Prognostic value of elevated trimethylamine-N-oxide levels in patients with coronary artery disease in China

Prognostic value of elevated trimethylamine-N-oxide levels in patients with coronary artery disease in China | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Prognostic value of elevated trimethylamine-N-oxide levels in patients with coronary artery disease in China Hao Zhang, Yuan-yuan Peng, Xi-ya Lu, Yu-xing Li, Wen-zhi Li, Yi Hu, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9065037/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Background Coronary artery disease (CAD) is a prevalent cardiovascular condition worldwide. Trimethylamine N-oxide (TMAO), a metabolite produced by gut microbiota, plays a crucial role in the pathogenesis and progression of CAD. However, the long-term prognostic value of plasma TMAO for all-cause mortality in Chinese CAD patients remains to be fully explored. Methods In this observational cohort study, 389 hospitalized CAD patients, confirmed via coronary angiography at Xiangya Hospital in 2022, were enrolled. Plasma TMAO levels were measured using liquid chromatography-tandem mass spectrometry. All-cause mortality events were identified through telephone interviews, hospital outpatient visits, and official hospital records, conducted semi-annually. Kaplan-Meier analysis and Cox regression analysis were employed to investigate the relationship between TMAO levels and all-cause mortality. Results Among 364 CAD patients who completed the median follow-up period of 39 months (IQR: 37–42 months), 40 patients (11.0%) experienced all-cause mortality. Patients with elevated TMAO levels, based on the optimal cutoff value of 317.62 ng/mL, had a significantly higher mortality rate compared to those with lower levels ( P < 0.0001). After adjusting for conventional risk factors, including diabetes, elevated TMAO levels remained a significant predictor of all-cause mortality (hazard ratio [HR] 2.626; 95% CI: 1.361 to 5.065; P = 0.004). Conclusions Elevated plasma TMAO levels are significantly associated with increased all-cause mortality over a median follow-up of 39 months in CAD patients from China. Trimethylamine N-oxide All-cause mortality Coronary artery disease Introduction Coronary artery disease (CAD) remains a leading cause of morbidity worldwide, with a continuously rising prevalence in China[ 1 ]. Despite advances in medical management, the risk of death among CAD patients remains substantial. Recently, growing attention has focused on trimethylamine N-oxide (TMAO), a gut microbiota-dependent metabolite, which has been implicated in the pathogenesis and progression of CAD[ 2 – 4 ]. TMAO is produced when dietary nutrients such as choline, L-carnitine, and betaine, commonly found in red meat, eggs, sausage, and processed foods, are metabolized by intestinal flora into trimethylamine (TMA). This TMA is subsequently oxidized to TMAO by flavin-containing monooxygenase 3 (FMO3) in the liver[ 5 , 6 ]. Accumulating evidence has demonstrated that TMAO is closely associated with several pathophysiological processes that underlie CAD. These include disturbances in cholesterol and bile acid metabolism[ 7 – 9 ], foam cell formation[ 7 ], inflammation, endothelial dysfunction[ 10 , 11 ], platelet activation[ 12 – 14 ], atherosclerosis[ 7 , 15 ], fibrosis and vascular aging[ 7 , 16 ]. Several studies have further suggested that elevated circulating TMAO levels are associated with an increased risk of major adverse cardiovascular events (MACEs) in CAD patients[ 17 – 19 ]. For instance, Tang et al. reported that higher plasma TMAO levels predicted an increased risk of MACEs, including all-cause death, in 4007 patients with stable CAD during a three-year follow-up[ 17 ]. Similarly, Toru et al. demonstrated that plasma TMAO was a superior independent predictor of all-cause mortality or myocardial infarction compared to other biomarkers (adrenomedullin, oxidized LDL, and natriuretic peptides) in 1079 patients with acute myocardial infarction over a two-year period[ 18 ]. Recently, Yu et al. found that elevated TMAO levels were significantly associated with MACEs only in CAD patients with diabetes in northern China, but not in those without diabetes[ 19 ], which clearly contrasted with previous studies. Despite the potential of TMAO as a biomarker for CAD prognosis and as a target for therapeutic intervention, the current evidence remains limited and somewhat inconsistent. Particularly, the association between plasma TMAO levels and all-cause mortality among Chinese CAD patients, after adjusting for traditional risk factors (including diabetes), warrants further investigation. Therefore, this study aimed to investigate the relationship between circulating TMAO levels and all-cause mortality in a cohort of CAD patients from South China. Methods Study population and clinical outcomes This observational cohort study enrolled 389 patients hospitalized with CAD between January 1, 2022, and December 31, 2022, at the Department of Geriatrics, Xiangya Hospital, Central South University. The diagnosis of CAD was confirmed based on coronary angiography, and defined as ≥ 50% stenosis in the luminal diameter of one or more major epicardial coronary arteries. The exclusion criteria for patients were as follows: (1) aged < 18 years old; (2) a diagnosis of malignant tumor requiring advanced medical or surgical therapy; (3) with clinically significant infectious diseases; (4) patients with severe hepatic dysfunction [alanine amino transferase (ALT) level > 135 U/L]. All patients underwent comprehensive clinical evaluations, including physical examinations and biochemical assessments such as complete blood count, blood glucose, blood lipid profiles, hepatic and renal function tests, high-sensitivity troponin T (hs-TnT), and N-terminal pro-brain natriuretic peptide (NT-proBNP). Left ventricular ejection fraction (LVEF) was assessed using transthoracic echocardiography. Hypertension was defined as systolic blood pressure ≥ 140 mmHg or diastolic blood pressure ≥ 90 mmHg, consistent with standard guidelines[ 20 ]. Diabetes was diagnosed based on a fasting glucose level of ≥ 7.0 mmol/L or a 2-hour oral glucose tolerance test (OGTT) value of ≥ 11.1 mmol/L[ 21 ]. Smoking status included both current and former smokers. Clinical outcomes focused on all-cause mortality. These outcomes were tracked and verified semi-annually through a combination of telephone interviews, hospital outpatient visits, and official hospital records. The study was conducted in compliance with the Declaration of Helsinki and received approval from the Ethics Committee of Xiangya Hospital, Central South University (Approval number: 202211735). Written informed consent was obtained from 389 participants or their legal guardians, as appropriate (age range: 38–95 years, 271 males, and 118 females). Sample preparation and TMAO analysis Blood samples were collected from the peripheral veins of patients in the morning of the second day of hospitalization, following an overnight fast. The samples were drawn into ethylenediaminetetraacetic acid (EDTA) anticoagulant tubes, centrifuged at 3000 rpm for 10 minutes, and the plasma fraction was separated and stored at − 80°C until further analysis. Plasma TMAO levels were measured using high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS) with a d9-(trimethyl)-labeled internal standard, following previously established protocols[ 22 ]. Absolute quantification was performed by calculating the peak area ratio of TMAO to the internal standard TMAO-d9, with the concentration of TMAO standards used as the independent variable. The actual TMAO concentration was determined based on a standard calibration curve generated during each analytical run. All sample recovery rates were within the range of 85%–115%, and the coefficients of variation (CV) for precision and interbatch variability were both ≤ 15%, meeting the established acceptance criteria. Other laboratory parameters, including renal and liver function, lipid profiles, and cardiac biomarkers, were assessed in the clinical laboratory of Xiangya Hospital. Estimated glomerular filtration rate (eGFR) was calculated using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) formula. Management of missing data and outliers To minimize bias, variables with missing values less than 5% (triglyceride, total cholesterol, high-density lipoprotein cholesterol, eGFR, NT-proBNP, hs-TnT, and LVEF) were replaced with the mean of that variable. Variables with abnormal values were handled by the winsorization method, with the 1% and 99% as cut-off points. All missing