The Relationship Between Serum Amyloid A Protein and Short-Term Adverse Cardiovascular Outcomes in Older Patients with STEMI: A Prospective Study

The Relationship Between Serum Amyloid A Protein and Short-Term Adverse Cardiovascular Outcomes in Older Patients with STEMI: A Prospective Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Relationship Between Serum Amyloid A Protein and Short-Term Adverse Cardiovascular Outcomes in Older Patients with STEMI: A Prospective Study Xinying Yang, Xinhui Wang, Chen Chen, Xianjing Xu, Xuanchao Cao, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9015088/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Background Despite improved treatments for myocardial infarction (MI), residual risk persists in older patients, mainly attributed to age- associated comorbidities and inflammation. Serum amyloid A (SAA) lacks sufficient evidence for predicting short-term clinical endpoints in older patients with ST-segment elevation MI (STEMI). Methods We prospectively enrolled 327 older patients (≥ 60 years) with STEMI individuals treated with primary percutaneous coronary intervention and 327 healthy controls (HCs). SAA levels were measured at admission. The Global Registry of Acute Coronary Events score, Thrombolysis in Myocardial Infarction risk score, and frailty index were assessed. The primary endpoint was 30-day major adverse cardiovascular events (MACE), defined as cardiac death, heart failure (HF), and cardiogenic shock (CS). Results The serum SAA levels in older STEMI patients were significantly higher than those in the HC group ([735.94 ± 506.60] ng/mL vs. [427.58 ± 273.70] ng/mL, P<0.001). Moreover, the SAA expression was further remarkably elevated in patients with 30-day heart failure (HF), cardiogenic shock (CS) or cardiac death events (all P<0.001). After adjusting for risk scores, traditional biomarkers, and clinical variables, higher SAA levels independently predicted HF events (all adjusted P < 0.01) and CS and/or cardiac death (all adjusted P < 0.01). The inclusion of SAA in an established risk factor models significantly enhanced C-statistics, net reclassification, and integrated discrimination. SAA strongly predicted HF in non-frail patients (hazard ratio [HR] = 5.477, P < 0.001), but not in frail patients (HR = 1.558, P = 0.104), with a significant interaction (P = 0.019). Conclusions SAA is an independent predictor of 30-day MACE in older patients with STEMI and enhances traditional risk assessment, especially in non-frail individuals. Trial registration: ClinicalTrails.gov registration no. NCT03752515 acute myocardial infarction adverse cardiovascular events older patients prospective study serum amyloid A protein Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Background Acute myocardial infarction (AMI) is a prevalent cardiac emergency associated with considerable risks of morbidity and mortality.[ 1 , 2 ] Although considerable advances have been made in reperfusion strategies, pharmacologic interventions and the management of modifiable risk factors, adverse cardiovascular events persist following AMI.[ 3 ] The factors determining this residual risk—defined as the risk that persists despite optimal control of known modifiable factors—are still uncertain. Among these determinants, age-related conditions and inflammation play a complex yet increasingly recognized role.[ 4 , 5 ] The global population structure has undergone substantial changes, with a rapid increase in both the size and proportion of the older population.[ 6 ] The aging process in China has garnered exceptional attention worldwide. The population aged 60 years and over is predicted to nearly double, increasing from 168 million (12.4%) in 2010 to 402 million (28%) by 2040.[ 6 ] Aging is a high-risk factor for increased mortality and complication rates after percutaneous coronary intervention (PCI),[ 7 ] yet evidence-based risk assessment tools for the older population remain lacking. Notably, age-related comorbidities such as frailty and sarcopenia[ 8 ] not only exacerbate systemic inflammation but also create therapeutic dilemmas in clinical practice, thereby impairing treatment efficacy.[ 9 ] AMI is primarily caused by the rupture of coronary atherosclerotic plaques, which triggers thrombosis that rapidly occludes the coronary lumen, leading to persistent myocardial ischemia, hypoxia, and necrosis.[ 10 ] Inflammatory response exerts a pivotal regulatory role in the pathological progression of AMI.[ 11 ] Myocardial ischemic injury can induce a substantial release of inflammatory biomarkers. These markers are not only involved in the infarct repair of myocardial tissue but may also lead to secondary damage in the ischemic myocardial region.