cp\u003e\u0026nbsp;-0.002～0.007\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 117px;\"\u003e\n\u003cp\u003e\u0026nbsp;0.308\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 144px;\"\u003e\n\u003cp\u003e\u003cstrong\u003eMale\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 284px;\" colspan=\"3\"\u003e\n\u003cp\u003e0.120\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 87px;\"\u003e\n\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 60px;\"\u003e\n\u003cp\u003e0.725\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 136px;\"\u003e\n\u003cp\u003e0.340～1.110\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 117px;\"\u003e\n\u003cp\u003e\u0026nbsp;\u0026lt;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 144px;\"\u003e\n\u003cp\u003e\u003cstrong\u003eSmoking\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 284px;\" colspan=\"3\"\u003e\n\u003cp\u003e0.110\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 87px;\"\u003e\n\u003cp\u003e0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 60px;\"\u003e\n\u003cp\u003e0.320\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 136px;\"\u003e\n\u003cp\u003e-0.086～0.725\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 117px;\"\u003e\n\u003cp\u003e\u0026nbsp;0.122\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 144px;\"\u003e\n\u003cp\u003e\u003cstrong\u003eDiabetes\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 284px;\" colspan=\"3\"\u003e\n\u003cp\u003e0.144\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 87px;\"\u003e\n\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 60px;\"\u003e\n\u003cp\u003e0.232\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 136px;\"\u003e\n\u003cp\u003e0.058～0.522\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 117px;\"\u003e\n\u003cp\u003e\u0026nbsp;0.117\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 144px;\"\u003e\n\u003cp\u003e\u003cstrong\u003eHypertension\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 284px;\" colspan=\"3\"\u003e\n\u003cp\u003e0.278\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 87px;\"\u003e\n\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 60px;\"\u003e\n\u003cp\u003e0.883\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 136px;\"\u003e\n\u003cp\u003e0.501～1.265\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 117px;\"\u003e\n\u003cp\u003e\u0026nbsp;\u0026lt;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 144px;\"\u003e\n\u003cp\u003e\u003cstrong\u003eTG*\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 284px;\" colspan=\"3\"\u003e\n\u003cp\u003e0.013\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 87px;\"\u003e\n\u003cp\u003e0.646\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 60px;\"\u003e\n\u003cp\u003e0.044\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 136px;\"\u003e\n\u003cp\u003e-0.002～0.091\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 117px;\"\u003e\n\u003cp\u003e0.063\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 144px;\"\u003e\n\u003cp\u003e\u003cstrong\u003eHDL-C*\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 284px;\" colspan=\"3\"\u003e\n\u003cp\u003e0.029\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 87px;\"\u003e\n\u003cp\u003e0.321\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 60px;\"\u003e\n\u003cp\u003e0.100\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 136px;\"\u003e\n\u003cp\u003e\u0026nbsp;-0.070～0.270\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 117px;\"\u003e\n\u003cp\u003e\u0026nbsp;0.250\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 144px;\"\u003e\n\u003cp\u003e\u003cstrong\u003eLDL-C\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 284px;\" colspan=\"3\"\u003e\n\u003cp\u003e0.019\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 87px;\"\u003e\n\u003cp\u003e0.515\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 60px;\"\u003e\n\u003cp\u003e0.222\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 136px;\"\u003e\n\u003cp\u003e\u0026nbsp;0.077～0.367\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 117px;\"\u003e\n\u003cp\u003e0.003\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 144px;\"\u003e\n\u003cp\u003e\u003cstrong\u003eTC\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 284px;\" colspan=\"3\"\u003e\n\u003cp\u003e0.049\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 87px;\"\u003e\n\u003cp\u003e0.092\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 60px;\"\u003e\n\u003cp\u003e0.227\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 136px;\"\u003e\n\u003cp\u003e0.102～0.351\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 117px;\"\u003e\n\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 144px;\"\u003e\n\u003cp\u003e\u003cstrong\u003eSBP\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 284px;\" colspan=\"3\"\u003e\n\u003cp\u003e0.011\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 87px;\"\u003e\n\u003cp\u003e0.702\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 