and abnormal data were processed using the Statistical Package for Social Sciences (SPSS; version 26.0) software. Statistical analysis Continuous variables were presented as mean ± standard deviation (SD) or median (interquartile range, IQR), depending on their distribution. If the data followed a normal distribution and exhibited homogeneity of variance, Student’s t-test was used for inter-group comparisons; otherwise, the Mann–Whitney U-test was employed. Categorical variables were expressed as numbers (percentages) and compared between groups using the chi-square test or Fisher's exact test, as appropriate. The receiver operating characteristic (ROC) curve analysis was used to assess the predictive value of selected variable TMAO. The optimal cutoff value for predicting mortality was calculated using the Youden index. The area under the ROC curve and its statistical significance were also calculated. The Kaplan–Meier (KM) survival curve was plotted, and the two groups were compared using the log-rank test to assess statistical significance. We used both univariate and multivariate Cox proportional hazard models to investigate the connection between TMAO and all-cause mortality. To assess potential effect modification by subgroup variables on the TMAO association, the "TMAO grouping × subgroup variable" interaction term in the Cox models was statistically tested using the R package "jstable" on the CNSknowall Platform ( https://cnsknowall.com ), and a forest plot was generated. Considering the limited number of deaths in this study, we utilized bootstrapping (with 2000 bootstrap iterations) to reduce the risk of model overfitting. By repeatedly sampling the data for model validation. All statistical analyses were performed using SPSS (version 26.0; IBM Corp., Armonk, NY, USA), the CNSknowall Platform (a comprehensive web service for biomedical data analysis and visualization) or SPlot (a free online platform for data visualization and graphing)[ 23 ]. P < 0.05 was considered statistically significant. Results Baseline characteristics Among the 389 CAD patients, 25 were lost to follow-up, leaving 364 patients for outcome analysis. During a median follow-up of 39 months (IQR: 37, 42 months), 40 deaths (11.0%) were recorded. The baseline characteristics of the 364 enrolled CAD patients are summarized in Table 1. The mean age of the cohort was 68.1 ± 10.4 years, and 30.8% of the participants were female. Patients were categorized into two groups based on survival outcomes (Live vs . Death) during follow-up. Table 1. Baseline characteristics stratified by survival outcomes in CAD patients. Variables All N = 364 Live N = 324 Death N = 40 P -value Age (yrs) 68.1 ± 10.4 67.1 ± 9.95 75.9 ± 11.0 <0.001 Female (N, %) 112 (30.8) 96 (29.6) 16 (40.0) 0.180 Diabetes (%) 37.9 36.1 52.5 0.044 Smoke (%) 47.5 47.8 45.0 0.734 Hypertension (%) 71.2 69.4 85.0 0.040 TG (mmol/L) 1.32 (0.96, 1.93) 1.32 (0.96, 1.95) 1.35 (0.99, 1.82) 0.721 TC (mmol/L) 3.77 (3.12, 4.48) 3.76 (3.13, 4.50) 3.82 (2.90, 4.34) 0.441 HDL-C (mmol/L) 1.02 (0.86, 1.17) 1.03 (0.87, 1.18) 0.94 (0.77, 1.10) 0.070 LDL-C (mmol/L) 2.37 (1.78, 2.99) 2.35 (1.79, 3.02) 2.60 (1.70, 2.93) 0.875 eGFR (mL/min/1.73 m 2 ) 80.15 (61.95, 91.40) 83.15 (65.05, 92.55) 52.00 (25.63, 71.93) <0.001 NT-proBNP (pg/mL) 208.42 (62.20, 782.33) 157.85 (56.94, 757.87) 1713.79 (355.20, 3189.39) <0.001 hs-TnT (pg/mL) 9.0 (4.0, 32.0) 8.5 (3.0, 26.0) 30.5 (9.0, 91.3) 0.238 LVEF (%) 54.0 (40.3, 62.0) 55.0 (42.0, 62.0) 43.5 (29.0, 61.0) 0.020 TMAO (ng/mL) 201.82 (120.91, 299.80) 193.31 (113.67, 280.26) 324.81 (167.06, 649.19) 0.001 Antiplatelet drugs (%) 93.4 93.4 80.0 0.001 ACEI/ARB (%) 69.0 69.4 65.0 0.567 β-blocker (%) 81.3 81.5 80.0 0.821 Statins (%) 98.9 99.4 95.0 0.088 Values expressed as mean ± SD, percentage or median (interquartile range). Abbreviations: TG = Triglyceride; TC = total cholesterol; HDL-C = high density lipoprotein cholesterol; LDL-C = low-density lipoprotein cholesterol; eGFR = estimated glomerular filtration rate; NT-proBNP = N-terminal pro-brain natriuretic peptide; hs-TnT = high‐sensitivity troponin T; LVEF = left ventricular ejection fraction; TMAO = trimethylamine N-oxide; ACEI = Angiotensin-Converting Enzyme Inhibitor; ARB = Angiotensin Receptor Blocker. Patients who experienced all-cause mortality were generally older, had lower eGFR and LVEF, and lower usage of antiplatelet drugs, and showed a higher prevalence of diabetes and hypertension. They also exhibited significantly higher levels of plasma TMAO and NT-proBNP compared to those who survived (all P <0.05). Baseline characteristics of participants with missing data were compared with those with complete data (including age, sex, diabetes, smoking status, hypertension, eGFR, and TMAO), with all P -values >0.05, indicating no systematic differences (S1 Table). Baseline fasting plasma TMAO and all-cause mortality In the ROC curve analysis of the entire population, TMAO levels above 317.62 ng/mL identified patients at greater risk of death, with a sensitivity of 55%, specificity of 81%, area under the ROC curve (AUC) 0.68; 95% CI: 0.579–0.780; P < 0.0001, and Youden index 0.37 (Fig. 1). Using this cutoff (317.62 ng/mL) determined in the ROC curve analysis, patients were classified into two groups: Group 1 (n = 283) with lower TMAO levels and Group 2 (n = 81) with higher TMAO levels. Kaplan–Meier survival analysis demonstrated that patients with elevated TMAO levels (>317.62 ng/mL) had a significantly higher incidence of all-cause mortality compared to those with lower TMAO levels (≤317.62 ng/mL), with an unadjusted HR of 4.546 (95% CI: 2.087–9.903; P < 0.0001) (Fig 2). Association between TMAO levels and all-cause mortality Univariate COX regression analyses stratified by survival outcomes are presented in S2 Table. Multifactorial COX regression analyses were presented in Table 2. Model 1 (minimally adjusted model) adjusted for traditional risk factors including gender, age, TG, TC, LDL-C, HDL-C, hypertension, smoking status, and diabetes. On the other hand, Model 2 (primary model) further adjusted for additional variables including hs-TnT, NT-proBNP, LVEF, and eGFR. In Model 2, elevated plasma TMAO levels were independently associated with an increased risk of all-cause mortality (HR 2.626; 95% CI: 1.361 to 5.065; P = 0.004). Table 2. Relationship between all-cause mortality and plasma TMAO levels based on multivariate Cox regression analysis and bootstrapping analysis. Model Multivariate analysis Bootstrapping analysis P HR CI lower CI upper P HR CI lower CI upper Unadjusted <0.001 4.766 2.556 8.889 <0.001 4.768 2.609 9.143 Model 1 0.001 2.972 1.536 5.750 0.002 2.971 1.493 6.726 Model 2 0.004 2.626 1.361 5.065 0.004 2.625 1.330 6.190 Model 1: adjusted for conventional risk factors including gender, age, smoking status, hypertension, diabetes, TG, TC, LDL-C, and HDL-C. Model 2: adjusted for conventional risk factors in model 1 plus hs-TnT, NT-proBNP, LVEF and eGFR. Through bootstrap validation, the association between elevated TMAO levels and increased mortality risk remained statistically significant across 2000 bootstrap samples (HR 2.625; 95% CI: 1.330 to 6.190; P = 0.004). Stratified analysis and interaction tests Forest plots (Fig 3) demonstrated the impact of TMAO on all-cause mortality across various subgroups between Group 1 and Group 2 including age (<65 years and ≥65 years), gender (female and male), hypertension (Yes and No), smoke (Yes and No), diabetes (Yes and No), eGFR (<90 mL/min/1.73 m 2 and ≥90 mL/min/1.73 m 2 ), LDL-C (0.014 ng/mL), NT-proBNP (0.05), indicating that the impact of TMAO on all-cause mortality was consistent across these subgroups. Discussion This study demonstrates that elevated baseline plasma TMAO levels are significantly associated with an increased risk of all-cause mortality in Chinese patients with CAD, regardless of the presence of diabetes, after adjustment for some conventional risk factors, including comorbidities (e.g., hypertension), demographic characteristics, and some cardiorenal indices (NT-proBNP and eGFR). Although both baseline plasma TMAO levels and diabetes prevalence were significantly higher in patients who died, the present study demonstrated that the predictive value of TMAO for all-cause mortality remained unaffected by concomitant diabetes. The interrelationship between TMAO and diabetes have been gradually studied and reported over the past decade. Animal models suggest that TMAO may affect glucose metabolism and promote diabetes[24–26]. In human observational studies, TMAO levels are often elevated in individuals with diabetes[27–29]. One prospective Chinese