[ 12 ] High-sensitivity C-reactive protein (hs-CRP), and other relevant indicators are pivotal clinical inflammatory markers in this context and have been thoroughly explored.[ 13 – 15 ] Notably, increased hs-CRP levels are linked to more severe myocardial and microvascular injury and subsequent clinical outcomes.[ 16 ] In the EPIC-NORFOLK cohort, which followed a large primary prevention population for 20 years, hs-CRP was independently associated with major adverse cardiovascular events.[ 17 ] Lin et al. demonstrated that hs-CRP is a prognostic biomarker for poor outcomes in older patients with AMI, yet the association between hs-CRP and frailty was not analyzed in their study.[ 18 ] During in vivo inflammation, hepatic synthesis of serum amyloid A protein (SAA) and hs-CRP is upregulated by inflammatory cytokines.[ 19 ] Both markers were significantly and independently associated with subsequent major adverse cardiovascular events, including acute myocardial infarction, congestive heart failure, and stroke. Notably, however, only SAA showed an independent but moderate association with angiographically confirmed coronary artery disease.[ 20 ] Johnson et al. observed that SAA may be complementary to hs-CRP.[ 20 ] Furthermore, SAA may serve as a superior marker of disease activity, featuring a broader dynamic range and faster response, and it denotes a distinct type of acute-phase response compared to hs-CRP.[ 21 , 22 ] However, evidence remains remarkably limited concerning the prognostic significance of serum SAA for outcomes in older patients with AMI. This study specifically fills this critical knowledge gap by assessing SAA’s predictive ability for 30-day follow-up major cardiovascular events (MACE) in this high-risk population. Methods Study design and participants We conducted a prospective cohort study enrolling 327 patients aged ≥ 60 years with ST-segment elevation myocardial infarction (STEMI) from the Acute Coronary Syndrome Genetics and Biomarkers Registry Study (ARSGB-ACS) (ClinicalTrials.gov identifier: NCT03752515). All patients were admitted within 12 h of chest pain onset and underwent primary PCI at Henan Provincial People’s Hospital from March 4, 2021, to July 16, 2022. Primary PCI and adjunctive pharmacological treatment were performed according to contemporary guidelines.[ 23 , 24 ] Patients presenting with STEMI combined with cardiogenic shock (CS), as well as those receiving rescue PCI following unsuccessful thrombolysis, were excluded from the study.[ 25 ] Additional exclusion criteria included a history of malignancy, cardiomyopathy, valvular heart disease, infectious diseases, renal insufficiency, or systemic inflammatory disease, as these comorbidities may substantially alter SAA levels.[ 19 ] Healthy control subjects (HCs) were free of cardiovascular disease, as confirmed by detailed medical history, thorough physical examination, ECG, and echocardiography. This study was approved by the Ethics Committee of Henan Provincial People’s Hospital. All procedures were conducted in accordance with the Declaration of Helsinki. All participants provided written informed consent, and this study was conducted in accordance with the STROBE checklist ( Supplementary Table S1 ). Measurement of SAA and other biomarkers Venous blood samples were collected into anticoagulant-free serum collection tubes, and the samples were immediately centrifuged at 2000 ×g for 10 minutes within one hour after collection. All serum specimens were aliquoted and preserved at − 80°C in a low-temperature refrigerator, and the SAA concentrations were determined in a single blind batch assay subsequently. Serum SAA concentrations were determined using a commercial enzyme-linked immunosorbent assay kit (R&D Systems Europe, Abingdon, OX, UK). N-terminal pro-B-type natriuretic peptide (NT-proBNP) levels were measured using the Vitros 5600 NT-proBNP ECI assay (Ortho Clinical Diagnostics). High-sensitivity troponin I (hs-cTnI) levels were determined by chemiluminescence assay. hs-CRP levels were measured using a turbidimetric inhibition immunoassay. All biomarker assessments were conducted by investigators blinded to the patients’ clinical features and endpoints. Calculation of risk score The Global Registry of Acute Coronary Events (GRACE) score[ 26 ] and the Thrombolysis in Myocardial Infarction (TIMI) risk score,[ 27 ] which were used to calculate the risk of in-hospital mortality or nonfatal myocardial infarction, are widely used prediction tools that have already been reported elsewhere. Frailty assessment Based on previous literature, we selected 28 clinical variables and constructed a frailty index (FI) using a deficit accumulation model.