60px;\"\u003e\n\u003cp\u003e0.046\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 136px;\"\u003e\n\u003cp\u003e\u0026nbsp;0.033～0.060\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 117px;\"\u003e\n\u003cp\u003e\u0026nbsp;0.872\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 144px;\"\u003e\n\u003cp\u003e\u003cstrong\u003eDBP\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 284px;\" colspan=\"3\"\u003e\n\u003cp\u003e0.049\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 87px;\"\u003e\n\u003cp\u003e0.093\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 60px;\"\u003e\n\u003cp\u003e0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 136px;\"\u003e\n\u003cp\u003e\u0026nbsp;0.000～0.003\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 117px;\"\u003e\n\u003cp\u003e\u0026nbsp;0.010\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 144px;\"\u003e\n\u003cp\u003e\u003cstrong\u003eBMI\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 284px;\" colspan=\"3\"\u003e\n\u003cp\u003e0.033\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 87px;\"\u003e\n\u003cp\u003e0.258\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 60px;\"\u003e\n\u003cp\u003e\u0026nbsp;0.010\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 136px;\"\u003e\n\u003cp\u003e-0.002～0.007\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 117px;\"\u003e\n\u003cp\u003e0.003\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 144px;\"\u003e\n\u003cp\u003e\u003cstrong\u003eFBG\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 284px;\" colspan=\"3\"\u003e\n\u003cp\u003e0.065\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 87px;\"\u003e\n\u003cp\u003e0.026\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 60px;\"\u003e\n\u003cp\u003e0.064\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 136px;\"\u003e\n\u003cp\u003e\u0026nbsp;0.037～0.091\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 117px;\"\u003e\n\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 144px;\"\u003e\n\u003cp\u003e\u003cstrong\u003eCr\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 284px;\" colspan=\"3\"\u003e\n\u003cp\u003e0.101\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 87px;\"\u003e\n\u003cp\u003e0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 60px;\"\u003e\n\u003cp\u003e0.231\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 136px;\"\u003e\n\u003cp\u003e0.098～0.391\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 117px;\"\u003e\n\u003cp\u003e0.565\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 144px;\"\u003e\n\u003cp\u003e\u003cstrong\u003eeGFR*\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 284px;\" colspan=\"3\"\u003e\n\u003cp\u003e-0.091\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 87px;\"\u003e\n\u003cp\u003e0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 60px;\"\u003e\n\u003cp\u003e-0.596\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 136px;\"\u003e\n\u003cp\u003e-0.915～-0.277\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 117px;\"\u003e\n\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 144px;\"\u003e\n\u003cp\u003e\u003cstrong\u003eHs-CRP\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 284px;\" colspan=\"3\"\u003e\n\u003cp\u003e0.087\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 87px;\"\u003e\n\u003cp\u003e0.005\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 60px;\"\u003e\n\u003cp\u003e0.044\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 136px;\"\u003e\n\u003cp\u003e-0.009～0.097\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 117px;\"\u003e\n\u003cp\u003e0.105\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 144px;\"\u003e\n\u003cp\u003e\u003cstrong\u003eBNP\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 284px;\" colspan=\"3\"\u003e\n\u003cp\u003e0.119\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 87px;\"\u003e\n\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 60px;\"\u003e\n\u003cp\u003e0.016\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 136px;\"\u003e\n\u003cp\u003e-0.022～0.055\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 117px;\"\u003e\n\u003cp\u003e0.411\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 828px;\" colspan=\"8\"\u003e\n\u003cp\u003eTC, total cholesterol; HDL-C, high- density lipoprotein cholesterol; TG, triglyceride; LDL-C, low-density lipoprotein cholesterol; SBP, systolic blood pressure; DBP, diastolic blood pressure; BMI, body mass index; FBG, fast blood glucose; eGFR, estimated glomerular filtration rate；HR, heart rate; Scr, Serum creatinine; Hs-CRP, High sensitive C-reactive protein; BNP, Brain Natriuretic Peptide; Hs-TnT, High sensitivity troponin T\u003c/p\u003e\n\u003cp\u003e*: natural logarithm transformed\u003c/p\u003e\n\u003cp\u003e\u0026sect;: Covariates in the multiple-adjusted models included age, gender, hypertension, Diabetes, current smoking, levels of plasma\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;TC, TG, LDL-C, HDL-C, SBP, DBP, FBG, BMI, Cr, eGFR, Hs-CRP and BNP.