cohort study found that higher TMAO levels were associated with increased diabetes risk over 8.9 years follow-up[29], while a U.S. study found no such association over 12 years[30]. These inconsistencies underscore the complex relationship between TMAO and diabetes. Our study corroborated the prognostic value of TMAO for all-cause mortality, as reported in earlier clinical research. Specifically, elevated plasma TMAO levels predicted either MACEs (encompassing all-cause mortality) or all-cause mortality alone in two stable CAD cohorts, regardless of diabetic status, over 3- and 5-year follow-ups[17,31]. Furthermore, an independent association between elevated TMAO levels and poor prognosis (all-cause death and myocardial infarction) was observed over 2-year follow-up in a separate cohort with acute myocardial infarction[18]. Our findings diverge from those reported in a northern Chinese cohort, in which the association between TMAO and MACEs was observed only in CAD patients with diabetes. The absence of this association in their non-diabetic subgroup may be attributable to methodological differences in TMAO stratification[19]. Whereas the prior study categorized TMAO levels by tertiles, our analysis employed an optimal cutoff value[19]. Our study also showed that the deceased cohort had higher plasma TMAO levels and a lower eGFR, which was consistent with some clinical reports linking higher plasma TMAO levels to worse long-term survival in patients with chronic kidney disease[22,32]. TMAO is bidirectionally associated with renal function since it is both cleared by the kidneys and associated with impaired renal function and renal fibrosis[16,33]. Consistent with prior research, our results support the existence of a robust association between elevated TMAO levels and increased mortality risk in both stable CAD and acute myocardial infarction cohorts, even after adjusting for the cardiorenal indicator eGFR[18,31]. Previous research have found that TMAO is closely associated with pathophysiological processes of CAD. Emerging evidence further suggests that TMAO may influence the efficacy of antiplatelet therapy, which is central to CAD management. Aspirin and clopidogrel are the most commonly prescribed antiplatelet agents. Zhu et al. demonstrated elevated TMAO levels appeared to partially attenuate the antiplatelet effects of low-dose aspirin (81 mg per day) in healthy individuals[12]. Additionally, TMAO has been found to significantly increase Carboxylesterase 1 protein activity and clopidogrel hydrolysis in the liver, leading to reduced formation of clopidogrel's active metabolite and impaired platelet response in mice[34]. In a rat model of ischemia and reperfusion, TMAO also mitigated the antiplatelet aggregation of clopidogrel[13]. These findings suggest that elevated plasma TMAO may weaken the response to antiplatelet drugs, potentially increasing the risk of major adverse cardiovascular events, including all-cause mortality, in CAD patients prescribed aspirin or clopidogrel. This study has several limitations. First, this was an observational analysis, and residual confounding cannot be excluded. Most importantly, the study protocol and analysis plan were not pre-registered in a public repository. Therefore, the findings should be considered exploratory and hypothesis-generating, and require validation in pre-registered, prospective studies. Second, data on dietary intake were not available which could influence plasma TMAO levels. Third, we did not assess cause-specific mortality, which limits the ability to clarify the causal mechanisms underlying the observed associations. Lastly, the absence of inflammatory biomarkers or microbiota profiling limits mechanistic insight, which we plan to address in subsequent studies. This study demonstrates that elevated plasma TMAO levels are independently associated with an increased risk of all-cause mortality in patients with CAD in South China, irrespective of diabetes status. These findings highlight the prognostic value of TMAO as a potential biomarker for risk stratification in CAD. Our future research should focus on elucidating the underlying mechanisms linking TMAO to adverse outcomes. Abbreviations TG Triglyceride TC total cholesterol HDL-C high density lipoprotein cholesterol LDL-C low-density lipoprotein cholesterol eGFR estimated glomerular filtration rate NT-proBNP N-terminal pro-brain natriuretic peptide hs-TnT high-sensitivity troponin T LVEF left ventricular ejection fraction TMAO trimethylamine N-oxide ACEI Angiotensin-Converting Enzyme Inhibitor ARB Angiotensin Receptor Blocker. Declarations Ethics approval and consent to participate The study was conducted in accordance with the Declaration of Helsinki (as revised in 2024) and received approval from the Ethics Committee of Xiangya Hospital, Central South University (Approval number: 2024040396). Clinical trial number Not applicable Consent for publication Not applicable Availability of data and materials The datasets used and analysed during the current study are available from the corresponding author on reasonable request. Conflict of interest The authors state no conflict of interest. Funding Statement This work was supported by grants from the Changsha Science and Technology Bureau, Hunan province, China (KQ2202367); the Science and Technology Department of Hunan Province (2022JJ70156), Hunan Key Laboratory for Bioanalysis of Complex Matrix Samples (FZJZ-202307); and the National Natural Science Foundation of China (82474013). Authors’ Contributions Hao Zhang: Conceptualization, Formal analysis, Writing–original draft, Investigation, Methodology. Yuan-yuan Peng: Conceptualization, Methodology, Investigation, Project administration. Xi-ya Lu: Methodology, Investigation, Data curation. Yu-xing Li: Methodology, Investigation, Data curation. 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Gao X, Liu X, Xu J, Xue C, Xue Y, Wang Y. Dietary trimethylamine N-oxide exacerbates impaired glucose tolerance in mice fed a high fat diet. J Biosci Bioeng. 2014;118:476–81. https://doi.org/10.1016/j.jbiosc.2014.03.001 . Shih DM, Wang Z, Lee R, Meng Y, Che N, Charugundla S, Qi H, Wu J, Pan C, Brown JM, Vallim T, Bennett BJ, Graham M, Hazen SL, Lusis AJ. Flavin containing monooxygenase 3 exerts broad effects on glucose and lipid metabolism and atherosclerosis. J Lipid Res. 2015;56:22–37. https://doi.org/10.1194/jlr.M051680 . Li S, Chen S, Lu X, Fang A, Chen Y, Huang R, Lin X, Huang Z, Ma J, Huang B, Zhu H. Serum trimethylamine-N-oxide is associated with incident type 2 diabetes in middle-aged and older adults: a prospective cohort study. J Transl Med. 2022;20:374. https://doi.org/10.1186/s12967-022-03581-7 . Kalagi NA, Thota RN, Stojanovski E, Alburikan KA, Garg ML. Association between Plasma Trimethylamine N-Oxide Levels and Type 2 Diabetes: A Case Control Study. Nutrients 2022;14:2093. https://doi.org/10.3390/nu14102093 Shan Z, Sun T, Huang H, Chen S, Chen L, Luo C, Yang W, Yang X, Yao P, Cheng J, Hu FB, Liu L. Association between microbiota-dependent metabolite trimethylamine-N-oxide and type 2 diabetes. Am J Clin Nutr. 2017;106:888–94. https://doi.org/10.3945/ajcn.117.157107 . Lemaitre RN, Jensen PN, Wang Z, Fretts AM, McKnight B, Nemet I, Biggs ML, Sotoodehnia N, De Oliveira Otto MC, Psaty BM, Siscovick DS, Hazen SL, Mozaffarian D. Association of Trimethylamine N -Oxide and Related Metabolites in Plasma and Incident Type 2 Diabetes: The Cardiovascular Health Study. JAMA Netw Open. 2021;4:e2122844. https://doi.org/10.1001/jamanetworkopen.2021.22844 . Senthong V, Wang Z, Li XS, Fan Y, Wu Y, Wilson Tang WH, Hazen SL. Intestinal Microbiota-Generated Metabolite Trimethylamine‐ N‐ Oxide and 5‐Year Mortality Risk in Stable Coronary Artery Disease: The Contributory Role of Intestinal Microbiota in a COURAGE‐Like Patient Cohort. J Am Heart Assoc. 2016;5:e002816. https://doi.org/10.1161/JAHA.115.002816 . Missailidis C, Hällqvist J, Qureshi AR, Barany P, Heimbürger O, Lindholm B, Stenvinkel P, Bergman P. Serum Trimethylamine-N-Oxide Is Strongly Related to Renal Function and Predicts Outcome in Chronic Kidney Disease. PLoS ONE. 2016;11:e0141738. https://doi.org/10.1371/journal.pone.0141738 . Fretts AM, Hazen SL, Jensen P, Budoff M, Sitlani CM, Wang M, de Oliveira Otto MC, DiDonato JA, Lee Y, Psaty BM, Siscovick DS, Sotoodehnia N, Tang WHW, Lai H, Lemaitre RN, Mozaffarian D. Association of Trimethylamine N-Oxide and Metabolites With Mortality in Older Adults. JAMA Netw Open. 2022;5:e2213242. https://doi.org/10.1001/jamanetworkopen.2022.13242 . Ge P-X, Tai T, Jiang L-P, Ji J-Z, Mi Q-Y, Zhu T, Li Y-F, Xie H-G. Choline and trimethylamine N-oxide impair metabolic activation of and platelet response to clopidogrel through activation of the NOX/ROS/Nrf2/CES1 pathway. J Thromb Haemost. 