[ 28 , 29 ] The indicators included in the frailty index (FI) mainly involve diseases, comorbidities, clinical symptoms, physical signs, and laboratory test results. Each selected variable must be associated with aging, predict adverse clinical outcomes, represent a range of organ systems when used together, and not be common to all individuals at a relatively early stage. Binary variables such as diabetes were recorded as 0 for none and 1 for existence. Continuous variables were categorized into binary scores as deemed appropriate. All subjects were assigned a score ranging from 0 to 28. The FI was calculated as the frailty score divided by 28, ranging from 0 to 1. While deficit accumulation–based frailty exists on a continuous spectrum, earlier studies divided subjects into two categories: those with FI ≥ 0.25 (frailty score ≥ 7) were classified as frail, and those with FI < 0.25 (frailty score < 7) as non‑frail. Study endpoints Participants who experienced cardiac death, new-onset heart failure (HF), or CS within the 30-day follow-up were considered to have developed MACE. All patients included in the study had complete follow-up data. New-onset HF[ 23 ] was diagnosed if all of the following criteria were met: (i) normal heart function at admission (Killip class I) and no history of HF, (ii) typical symptoms and signs of HF, (iii) NT-proBNP concentration > 900 ng/L, and (iv) treatment with intravenous diuretics. CS[ 23 ] was defined as systolic blood pressure ≤ 90 mmHg for > 30 min after exclusion of hypovolemia, with clinical evidence of hypoperfusion, inotrope dependence, or mechanical left ventricular support to correct this situation. Each event was adjudicated by two independent, blinded physician adjudicators. If any discrepancies arose during evaluation, the reviewers discussed and re-evaluated their assignments, and sought a third blinded reviewer for further advice. Statistical analysis Categorical variables are presented as counts and percentages, while continuous variables are expressed as mean ± standard deviation (SD). Categorical variables were compared between groups with the chi-square test, and Fisher’s exact test was adopted if the expected frequency in a 2 × 2 table was < 5. Continuous variables were analyzed using either the Student’s t-test or the Mann–Whitney U test for intergroup comparisons. Schoenfeld residual testing was employed to verify the proportional hazards assumption for each time‑to‑event outcome, and the assumption was satisfied for all biomarker variables. We used univariate and multivariate Cox proportional hazards regression models to evaluate the possible relationships between biomarker concentrations or risk scores and the 30‑day endpoints of the study. Biomarker variables were natural log-transformed before inclusion in the Cox regression models. Crude and adjusted hazard ratios (HRs) along with corresponding 95% confidence intervals (CIs) per 1-SD increment were calculated for each variable. The limited number of events in the present study constrained the number of variables included in the regression models. To adjust for multiple clinical characteristics in the Cox regression analysis, we adjusted for (i) GRACE score and three risk biomarkers (NT-proBNP, hs-CRP, and hs-cTnI at admission), (ii) TIMI risk score and the aforementioned three risk biomarkers, and (iii) clinical variables (age, male sex, frailty, anterior MI, Killip class > 1, and left ventricular ejection fraction [LVEF]). Kaplan–Meier cumulative event curves were generated to illustrate clinical outcomes based on SAA tertiles, with between-group comparisons performed using the log-rank test. We compared the area under the receiver operating characteristic (ROC) curves as a measure of discrimination accuracy to detect 30-day MACE across SAA levels, known risk scores, and biomarkers. To determine whether SAA improved risk prediction, it was added to traditional prognostic biomarkers and to each risk score within the three models described above. Model performance was assessed using the C-statistic, net reclassification indices (NRIs), and integrated discrimination improvement (IDI). We computed the 95% CIs for all variables included in the analysis. A P-value < 0.05 was considered statistically significant. Given the exploratory design of this study, adjustment for multiple testing was not conducted. We performed all statistical analyses with IBM SPSS 30.0 (SPSS Inc., Chicago, IL, USA) and R 4.5.1 (R Foundation for Statistical Computing, Vienna, Austria). Results Characteristics of the study participants The characteristics of older patients with STEMI (n = 