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/br\u003e \u003cp\u003e\u003cstrong\u003e\u003cem\u003eAssociations of baseline hs-cTnT levels with major adverse cardiovascular events (MACE), coronary events and mortality\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eDuring the follow-up period, a total of 191 participants experienced MACE, and the incidence increased from 11.05% in the lowest (hs-cTnT\u0026thinsp;\u0026lt;\u0026thinsp;3\u0026nbsp;pg/ml) group to 23.02% in the highest group (hs-cTnT\u0026thinsp;\u0026gt;\u0026thinsp;14\u0026nbsp;pg/ml), as demonstrated by Kaplan-Meier survival analysis (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e-A). In addition, after adjusting for multiple factors in the Cox proportional hazards models analysis (Model 4), the risk of MACE increased with the increase of hs-cTnT levels (HR, 1.223, 95% CI, 1.054\u0026ndash;1.418, P\u0026thinsp;=\u0026thinsp;0.008) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCox proportional hazards models analysis for associations between baseline hs-cTnT\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eMace\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eCoronary event\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eAll cause Mortality\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eHR (95% CI)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eP值\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eHR (95% CI)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eP值\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eHR (95% CI)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eP值\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUnjust\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.313(1.144\u0026ndash;1.506)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.747(1.440\u0026ndash;2.210)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.996(1.470\u0026ndash;2.709)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModel1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.188(1.026\u0026ndash;1.375)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.395(1.122\u0026ndash;1.734)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.472(1.025\u0026ndash;2.115)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModel1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.183(1.016\u0026ndash;1.377)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.392(1.110\u0026ndash;1.746)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.657(1.145\u0026ndash;2.397)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModel1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.223(1.054\u0026ndash;1.418)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.391(1.106\u0026ndash;1.749)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.763(1.224\u0026ndash;2.540)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eModels are defined as follows: model 1\u0026thinsp;=\u0026thinsp;adjusted for age and gender; model 2\u0026thinsp;=\u0026thinsp;adjusted for model 1\u0026thinsp;+\u0026thinsp;presence of hypertension or diabetes mellitus, current smoking status, systolic blood pressure, postprandial blood glucose, total cholesterol, high-density lipoprotein cholesterol, antihypertensive medication use and antidiabetic medication use; model 3\u0026thinsp;=\u0026thinsp;adjusted for model 2\u0026thinsp;+\u0026thinsp;estimated glomerular filtration rate; model 4\u0026thinsp;=\u0026thinsp;adjusted for model 3\u0026thinsp;+\u0026thinsp;high-sensitivity C reactive protein and N-terminal pro-B-type natriuretic peptide (both after logarithmic transformation).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eMACE, major adverse cardiovascular event.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e \u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e \u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e \u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSimilar trends were discovered for coronary events. A total of 121 participants experienced coronary events, and the risk for coronary events and all-cause mortality by baseline hs-cTnT level is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e-B. Increased hs-cTnT levels were associated with coronary events (HR, 1.391, 95% CI, 1.106\u0026ndash;1.749, P\u0026thinsp;=\u0026thinsp;0.005) in Model 4 (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDuring the median follow-up of 9.5 years, a total of 84 deaths occurred, and the mortality rate increased significantly from 8.08% in the lowest quartile group (hs-cTnT\u0026thinsp;\u0026lt;\u0026thinsp;3\u0026nbsp;pg/mL) to 10.32% in the highest quartile (hs-cTnT\u0026thinsp;\u0026gt;\u0026thinsp;14\u0026nbsp;pg/mL) (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e-C). After adjusting for age, gender, blood pressure, blood lipids, renal function, and other traditional cardiovascular risk factors, Cox proportional risk regression model analysis revealed that increased hs-cTnT levels were associated with an increased risk of mortality (HR, 1.763, 95% CI, 1.224\u0026ndash;2.540, P\u0026thinsp;=\u0026thinsp;0.002), even after adjusting hs-CRP and NT-proBNP (Model 4) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cp\u003e\u003cstrong\u003e\u003cem\u003ehs-cTnT predicts MACE and mortality \u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eThe ROC curve was used to analyze the accuracy of hs-cTnT in predicting MACE (Fig.