2023;21:117–32. https://doi.org/10.1016/j.jtha.2022.10.010 . Additional Declarations No competing interests reported. Supplementary Files S1Table.pdf S1 Table. Baseline characteristics of participants with complete data versus those with missing data Values are expressed as mean ± SD, percentage, or median (interquartile range). S2Table.pdf S2 Table. Association between plasma TMAO levels and all-cause mortality in univariate Cox regression analysis. Abbreviations: TG = Triglyceride; TC = total cholesterol; HDL-C = high-density lipoprotein cholesterol; LDL-C = low-density lipoprotein cholesterol; eGFR = estimated glomerular filtration rate; NT-proBNP = N-terminal pro-brain natriuretic peptide; hs-TnT = high‐sensitivity troponin T; LVEF = left ventricular ejection fraction; TMAO = trimethylamine N-oxide. S3ethicsapprovaldocuments.pdf S3.ethics approval documents Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 12 Apr, 2026 Reviewers agreed at journal 05 Apr, 2026 Reviews received at journal 03 Apr, 2026 Reviewers agreed at journal 01 Apr, 2026 Reviewers invited by journal 29 Mar, 2026 Editor invited by journal 17 Mar, 2026 Editor assigned by journal 13 Mar, 2026 Submission checks completed at journal 13 Mar, 2026 First submitted to journal 08 Mar, 2026 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-9065037","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":616195483,"identity":"fa311da5-51ba-4a85-be29-ffda11f23702","order_by":0,"name":"Hao Zhang","email":"","orcid":"","institution":"Central South University","correspondingAuthor":false,"prefix":"","firstName":"Hao","middleName":"","lastName":"Zhang","suffix":""},{"id":616195484,"identity":"4fc0b9d3-2a8f-4eda-9b28-637caf1c7a35","order_by":1,"name":"Yuan-yuan Peng","email":"","orcid":"","institution":"Central South University","correspondingAuthor":false,"prefix":"","firstName":"Yuan-yuan","middleName":"","lastName":"Peng","suffix":""},{"id":616195485,"identity":"3e612946-86d6-403f-a94c-1b8dec2fc237","order_by":2,"name":"Xi-ya Lu","email":"","orcid":"","institution":"Central South University","correspondingAuthor":false,"prefix":"","firstName":"Xi-ya","middleName":"","lastName":"Lu","suffix":""},{"id":616195488,"identity":"d6ff2e17-c438-4c58-8a9c-c468aaeac224","order_by":3,"name":"Yu-xing Li","email":"","orcid":"","institution":"Central South University","correspondingAuthor":false,"prefix":"","firstName":"Yu-xing","middleName":"","lastName":"Li","suffix":""},{"id":616195489,"identity":"7b5c1bd5-1694-4728-886f-64891fac1ec7","order_by":4,"name":"Wen-zhi Li","email":"","orcid":"","institution":"Central South University","correspondingAuthor":false,"prefix":"","firstName":"Wen-zhi","middleName":"","lastName":"Li","suffix":""},{"id":616195491,"identity":"0cd31cf0-1938-4215-8b8c-1b9f6ba9425d","order_by":5,"name":"Yi Hu","email":"","orcid":"","institution":"Central South University","correspondingAuthor":false,"prefix":"","firstName":"Yi","middleName":"","lastName":"Hu","suffix":""},{"id":616195494,"identity":"865c9c60-a58f-46cf-998d-d221e30ad4ef","order_by":6,"name":"Li Wan","email":"","orcid":"","institution":"Central South University","correspondingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Wan","suffix":""},{"id":616195496,"identity":"c5ddddcf-a0a2-493c-bd31-5d29cca07a0b","order_by":7,"name":"Bi-lian Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA10lEQVRIiWNgGAWjYBACA2bmhgMMDBJy/BA+MzFaGEFaLIwlG4jWwsAIUluRuOEAsVrM2RkbD/zcIWFsfPz4MwmGCuvEBvazB/BqsWxmbDjYe0ZCzuxMjpkEw5n0xAaevAT8DjsM9Atvm4Sx2Q0eNgnGtsOJDRI8BgS1HPzbJpG4eQb7MwnGf0RqOQy0JXGDBIOZBJBNWAvIL4dlgQ6TOJNjbJFwLN24jScHvxZz/sOHP75tq5Pjbz/+8MaHGmvZfvYz+LWgggQgZiNB/SgYBaNgFIwCHAAAMmlDGbPPSF4AAAAASUVORK5CYII=","orcid":"","institution":"Central South University","correspondingAuthor":true,"prefix":"","firstName":"Bi-lian","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2026-03-08 14:53:41","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9065037/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9065037/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105986236,"identity":"4770b00d-7fb8-44f3-b7d4-6e732dc7f774","added_by":"auto","created_at":"2026-04-02 07:28:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":600137,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9065037/v1/3c89789f-cf3c-4d81-8e98-ddee9a41d3b1.pdf"},{"id":105986089,"identity":"7041e570-3de1-4bd6-b7e3-2c749e10f115","added_by":"auto","created_at":"2026-04-02 07:27:47","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":104550,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eS1 Table. Baseline characteristics of participants with complete data versus those with missing data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eValues are expressed as mean ± SD, percentage, or median (interquartile range).\u003c/p\u003e","description":"","filename":"S1Table.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9065037/v1/4e2f59f53d010ea926efe3fd.pdf"},{"id":105986188,"identity":"c4e3a386-17e1-482e-8099-094bcb252eb6","added_by":"auto","created_at":"2026-04-02 07:28:10","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":79023,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eS2 Table. Association between plasma TMAO levels and all-cause mortality in univariate Cox regression analysis.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAbbreviations: TG = Triglyceride; TC = total cholesterol; HDL-C = high-density lipoprotein cholesterol; LDL-C = low-density lipoprotein cholesterol; eGFR = estimated glomerular filtration rate; NT-proBNP = N-terminal pro-brain natriuretic peptide; hs-TnT = high‐sensitivity troponin T; LVEF = left ventricular ejection fraction; TMAO = trimethylamine N-oxide.\u003c/p\u003e","description":"","filename":"S2Table.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9065037/v1/8db5bcdbbb56ed243dc1e338.pdf"},{"id":105986191,"identity":"6d1b7979-e535-4e56-918a-fad5a572c966","added_by":"auto","created_at":"2026-04-02 07:28:11","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":467777,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eS3.ethics approval documents\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"S3ethicsapprovaldocuments.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9065037/v1/70b1c707f583ef2859568951.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prognostic value of elevated trimethylamine-N-oxide levels in patients with coronary artery disease in China","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCoronary artery disease (CAD) remains a leading cause of morbidity worldwide, with a continuously rising prevalence in China[\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e]. Despite advances in medical management, the risk of death among CAD patients remains substantial. Recently, growing attention has focused on trimethylamine N-oxide (TMAO), a gut microbiota-dependent metabolite, which has been implicated in the pathogenesis and progression of CAD[\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e–\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e]. TMAO is produced when dietary nutrients such as choline, L-carnitine, and betaine, commonly found in red meat, eggs, sausage, and processed foods, are metabolized by intestinal flora into trimethylamine (TMA). This TMA is subsequently oxidized to TMAO by flavin-containing monooxygenase 3 (FMO3) in the liver[\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAccumulating evidence has demonstrated that TMAO is closely associated with several pathophysiological processes that underlie CAD. These include disturbances in cholesterol and bile acid metabolism[\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e–\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e], foam cell formation[\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e], inflammation, endothelial dysfunction[\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e], platelet activation[\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e–\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e], atherosclerosis[\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e], fibrosis and vascular aging[\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSeveral studies have further suggested that elevated circulating TMAO levels are associated with an increased risk of major adverse cardiovascular events (MACEs) in CAD patients[\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e–\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e]. For instance, Tang et al. reported that higher plasma TMAO levels predicted an increased risk of MACEs, including all-cause death, in 4007 patients with stable CAD during a three-year follow-up[\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e]. Similarly, Toru et al. demonstrated that plasma TMAO was a superior independent predictor of all-cause mortality or myocardial infarction compared to other biomarkers (adrenomedullin, oxidized LDL, and natriuretic peptides) in 1079 patients with acute myocardial infarction over a two-year period[\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e]. Recently, Yu et al. found that elevated TMAO levels were significantly associated with MACEs only in CAD patients with diabetes in northern China, but not in those without diabetes[\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e], which clearly contrasted with previous studies.