\u0026nbsp;\u0026lt;link rid=\"fig2\"\u0026gt;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u0026lt;/link\u0026gt;\u003c/span\u003e-A, \u0026lt;link rid=\"fig2\"\u0026gt;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u0026lt;/link\u0026gt;\u003c/span\u003e-B, and \u0026lt;link rid=\"fig2\"\u0026gt;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u0026lt;/link\u0026gt;\u003c/span\u003e-C). The area under the ROC curve for predicting MACE was 0.559 (95% CI, 0.523\u0026ndash;0.595, P\u0026thinsp;=\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e-A). The best cut-off value of hs-cTnT for predicting MACE was 5.01\u0026nbsp;pg/mL. At the same time, the sensitivity and specificity were 53.8% and 59.1%, respectively.\u003c/p\u003e\u003cp\u003eThe areas under the ROC curve for predicting coronary events and mortality were 0.629 (95% CI, 0.580\u0026ndash;0.678, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e-B) and 0.644 (95% CI, 0.564\u0026ndash;0.725, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e-C), respectively. The best cut-off value of hs-cTnT for predicting coronary events and mortality were 4.05\u0026nbsp;pg/mL and 4.6\u0026nbsp;pg/mL, respectively. When the hs-cTnT value was 4.05\u0026nbsp;pg/mL, the sensitivity for predicting coronary events was 72.5% and the specificity was 49.8%. When the hs-cTnT value was 4.6\u0026nbsp;pg/mL, the sensitivity for predicting mortality was 74.1%, and the specificity was 51.4%.\u003c/p\u003e \u003c/div\u003e "},{"header":"Discussion","content":" \u003cp\u003eTo our knowledge, this study is the first follow-up study to evaluate the predictive value of hs-cTnT for cardiovascular events and mortality conducted in China. Through the follow-up study of this community population for nearly 10 years, this study has some significance. First, we observed minor subclinical myocardial injury in the general population using the latest generation of hs-cTnT measurement methods, rather than only focusing on the elderly or high-risk population. We found that the hs-cTnT detection rate in this population was 54.70%. Second, the results confirmed that the baseline level of hs-cTnT can predict the mortality and cardiovascular event risk of the population of the community. Furthermore, we also found that increases in hs-cTnT also have predictive value for cardiovascular events and mortality.\u003c/p\u003e \u003cp\u003eIn recent years, highly sensitive methods for detecting hs-cTnT have increased in clinical practice. At present, there is not a very clear definition of hs-cTnT, which is mainly based on the analytical performance of the lowest detection limit and the measurement precision in the low cTnT concentration range. The definition of hs-cTnT is as follows [\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]: high-sensitivity methods can detect cTnT (e.g., as low as 10\u0026nbsp;ng/L) levels that cannot be detected by current traditional methods; the cTnT with the minimum detection value of CV\u0026thinsp;\u0026le;\u0026thinsp;10% and the 99th percentile value detected by the system or reagent that meets the requirements of the guidelines; or the cTnT can be detected in some or all of the surface healthy people and the 99th percentile value/CV\u0026thinsp;\u0026le;\u0026thinsp;10%. In 2018, the International Federation of Clinical Chemistry and Laboratory Medicine released the latest specific quality standards for troponin detection [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], which proposed that high-sensitivity troponin should meet the detection rate of more than 50% in apparently healthy males and females. New research has also shown differences in hs-cTnT levels among different ethnic groups [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The results of this study suggest that the prevalence of hs-cTnT levels\u0026thinsp;\u0026gt;\u0026thinsp;14.0\u0026nbsp;pg/mL was approximately 11.0%, which is slightly higher than the previous studies. In the current study, the detection rate of hs-cTnT in the general population in the Beijing community was 70.65% for males and 43.85% for females, respectively, which was similar to foreign studies. Our findings are slightly different from the previous study, probably because of the middle-aged and elderly in the study. Although there is no clear history of cardiovascular disease, traditional cardiovascular risk factors, such as hypertension, diabetes, and smoking, are still common in this group of middle-aged and elderly people. Undiagnosed resting myocardial ischemia and chronic changes in cardiac structure and function may still possibly be present.\u003c/p\u003e \u003cp\u003ehs-cTnT has been recognized as the first marker of myocardial injury in the diagnosis, risk stratification and prognosis of acute coronary syndrome (ACS), and its value has been recognized at home and abroad [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Gareth L et al concluded that early elevation in plasma hs-cTnT within 24\u0026nbsp;h of elective noncardiac surgery precedes the subsequent development of noncardiac organ dysfunction[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].Because of its high sensitivity and specificity, hs-cTnT has been agreed upon by the European Heart Association and Chinese experts as the best basis for early diagnosis of acute myocardial infarction (AMI) and risk stratification of heart diseases [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. In predicting future events, multiple trials have confirmed an association between elevated hs-cTnT levels and mortality or cardiovascular death in some populations at high risk for cardiovascular disease. In addition, few studies have reported the association of hs-cTnT levels with cardiovascular events in individuals from a general population, and the results have been controversial. The Atherosclerosis Risk in Communities (ARIC) Study found an association between detectable cTnT and a highly sensitive assay was associated with incident CHD, mortality, and HF in individuals without known CHD/stroke [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Evidence from a prospective study of the cTnT detected with a highly sensitive assay supports a stronger association with structural heart disease and subsequent risk for all-cause mortality [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Changes in cTnT levels measured with a highly sensitive assay were found to be significantly associated with incident HF and cardiovascular death [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Welsh did not identify a significant relationship between baseline cTnT levels and some CVD outcomes, whereas they reported that cTnT is more strongly associated with the risk of non-CVD death [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Although hs-cTnT as a continuous variable is significantly related to MACE and mortality, when used as a categorical variable, the clear correlation between hs-cTnT and mortality was only present in the highest group (\u0026gt;\u0026thinsp;14\u0026nbsp;pg/mL).In the Cox proportional hazard model analysis, the correlation between hs-cTnT and MACE and mortality did not decrease significantly after adjusting for traditional risk factors, renal function, and hs-CRP. However, when the NT-proBNP level was further adjusted, the risk of MACE and mortality was significantly reduced, suggesting that NT-proBNP and hs-cTnT partially overlap information about abnormalities in cardiac structure and function.\u003c/p\u003e \u003cp\u003eFew studies have reported the optimal hs-cTnT cut-off for predicting MACE and mortality in the general population. Use of an equivalent cut-off for hs-cTnT merits further discussion, and universally using a cut-off of 0.014\u0026nbsp;\u0026micro;g/L may result in the improper diagnosis of acute CVD [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. An individual patient data meta-analysis reported that the optimal cut-off value for all-cause death was 18\u0026nbsp;ng/L in a chronic heart failure population, whereas the optimal cut-off values increased progressively with worse renal function[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].In our study, the ROC curve analysis predicted the area under the ROC curve for MACE and mortality was 0.559 and 0.644, respectively. When the hs-cTnT value was 5.01\u0026nbsp;pg/mL, the sensitivity for predicting MACE was 53.8% and the specificity was 59.1%. When the hs-cTnT value was 4.6\u0026nbsp;pg/mL, the sensitivity for predicting MACE was 74.1% and the specificity was 51.4%. Obviously, specialized research should determine whether patient management guided by the same cut-off value is beneficial to prognosis and cost-effective.\u003c/p\u003e \u003cp\u003eThe mechanisms of small elevations of hs-cTnT in apparently healthy subjects are not fully understood. It is well known that serum elevation of cTnT is associated with myocardial ischemia, and a positive correlation between elevated hs-cTnT levels and new or suspected ECG ST-T changes or new left bundle branch block was recently described [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. However, the study found that concentrations of hs-cTnT remained unchanged after exercise in patients with and without detectable ischemia[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], supporting that factors (e.g., coronary microvascular dysfunction[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], apoptosis[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], or subclinical abnormalities of cardiac structure or function [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] could induce troponin release) in addition to ischemia contribute significantly to the risk associated with cTnT. Therefore, there may be several mechanisms underlying elevated troponin levels, and it is unclear which mechanisms are related to the outcomes observed in our study.