\u003c/p\u003e \u003cp\u003eDespite the potential of TMAO as a biomarker for CAD prognosis and as a target for therapeutic intervention, the current evidence remains limited and somewhat inconsistent. Particularly, the association between plasma TMAO levels and all-cause mortality among Chinese CAD patients, after adjusting for traditional risk factors (including diabetes), warrants further investigation. Therefore, this study aimed to investigate the relationship between circulating TMAO levels and all-cause mortality in a cohort of CAD patients from South China.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e\u003c/p\u003e\n\n \n\n "},{"header":"Methods","content":"\u003ch3\u003eStudy population and clinical outcomes\u003c/h3\u003e\u003cp\u003eThis observational cohort study enrolled 389 patients hospitalized with CAD between January 1, 2022, and December 31, 2022, at the Department of Geriatrics, Xiangya Hospital, Central South University. The diagnosis of CAD was confirmed based on coronary angiography, and defined as ≥ 50% stenosis in the luminal diameter of one or more major epicardial coronary arteries. The exclusion criteria for patients were as follows: (1) aged \u0026lt; 18 years old; (2) a diagnosis of malignant tumor requiring advanced medical or surgical therapy; (3) with clinically significant infectious diseases; (4) patients with severe hepatic dysfunction [alanine amino transferase (ALT) level \u0026gt; 135 U/L].\u003c/p\u003e\u003cp\u003eAll patients underwent comprehensive clinical evaluations, including physical examinations and biochemical assessments such as complete blood count, blood glucose, blood lipid profiles, hepatic and renal function tests, high-sensitivity troponin T (hs-TnT), and N-terminal pro-brain natriuretic peptide (NT-proBNP). Left ventricular ejection fraction (LVEF) was assessed using transthoracic echocardiography. Hypertension was defined as systolic blood pressure ≥ 140 mmHg or diastolic blood pressure ≥ 90 mmHg, consistent with standard guidelines[\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e]. Diabetes was diagnosed based on a fasting glucose level of ≥ 7.0 mmol/L or a 2-hour oral glucose tolerance test (OGTT) value of ≥ 11.1 mmol/L[\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e]. Smoking status included both current and former smokers.\u003c/p\u003e\u003cp\u003eClinical outcomes focused on all-cause mortality. These outcomes were tracked and verified semi-annually through a combination of telephone interviews, hospital outpatient visits, and official hospital records.\u003c/p\u003e\u003cp\u003e The study was conducted in compliance with the Declaration of Helsinki and received approval from the Ethics Committee of Xiangya Hospital, Central South University (Approval number: 202211735). Written informed consent was obtained from 389 participants or their legal guardians, as appropriate (age range: 38–95 years, 271 males, and 118 females).\u003c/p\u003e\u003ch2\u003eSample preparation and TMAO analysis\u003c/h2\u003e\u003cp\u003eBlood samples were collected from the peripheral veins of patients in the morning of the second day of hospitalization, following an overnight fast. The samples were drawn into ethylenediaminetetraacetic acid (EDTA) anticoagulant tubes, centrifuged at 3000 rpm for 10 minutes, and the plasma fraction was separated and stored at − 80°C until further analysis.\u003c/p\u003e\u003cp\u003ePlasma TMAO levels were measured using high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS) with a d9-(trimethyl)-labeled internal standard, following previously established protocols[\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e]. Absolute quantification was performed by calculating the peak area ratio of TMAO to the internal standard TMAO-d9, with the concentration of TMAO standards used as the independent variable. The actual TMAO concentration was determined based on a standard calibration curve generated during each analytical run. All sample recovery rates were within the range of 85%–115%, and the coefficients of variation (CV) for precision and interbatch variability were both ≤ 15%, meeting the established acceptance criteria.\u003c/p\u003e\u003cp\u003eOther laboratory parameters, including renal and liver function, lipid profiles, and cardiac biomarkers, were assessed in the clinical laboratory of Xiangya Hospital. Estimated glomerular filtration rate (eGFR) was calculated using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) formula.\u003c/p\u003e\u003ch3\u003eManagement of missing data and outliers\u003c/h3\u003e\u003cp\u003eTo minimize bias, variables with missing values less than 5% (triglyceride, total cholesterol, high-density lipoprotein cholesterol, eGFR, NT-proBNP, hs-TnT, and LVEF) were replaced with the mean of that variable. Variables with abnormal values were handled by the winsorization method, with the 1% and 99% as cut-off points. All missing and abnormal data were processed using the Statistical Package for Social Sciences (SPSS; version 26.0) software.\u003c/p\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eContinuous variables were presented as mean ± standard deviation (SD) or median (interquartile range, IQR), depending on their distribution. If the data followed a normal distribution and exhibited homogeneity of variance, Student’s t-test was used for inter-group comparisons; otherwise, the Mann–Whitney U-test was employed. Categorical variables were expressed as numbers (percentages) and compared between groups using the chi-square test or Fisher's exact test, as appropriate.\u003c/p\u003e\u003cp\u003eThe receiver operating characteristic (ROC) curve analysis was used to assess the predictive value of selected variable TMAO. The optimal cutoff value for predicting mortality was calculated using the Youden index. The area under the ROC curve and its statistical significance were also calculated. The Kaplan–Meier (KM) survival curve was plotted, and the two groups were compared using the log-rank test to assess statistical significance. We used both univariate and multivariate Cox proportional hazard models to investigate the connection between TMAO and all-cause mortality. To assess potential effect modification by subgroup variables on the TMAO association, the \"TMAO grouping × subgroup variable\" interaction term in the Cox models was statistically tested using the R package \"jstable\" on the CNSknowall Platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cnsknowall.com\u003c/span\u003e\u003cspan class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and a forest plot was generated.\u003c/p\u003e\u003cp\u003eConsidering the limited number of deaths in this study, we utilized bootstrapping (with 2000 bootstrap iterations) to reduce the risk of model overfitting. By repeatedly sampling the data for model validation. All statistical analyses were performed using SPSS (version 26.0; IBM Corp., Armonk, NY, USA), the CNSknowall Platform (a comprehensive web service for biomedical data analysis and visualization) or SPlot (a free online platform for data visualization and graphing)[\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e]. \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eBaseline characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAmong the 389 CAD patients, 25 were lost to follow-up, leaving 364 patients for outcome analysis. During a median follow-up of 39 months (IQR: 37, 42 months), 40 deaths (11.0%) were recorded.\u003c/p\u003e\n\u003cp\u003eThe baseline characteristics of the 364 enrolled CAD patients are summarized in Table 1. The mean age of the cohort was 68.1 \u0026plusmn; 10.4 years, and 30.8% of the participants were female. Patients were categorized into two groups based on survival outcomes (Live \u003cem\u003evs\u003c/em\u003e. Death) during follow-up.\u003c/p\u003e\n\u003cp\u003eTable 1.\u0026nbsp;Baseline characteristics stratified by survival outcomes in CAD patients.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003eAll\u003c/p\u003e\n \u003cp\u003eN = 364\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003eLive\u003c/p\u003e\n \u003cp\u003eN = 324\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003eDeath\u003c/p\u003e\n \u003cp\u003eN = 40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003eAge (yrs)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e68.1 \u0026plusmn; 10.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e67.1 \u0026plusmn; 9.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e75.9 \u0026plusmn; 11.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003eFemale (N, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e112 (30.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e96 (29.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e16 (40.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.180\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003eDiabetes (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e37.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e36.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e52.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003eSmoke (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e47.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e47.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e45.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.734\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003eHypertension (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e71.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e69.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e85.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003eTG (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e1.32 (0.96, 1.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e1.32 (0.96, 1.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e1.35 (0.99, 1.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.721\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003eTC (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e3.77 (3.12, 4.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e3.76 (3.13, 4.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e3.82 (2.90, 4.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.441\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003eHDL-C (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e1.02 (0.86, 1.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e1.03 (0.87, 1.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e0.94 (0.77, 1.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.070\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003eLDL-C (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e2.37\u0026nbsp;(1.78, 2.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e2.35 (1.79, 3.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e2.60 (1.70, 2.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.875\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003eeGFR (mL/min/1.73 m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e80.15\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;(61.95, 91.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e83.15\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;(65.05, 92.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e52.00\u003c/p\u003e\n \u003cp\u003e(25.63, 71.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003eNT-proBNP (pg/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e208.42\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;(62.20, 782.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e157.85\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;(56.94, 757.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e1713.79\u003c/p\u003e\n \u003cp\u003e(355.20, 3189.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003ehs-TnT (pg/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e9.0 (4.0, 32.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e8.5 (3.0, 26.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e30.5 (9.0, 91.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.238\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003eLVEF (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e54.0 (40.3, 62.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e55.0 (42.0, 62.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e43.5 (29.0, 61.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003eTMAO (ng/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e201.82\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;(120.91, 299.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e193.31\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;(113.67, 280.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e324.81\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;(167.06, 649.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003eAntiplatelet drugs (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e93.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e93.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e80.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25px;\"\u003e\n \u003cp\u003eACEI/ARB (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e69.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e69.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e65.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.567\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u0026beta;-blocker (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e81.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e81.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e80.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.821\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 25px;\"\u003e\n \u003cp\u003eStatins (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 134px;\"\u003e\n \u003cp\u003e98.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e99.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 143px;\"\u003e\n \u003cp\u003e95.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.088\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eValues expressed as mean \u0026plusmn; SD, percentage or median (interquartile range).\u003c/p\u003e\n\u003cp\u003eAbbreviations: TG = Triglyceride; TC = total cholesterol; HDL-C = high density lipoprotein cholesterol; LDL-C = low-density lipoprotein cholesterol; eGFR = estimated glomerular filtration rate; NT-proBNP = N-terminal pro-brain natriuretic peptide; hs-TnT = high‐sensitivity troponin T; LVEF = left ventricular ejection fraction; TMAO = trimethylamine N-oxide; ACEI = Angiotensin-Converting Enzyme Inhibitor; ARB = Angiotensin Receptor Blocker.\u003c/p\u003e\n\u003cp\u003ePatients who experienced all-cause mortality were generally older, had lower eGFR and LVEF, and lower usage of antiplatelet drugs, and showed a higher prevalence of diabetes and hypertension. They also exhibited significantly higher levels of plasma TMAO and NT-proBNP compared to those who survived (all \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBaseline characteristics of participants with missing data were compared with those with complete data (including age, sex, diabetes, smoking status, hypertension, eGFR, and TMAO), with all \u003cem\u003eP\u003c/em\u003e-values \u0026gt;0.05, indicating no systematic differences (S1 Table).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBaseline fasting plasma TMAO and all-cause mortality\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the ROC curve analysis of the entire population, TMAO levels above 317.62 ng/mL identified patients at greater risk of death, with a sensitivity of 55%, specificity of 81%, area under the ROC curve (AUC) 0.68; 95% CI: 0.579\u0026ndash;0.780; \u003cem\u003eP\u003c/em\u003e\u0026lt; 0.0001, and Youden index 0.37 (Fig. 1).