\u003c/p\u003e \u003cp\u003eA new study shows that incorporating hs-cTnI testing into risk algorithms for patients with ASCVD provides enhanced risk stratification and leads to the reclassification of about 12% of patients into a more appropriate risk group[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Troponin levels should not be viewed just as a marker for myocardial injury and diagnosis of MI in acute coronary syndrome but should be used more frequently for assessing CVD risk in stable patients with ischemic heart disease. It is very meaningful to screen hs-cTnT in the general population. Elevated hs-cTnT levels in community populations may be an early warning device to reflect the risk of chronic cardiovascular disease or future cardiovascular disease. However, the increase of sensitivity may also lead to the decrease of diagnostic specificity, especially in diabetes, chronic kidney disease or elderly men, where an increase of the hs-cTnT level is very common. Therefore, it is necessary to establish appropriate hs-cTnT diagnostic thresholds according to different populations. The deficiency of this study is the lack of objective examination to evaluate the structure and function of the heart, such as echocardiography and imaging examinations of coronary arteries.\u003c/p\u003e "},{"header":"Conclusions","content":" \u003cp\u003eOur findings in the Chinese cohort support that hs-cTnT is a risk factor for major adverse cardiovascular events and all-cause mortality.\u003c/p\u003e "},{"header":"Abbreviations","content":" \u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ehs-cTnT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ehigh-sensitivity cardiac troponin T; SBP:Systolic blood pressures; DBP:diastolic blood pressures; FBG:fasting blood glucose; TC:total cholesterol; TGs:triglycerides; LDL-C:low density lipoprotein cholesterol; HDL-C:high density lipoprotein cholesterol; Hcy:homocysteine; hs-CRP:high-sensitivity C-reactive protein; NT-proBNP:N-terminal B-type brain natriuretic peptide; MACE:major adverse cardiovascular events\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEthics approval and consent to participate:\u003c/em\u003e\u003c/strong\u003e The study was approved by the ethics committee of the People\u0026rsquo;s Liberation Army General Hospital, and each subject provided informed written consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eConsent for publication:\u003c/em\u003e\u003c/strong\u003e Not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAvailability of data and materials:\u003c/em\u003e\u003c/strong\u003e Not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCompeting interests:\u003c/em\u003e\u003c/strong\u003e The authors declare that they have no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFunding: \u003c/em\u003e\u003c/strong\u003eThis research is supported by the grant from the Key National Basic Research Program of China (2012CB517503, 2013CB530804) and the Key Science and Technology Foundation of China (2012ZX09303004-002) to Dr. Ping Ye.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAuthors' contributions: \u003c/em\u003e\u003c/strong\u003eXW and PY designed the study; RC, XY, WX and YZ participated in acquisition of data; XW, PY and YB researched and evaluated the literature; XW undertook the statistical analysis and wrote the first draft of the manuscript. All authors read and approved the final manuscript\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAcknowledgements: \u003c/em\u003e\u003c/strong\u003eAcknowledgments and disclosures: We thank colleagues at the Department of Laboratory Medicine, the PLA General Hospital for help with biochemical measurements. We are also grateful to all study participants for their participation in the study\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eKhot UN, Khot MB, Bajzer CT, et al. Prevalence of conventional risk factors in patients with coronary heart disease. JAMA. 2003;290(7):898\u0026ndash;904.\u003c/li\u003e\n\u003cli\u003eMueller M, Vafaie M, Biener M, Giannitsis E, Katus HA. Cardiac troponin T: from diagnosis of myocardial infarction to cardiovascular risk prediction. Circ J. 2013; 77 (7):1653\u0026ndash;1661.\u003c/li\u003e\n\u003cli\u003eOmland T, de Lemos JA, Sabatine MS, et al. A sensitive cardiac troponin T assay in stable coronary artery disease. N Engl J Med. 2009;361(26):2538\u0026ndash;2547.\u003c/li\u003e\n\u003cli\u003eAimo A, Januzzi JL Jr, Vergaro G, et al. Prognostic Value of High-Sensitivity Troponin T in Chronic Heart Failure: An Individual Patient Data Meta-Analysis. Circulation. 2018;137(3):286\u0026ndash;297.\u003c/li\u003e\n\u003cli\u003edeFilippi CR, de Lemos JA, Christenson RH, et al. Association of serial measures of cardiac troponin T using a sensitive assay with incident heart failure and cardiovascular mortality in older adults. JAMA. 2010;304(22):2494\u0026ndash;2502.