\u003c/p\u003e\n\u003cp\u003eUsing this cutoff (317.62 ng/mL) determined in the ROC curve analysis, patients were classified into two groups: Group 1 (n = 283) with lower TMAO levels and Group 2 (n = 81) with higher TMAO levels. Kaplan\u0026ndash;Meier survival analysis demonstrated that patients with elevated TMAO levels (\u0026gt;317.62 ng/mL) had a significantly higher incidence of all-cause mortality compared to those with lower TMAO levels (\u0026le;317.62 ng/mL), with an unadjusted HR of 4.546 (95% CI: 2.087\u0026ndash;9.903; \u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.0001) (Fig 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssociation between TMAO levels and all-cause mortality\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUnivariate COX regression analyses stratified by survival outcomes are presented in S2 Table. Multifactorial COX regression analyses were presented in Table 2. Model 1 (minimally adjusted model) adjusted for traditional risk factors including gender, age, TG, TC, LDL-C, HDL-C, hypertension, smoking status, and diabetes. On the other hand, Model 2 (primary model) further adjusted for additional variables including hs-TnT, NT-proBNP, LVEF, and eGFR. In Model 2, elevated plasma TMAO levels were independently associated with an increased risk of all-cause mortality (HR 2.626; 95% CI: 1.361 to 5.065; \u003cem\u003eP\u003c/em\u003e = 0.004).\u003c/p\u003e\n\u003cp\u003eTable 2.\u0026nbsp;Relationship between all-cause mortality and plasma TMAO levels based on multivariate Cox regression analysis and bootstrapping analysis.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"99%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eModel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eMultivariate analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003eBootstrapping analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003eHR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003eCI lower\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003eCI upper\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003eHR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003eCI lower\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eCI upper\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eUnadjusted\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e4.766\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e2.556\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e8.889\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e4.768\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e2.609\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e9.143\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e2.972\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e1.536\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e5.750\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e2.971\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e1.493\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e6.726\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7px;\"\u003e\n \u003cp\u003e2.626\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e1.361\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e5.065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 3px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7px;\"\u003e\n \u003cp\u003e2.625\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e1.330\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e6.190\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eModel 1: adjusted for conventional risk factors including gender, age, smoking status, hypertension, diabetes, TG, TC, LDL-C, and HDL-C.\u003c/p\u003e\n\u003cp\u003eModel 2: adjusted for conventional risk factors in model 1 plus hs-TnT, NT-proBNP, LVEF and eGFR.\u003c/p\u003e\n\u003cp\u003eThrough bootstrap validation, the association between elevated TMAO levels and increased mortality risk remained statistically significant across 2000 bootstrap samples (HR 2.625; 95% CI: 1.330 to 6.190; \u003cem\u003eP\u003c/em\u003e = 0.004).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStratified analysis and interaction tests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eForest plots (Fig 3) demonstrated the impact of TMAO on all-cause mortality across various subgroups between Group 1 and Group 2 including age (\u0026lt;65 years and \u0026ge;65 years), gender (female and male), hypertension (Yes and No), smoke (Yes and No), diabetes (Yes and No), eGFR (\u0026lt;90 mL/min/1.73 m\u003csup\u003e2\u003c/sup\u003e and \u0026ge;90 mL/min/1.73 m\u003csup\u003e2\u003c/sup\u003e), LDL-C (\u0026lt;2.6 mmol/L and \u0026ge;2.6 mmol/L), hs-TnT (\u0026le;0.014 ng/mL and \u0026gt;0.014 ng/mL), NT-proBNP (\u0026lt;300 pg/mL and \u0026ge;300 pg/mL) (Fig 3). No significant interactions were observed between plasma TMAO levels and any of the predefined factors (\u003cem\u003eP\u0026nbsp;\u003c/em\u003efor interaction \u0026gt;0.05), indicating that the impact of TMAO on all-cause mortality was consistent across these subgroups.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study demonstrates that elevated baseline plasma TMAO levels are significantly associated with an increased risk of all-cause mortality in Chinese patients with CAD, regardless of the presence of diabetes, after adjustment for some conventional risk factors, including comorbidities (e.g., hypertension), demographic characteristics, and some cardiorenal indices (NT-proBNP and eGFR).\u003c/p\u003e\n\u003cp\u003eAlthough both baseline plasma TMAO levels and diabetes prevalence were significantly higher in patients who died, the present study demonstrated that the predictive value of TMAO for all-cause mortality remained unaffected by concomitant diabetes. The interrelationship between TMAO and diabetes have been gradually studied and reported over the past decade. Animal models suggest that TMAO may affect glucose metabolism and promote diabetes[24\u0026ndash;26]. In human observational studies, TMAO levels are often elevated in individuals with diabetes[27\u0026ndash;29]. One prospective Chinese cohort study found that higher TMAO levels were associated with increased diabetes risk over 8.9 years follow-up[29], while a U.S. study found no such association over 12 years[30]. These inconsistencies underscore the complex relationship between TMAO and diabetes.\u003c/p\u003e\n\u003cp\u003eOur study corroborated the prognostic value of TMAO for all-cause mortality, as reported in earlier clinical research. Specifically, elevated plasma TMAO levels predicted either MACEs (encompassing all-cause mortality) or all-cause mortality alone in two stable CAD cohorts, regardless of diabetic status, over 3- and 5-year follow-ups[17,31]. Furthermore, an independent association between elevated TMAO levels and poor prognosis (all-cause death and myocardial infarction) was observed over 2-year follow-up in a separate cohort with acute myocardial infarction[18]. Our findings diverge from those reported in a northern Chinese cohort, in which the association between TMAO and MACEs was observed only in CAD patients with diabetes. The absence of this association in their non-diabetic subgroup may be attributable to methodological differences in TMAO stratification[19]. Whereas the prior study categorized TMAO levels by tertiles, our analysis employed an optimal cutoff value[19].\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Our study also showed that the deceased cohort had higher plasma TMAO levels and a lower eGFR, which was consistent with some clinical reports linking higher plasma TMAO levels to worse long-term survival in patients with chronic kidney disease[22,32]. TMAO is bidirectionally associated with renal function since it is both cleared by the kidneys and associated with impaired renal function and renal fibrosis[16,33]. Consistent with prior research, our results support the existence of a robust association between elevated TMAO levels and increased mortality risk in both stable CAD and acute myocardial infarction cohorts, even after adjusting for the cardiorenal indicator eGFR[18,31].\u003c/p\u003e\n\u003cp\u003ePrevious research have found that TMAO is closely associated with pathophysiological processes of CAD. Emerging evidence further suggests that TMAO may influence the efficacy of antiplatelet therapy, which is central to CAD management. Aspirin and clopidogrel are the most commonly prescribed antiplatelet agents.\u0026nbsp;Zhu et al. demonstrated elevated TMAO levels appeared to partially attenuate the antiplatelet effects of low-dose aspirin (81 mg per day) in healthy individuals[12]. Additionally, TMAO has been found to significantly increase\u0026nbsp;Carboxylesterase 1\u0026nbsp;protein activity and clopidogrel hydrolysis in the liver, leading to reduced formation of clopidogrel\u0026apos;s active metabolite and impaired platelet response in mice[34]. In a rat model of ischemia and reperfusion, TMAO also mitigated the antiplatelet aggregation of clopidogrel[13]. These findings suggest that elevated plasma TMAO may weaken the response to antiplatelet drugs, potentially increasing the risk of major adverse cardiovascular events, including all-cause mortality, in CAD patients prescribed aspirin or clopidogrel.