\u003c/li\u003e\n\u003cli\u003ede Lemos JA, Drazner MH, Omland T, et al. Association of troponin T detected with a highly sensitive assay and cardiac structure and mortality risk in the general population [published correction appears in JAMA. 2011 Mar 23; 305 (12): 12 00]. JAMA. 2010;304(22):2503\u0026ndash;2512.\u003c/li\u003e\n\u003cli\u003edeFilippi CR, de Lemos JA, Christenson RH, et al. Association of serial measures of cardiac troponin T using a sensitive assay with incident heart failure and cardiovascular mortality in older adults. JAMA. 2010;304(22):2494\u0026ndash;2502.\u003c/li\u003e\n\u003cli\u003eSaunders JT, Nambi V, de Lemos JA, et al. Cardiac troponin T measured by a highly sensitive assay predicts coronary heart disease, heart failure, and mortality in the Atherosclerosis Risk in Communities Study. Circulation. 2011; 123 (13): 1367 \u0026ndash;1376.\u003c/li\u003e\n\u003cli\u003eOmland T, de Lemos JA, Sabatine MS, et al. A sensitive cardiac troponin T assay in stable coronary artery disease. N Engl J Med. 2009;361(26):2538\u0026ndash;2547.\u003c/li\u003e\n\u003cli\u003eSacks DB, Arnold M, Bakris GL, et al. Position statement executive summary: guidelines and recommendations for laboratory analysis in the diagnosis and management of diabetes mellitus. Diabetes Care. 2011;34(6):1419\u0026ndash;1423.\u003c/li\u003e\n\u003cli\u003eGiannitsis E, Kurz K, Hallermayer K, Jarausch J, Jaffe AS, Katus HA. Analytical validation of a high-sensitivity cardiac troponin T assay. Clin Chem. 2010; 56 (2): 254 \u0026ndash;261.\u003c/li\u003e\n\u003cli\u003eIves DG, Fitzpatrick AL, Bild DE, et al. Surveillance and ascertainment of cardiovascular events. The Cardiovascular Health Study. Ann Epidemiol. 1995; 5 (4): 278\u0026ndash;285.\u003c/li\u003e\n\u003cli\u003eWang TJ, Gona P, Larson MG, et al. Multiple biomarkers for the prediction of first major cardiovascular events and death. N Engl J Med. 2006;355(25):2631\u0026ndash;2639.\u003c/li\u003e\n\u003cli\u003eAngeja BG, Shlipak MG, Go AS, et al. Hormone therapy and the risk of stroke after acute myocardial infarction in postmenopausal women. J Am Coll Cardiol. 2001; 38(5):1297\u0026ndash;1301.\u003c/li\u003e\n\u003cli\u003eVasile VC, Saenger AK, Kroning JM, Jaffe AS. Biological and analytical variability of a novel high-sensitivity cardiac troponin T assay. Clin Chem. 2010; 56 (7):1086\u0026ndash;1090.\u003c/li\u003e\n\u003cli\u003eApple FS, Collinson PO; IFCC Task Force on Clinical Applications of Cardiac Biomarkers. Analytical characteristics of high-sensitivity cardiac troponin assays [published correction appears in Clin Chem. 2012 Apr;58(4):796]. Clin Chem. 2012; 58(1):54\u0026ndash;61.\u003c/li\u003e\n\u003cli\u003eKorley F K, Jaffe A S. Preparing the United States for High-Sensitivity Cardiac Troponin Assays[J]. Journal of the American College of Cardiology, 2013, 61 (17): 17 53-1758.\u003c/li\u003e\n\u003cli\u003eWu AHB, Christenson RH, Greene DN, et al. Clinical Laboratory Practice Recommendations for the Use of Cardiac Troponin in Acute Coronary Syndrome: Expert Opinion from the Academy of the American Association for Clinical Chemistry and the Task Force on Clinical Applications of Cardiac Bio-Markers of the International Federation of Clinical Chemistry and Laboratory Medicine. Clin Chem. 2018;64(4):645\u0026ndash;655.\u003c/li\u003e\n\u003cli\u003ePretorius CJ, Tate JR, Wilgen U, Cullen L, Ungerer JPJ. A critical evaluation of the Beckman Coulter Access hsTnI: Analytical performance, reference interval and concordance. Clin Biochem. 2018;55:49\u0026ndash;55.\u003c/li\u003e\n\u003cli\u003eGunsolus IL, Jaffe AS, Sexter A, et al. Sex-specific 99th percentiles derived from the AACC Universal Sample Bank for the Roche Gen 5 cTnT assay: Comorbidities and statistical methods influence derivation of reference limits. Clin Biochem. 2017; 50(18):1073\u0026ndash;1077.\u003c/li\u003e\n\u003cli\u003eChenevier-Gobeaux C, Bonnefoy-Cudraz \u0026Eacute;, Charpentier S, et al. High-sensitivity cardiac troponin assays: answers to frequently asked questions [published correction appears in Arch Cardiovasc Dis. 2015 May;108(5):331-2]. Arch Cardiovasc Dis. 2015; 108(2):132\u0026ndash;149.\u003c/li\u003e\n\u003cli\u003eAckland GL, Abbott TEF, Jones TF, et al. Early elevation in plasma high-sensitivity troponin T and morbidity after elective noncardiac surgery: prospective multicentre observational cohort study. Br J Anaesth. 2020;124(5):535-543.\u003c/li\u003e\n\u003cli\u003eAgewall S, Giannitsis E, Jernberg T, et al. Troponin elevation in coronary vs. non-coronary disease[J]. European Heart Journal (4):4.\u003c/li\u003e\n\u003cli\u003eSaunders J T, Nambi V, De Lemos J A, et al. Cardiac Troponin T Measured by a Highly Sensitive Assay Predicts Coronary Heart Disease, Heart Failure, and Mortality in the Atherosclerosis Risk in Communities Study[J]. Circulation, 2011, 123 (13): 1367-1376.\u003c/li\u003e\n\u003cli\u003eDe Lemos J A, Drazner M H, Omland T, et al. Association of Troponin T Detected With a Highly Sensitive Assay and Cardiac Structure and Mortality Risk in the General Population[J]. Jama, 2010, 304(22):2503.\u003c/li\u003e\n\u003cli\u003eMcEvoy JW, Chen Y, Ndumele CE, et al. Six-year change in high-sensitivity cardiac troponin T and risk of subsequent coronary heart disease, heart failure, and death. JAMA Cardiol. 2016; 1:519\u0026ndash;28.\u003c/li\u003e\n\u003cli\u003eWelsh P, Preiss D, Hayward C, et al. Cardiac Troponin T and Troponin I in the General Population. Circulation. 2019;139(24):2754\u0026ndash;2764.\u003c/li\u003e\n\u003cli\u003eGore MO, Seliger SL, Defilippi CR, et al. Age-and sex-dependent upper reference limits for the high-sensitivity cardiac troponin T assay. J Am Coll Cardiol. 2014; 63: 1441\u0026ndash;8.\u003c/li\u003e\n\u003cli\u003eAimo A, Januzzi JL Jr, Vergaro G, et al. Prognostic Value of High-Sensitivity Troponin T in Chronic Heart Failure: An Individual Patient Data Meta-Analysis. Circulation. 2018;137(3):286\u0026ndash;297.\u003c/li\u003e\n\u003cli\u003eKr\u0026aacute;l, Michal, ?aň\u0026aacute;k, Daniel, Veverka, Tom\u0026aacute;, et al. Troponin T in Acute Ischemic Stroke[J]. American Journal of Cardiology, 2013, 112(1):117-121.\u003c/li\u003e\n\u003cli\u003eKurz K, Giannitsis E, Zehelein J, Katus HA. Highly sensitive cardiac troponin T values remain constant after brief exercise- or pharmacologic induced reversible myocardial ischemia. Clin Chem. 2008;54: 1234\u0026ndash;1238.\u003c/li\u003e\n\u003cli\u003eCamici PG, Crea F. Coronary microvascular dysfunction. N Engl J Med. 2007; 356:830\u0026ndash;840.\u003c/li\u003e\n\u003cli\u003eBergmann O, Bhardwaj R D, Bernard S , et al. Evidence for Cardiomyocyte Renewal in Humans[J]. ence, 2009, 324(5923):98-102.\u003c/li\u003e\n\u003cli\u003eAnversa P, Leri A. Innate Regeneration in the Aging Heart: Healing?From Within[J]. Mayo Clinic Proceedings, 2013, 88(8):871-883.\u003c/li\u003e\n\u003cli\u003eSato Y, Fujiwara H, Takatsu Y. Cardiac troponin and heart failure in the era of high-sensitivity assays[J]. Journal of Cardiology, 2012, 60(3):160-167.\u003c/li\u003e\n\u003cli\u003eMarston NA, Bonaca MP, Jarolim P, et al. Clinical Application of High-Sensitivity Troponin Testing in the Atherosclerotic Cardiovascular Disease Framework of the Current Cholesterol Guidelines [published online ahead of print, 2020 Aug 5]. JAMA Cardiol.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"hs-cTnT, major adverse cardiovascular events, all-cause mortality","lastPublishedDoi":"10.21203/rs.3.rs-68461/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-68461/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u0026nbsp;\u003c/strong\u003eThe relationship between high-sensitivity cardiac troponin T (hs-cTnT) and different cardiovascular events has been observed in several large community studies, and the results have been controversial. However, there is currently no cross-sectional or longitudinal follow-up study on hs-cTnT in the Chinese population.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eWe analyzed the association of plasma hs-cTnT levels with major adverse cardiovascular events and all-cause mortality in 1325 subjects from a longitudinal follow-up community-based population in Beijing, China.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults:\u0026nbsp;\u003c/strong\u003eIn the Cox proportional hazards models analysis, the risk of MACE increased with the increase of hs-cTnT levels (HR, 1.223, 95% CI, 1.054–1.418, P = 0.008). Increased hs-cTnT levels were associated with coronary events (HR, 1.391, 95% CI, 1.106–1.749, P = 0.005) in Model 4. Cox proportional risk regression model analysis revealed that increased hs-cTnT levels were associated with an increased risk of mortality (HR, 1.763, 95% CI, 1.224–2.540, P = 0.002), even after adjusting hs-CRP and NT-proBNP. The area under the ROC curve for predicting MACE was 0.559 (95% CI, 0.523–0.595, P = 0.001). The areas under the ROC curve for predicting coronary events and mortality were 0.629 (95% CI, 0.580–0.678, P \u0026lt; 0.001) and 0.644 (95% CI, 0.564–0.725, P \u0026lt; 0.001), respectively.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e Our findings in the Chinese cohort support that hs-cTnT is a risk factor for major adverse cardiovascular events and all-cause mortality.\u003c/p\u003e","manuscriptTitle":"High-Sensitivity Cardiac Troponin T is A Risk Factor for Major Adverse Cardiovascular Events\u0026nbsp;and All-Cause Mortality:\u0026nbsp;9.5 Year Follow-up Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2020-09-23 14:24:45","doi":"10.21203/rs.3.rs-68461/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9f6d9a67-7ac7-4be7-ad28-4330778d2ef1","owner":[],"postedDate":"September 23rd, 2020","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":586969,"name":"Translational Medicine"}],"tags":[],"updatedAt":"2020-10-29T19:37:42+00:00","versionOfRecord":[],"versionCreatedAt":"2020-09-23 14:24:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-68461","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-68461","identity":"rs-68461","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}