\u003c/p\u003e\n\u003cp\u003eThis study has several limitations. First, this was an observational analysis, and residual confounding cannot be excluded.\u0026nbsp;Most importantly, the study protocol and analysis plan were not pre-registered in a public repository. Therefore, the findings should be considered exploratory and hypothesis-generating, and require validation in pre-registered, prospective studies. Second, data on dietary intake were not available which could influence plasma TMAO levels. Third, we did not assess cause-specific mortality, which limits the ability to clarify the causal mechanisms underlying the observed associations. Lastly, the absence of inflammatory biomarkers or microbiota profiling limits mechanistic insight, which we plan to address in subsequent studies.\u003c/p\u003e\n\u003cp\u003eThis study demonstrates that elevated plasma TMAO levels are independently associated with an increased risk of all-cause mortality in patients with CAD in South China, irrespective of diabetes status. These findings highlight the prognostic value of TMAO as a potential biomarker for risk stratification in CAD. Our future research should focus on elucidating the underlying mechanisms linking TMAO to adverse outcomes.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTriglyceride\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003etotal cholesterol\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHDL-C\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ehigh density lipoprotein cholesterol\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLDL-C\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003elow-density lipoprotein cholesterol\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eeGFR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eestimated glomerular filtration rate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNT-proBNP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eN-terminal pro-brain natriuretic peptide\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ehs-TnT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ehigh-sensitivity troponin T\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLVEF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eleft ventricular ejection fraction\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTMAO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003etrimethylamine N-oxide\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eACEI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAngiotensin-Converting Enzyme Inhibitor\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eARB\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAngiotensin Receptor Blocker.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eThe study was conducted in accordance with the Declaration of Helsinki (as revised in 2024) and received approval from the Ethics Committee of Xiangya Hospital, Central South University (Approval number: 2024040396).\u003c/p\u003e\n\u003cp\u003eClinical trial number\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Not applicable\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials\u003c/p\u003e\n\u003cp\u003eThe datasets used and analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003eConflict of interest\u003c/p\u003e\n\u003cp\u003eThe authors state no conflict of interest.\u003c/p\u003e\n\u003cp\u003eFunding Statement\u003c/p\u003e\n\u003cp\u003eThis work was supported by grants from the Changsha Science and Technology Bureau, Hunan province, China (KQ2202367); the Science and Technology Department of Hunan Province (2022JJ70156), Hunan Key Laboratory for Bioanalysis of Complex Matrix Samples (FZJZ-202307); and the National Natural Science Foundation of China (82474013).\u003c/p\u003e\n\u003cp\u003eAuthors\u0026rsquo;\u0026nbsp;Contributions\u003c/p\u003e\n\u003cp\u003eHao Zhang: Conceptualization, Formal analysis, Writing\u0026ndash;original draft, Investigation, Methodology. Yuan-yuan Peng: Conceptualization, Methodology, Investigation, Project administration. Xi-ya Lu: Methodology, Investigation, Data curation. Yu-xing Li: Methodology, Investigation, Data curation. Wen-zhi Li: Methodology, Investigation, Data curation. Yi Hu: Methodology, Investigation, Data curation. Li Wan: Methodology, Investigation, Data curation. Bi-lian Chen: Conceptualization, Methodology, Investigation, Writing - review \u0026amp; editing, Supervision, Project administration.\u003c/p\u003e\n\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eWe sincerely thank Professor Yi-ren Wang for providing valuable statistical guidance. We thank LetPub (www.letpub.com.cn) for linguistic assistance and pre-submission expert review.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWang Z, Ma L, Liu M, Fan J, Hu S. Summary of the 2022 Report on Cardiovascular Health and Diseases in China. 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J Thromb Haemost. 2023;21:117\u0026ndash;32. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jtha.2022.10.010\u003c/span\u003e\u003cspan address=\"10.1016/j.jtha.2022.10.010\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Trimethylamine N-oxide, All-cause mortality, Coronary artery disease","lastPublishedDoi":"10.21203/rs.3.rs-9065037/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9065037/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eCoronary artery disease (CAD) is a prevalent cardiovascular condition worldwide. Trimethylamine N-oxide (TMAO), a metabolite produced by gut microbiota, plays a crucial role in the pathogenesis and progression of CAD. However, the long-term prognostic value of plasma TMAO for all-cause mortality in Chinese CAD patients remains to be fully explored.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eIn this observational cohort study, 389 hospitalized CAD patients, confirmed via coronary angiography at Xiangya Hospital in 2022, were enrolled. Plasma TMAO levels were measured using liquid chromatography-tandem mass spectrometry. All-cause mortality events were identified through telephone interviews, hospital outpatient visits, and official hospital records, conducted semi-annually. Kaplan-Meier analysis and Cox regression analysis were employed to investigate the relationship between TMAO levels and all-cause mortality.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAmong 364 CAD patients who completed the median follow-up period of 39 months (IQR: 37\u0026ndash;42 months), 40 patients (11.0%) experienced all-cause mortality. Patients with elevated TMAO levels, based on the optimal cutoff value of 317.62 ng/mL, had a significantly higher mortality rate compared to those with lower levels (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). After adjusting for conventional risk factors, including diabetes, elevated TMAO levels remained a significant predictor of all-cause mortality (hazard ratio [HR] 2.626; 95% CI: 1.361 to 5.065; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eElevated plasma TMAO levels are significantly associated with increased all-cause mortality over a median follow-up of 39 months in CAD patients from China.\u003c/p\u003e","manuscriptTitle":"Prognostic value of elevated trimethylamine-N-oxide levels in patients with coronary artery disease in China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-02 07:26:16","doi":"10.21203/rs.3.rs-9065037/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-04-12T13:02:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"137417618178127078974945376595704422794","date":"2026-04-05T07:38:09+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-03T13:35:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"33868222419888299846686363474995845140","date":"2026-04-01T22:17:39+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-29T10:11:57+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-17T17:42:53+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-14T02:31:15+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-14T02:31:09+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cardiovascular Disorders","date":"2026-03-08T14:40:39+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6561dc95-ef45-408a-a1f9-960a2b733b08","owner":[],"postedDate":"April 2nd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-02T07:26:17+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-02 07:26:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9065037","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9065037","identity":"rs-9065037","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}