327) and HCs (n = 327) are shown in Supplementary Table S2 . Significant between-group differences were observed in triglyceride, total cholesterol, high-density lipoprotein cholesterol, and glucose levels (P < 0.05). Of the 327 enrolled older patients with STEMI, 45 (13.76%) experienced HF events, and 24 (7.34%) developed CS and/or cardiac death events within 30 days. Among patients who developed HF events within 30 days after enrollment, the proportions of anterior MI (P = 0.005), Killip class > 1 (P < 0.001), and frailty (P < 0.001) were higher; the levels of risk scores (GRACE, TIMI, all P < 0.001) and risk biomarkers (NT-proBNP, hs-CRP, hs-cTnI, all P < 0.05) were significantly elevated; and the LVEF (P = 0.003) was lower ( Table 1 ) . Table 1 Baseline clinical characteristics according to 30-day HF events in older STEMI patients. 30-day HF events Variable All patient (n=327) Yes (n=45) No (n=282) P Value Demographic factors Age, years 66.85±5.03 66.76±5.66 66.87±4.93 0.545 Male, n (%) 270(82.57) 37(82.22) 233(82.62) 0.947 Current smoking, n (%) 201(61.47) 33(73.33) 168(59.57) 0.078 Frailty, n (%) 78(23.85) 30(66.67) 48(17.02) <0.001 Previous history Hypertension, n (%) 195(59.63) 28(62.22) 167(59.22) 0.703 Hyperlipidaemia, n (%) 170(51.99) 21(46.67) 149(52.84) 0.442 Diabetes mellitus, n (%) 105(32.11) 16(35.56) 89(31.56) 0.594 Myocardial infarction, n (%) 32(9.79) 8(17.78) 24(8.51) 0.052 Clinical characteristic Time from symptom onset to blood collection, h 6.68±3.46 7.69±3.82 6.52±3.37 0.051 Anterior MI, n (%) 134(40.98) 27(60.00) 107(37.94) 0.005 SBP, mmHg 120.18±16.49 116.00±19.34 120.85±15.93 0.144 Heart rate, bpm 74.54±12.74 78.83±15.88 73.93±12.09 0.081 Killip class>1, n (%) 74(22.63) 29(64.44) 45(15.96) <0.001 LVEF, % 53.44±8.47 49.60±11.87 54.06±7.64 0.003 Coronary Angiography 1-vessel disease, n (%) 106(32.42) 13(28.89) 93(32.98) 0.586 2-vessel disease, n (%) 105(32.11) 14(31.11) 91(32.27) 0.877 3-vessel disease, n (%) 116(35.47) 18(40.00) 98(34.75) 0.494 Risk score (point) GRACE 160.53±21.23 175.69±25.52 158.11±19.45 <0.001 TIMI 4.34±2.07 6.00±2.24 4.07±1.92 <0.001 Laboratory results (Admission) Triglyceride, mmol/L 1.69±1.14 1.74±0.92 1.68±1.17 0.295 Total cholesterol, mmol/L 4.43±1.06 4.70±1.08 4.39±1.05 0.142 HDL cholesterol, mmol/L 1.04±0.25 1.02±0.31 1.04±0.24 0.479 LDL cholesterol, mmol/L 2.77±0.87 2.93±0.89 2.75±0.87 0.328 Fasting Glucose, mmol/L 7.45±2.99 8.34±4.37 7.31±2.69 0.294 NT-proBNP, ng/L 941.35±977.73 1834.16±1448.75 798.88±795.22 <0.001 hs-CRP, mg/L 14.19±13.00 19.91±14.30 13.28±12.57 0.006 creatinine, μmol/L 82.69±49.36 84.88±30.45 82.34±51.78 0.353 hs-cTnI, ng/ml 3.64±5.49 8.09±8.63 2.93±4.43 <0.001 Medication Aspirin, n (%) 327(100.0) 45(100.0) 282(100.0) / Clopidogrel, n (%) 263(80.43) 36(80.0) 227(80.50) 0.938 Ticagrelor, n (%) 64(19.57) 9(20.0) 55(19.50) 0.938 Any DAPT, n (%) 327(100.0) 45(100.0) 282(100.0) / Statin, n (%) 325(99.39) 44(97.78) 281(99.65) 0.257 ACE inhibit or ARB, n (%) 248(75.84) 35(77.78) 213(75.53) 0.268 Beta-blockers, n (%) 302(92.35) 39(86.67) 263(93.26) 0.132 Data are presented as absolute number (percentage) or mean ± standard deviation. Bold indicates P <0.05. ACEI, angiotensin-converting enzyme inhibitor; ARB, angiotensin-receptor blocker; DAPT: Dual Antiplatelet Therapy; GRACE, global registry of acute coronary events; HDL, high-density lipoprotein; hs-CRP, high-sensitivity C-reactive protein; hs-cTnI, high-sensitivity cardiac troponin I; HF: heart failure; LDL, low-density lipoprotein; LVEF, left ventricular ejection fraction; MI, myocardial infarction; NT-proBNP: N-terminal pro-B-type natriuretic peptide; SBP, systolic blood pressure; STEMI: ST-segment elevation myocardial infarction; TIMI, thrombolysis in myocardial infarction. Expression of SAA in older patients with STEMI and HCs The serum level of SAA is significantly elevated in older patients with STEMI (735.94 ± 506.60) ng/mL than in HCs (427.58 ± 273.70) ng/mL (P < 0.001; Supplementary Figure S1 ) . Data are displayed in Fig. 1 , we compared the SAA expression levels among older patients with STEMI and found that those who experienced 30-day HF events ([1372.08 ± 869.66] ng/mL vs. [634.42 ± 322.75] ng/mL, P < 0.001), CS and/or cardiac death events ([1528.62 ± 1048.41] ng/mL vs. [673.15 ± 373.41] ng/mL, P < 0.001) had significantly higher SAA levels than those without adverse events. Survival analysis of SAA in older patients with STEMI To illustrate the association between serum SAA and the major adverse cardiovascular events, we conducted univariable and multivariable Cox regression analyses. As shown in Supplementary Table S3 , after adjusting for risk scores, prognostic biomarkers (NT-proBNP, hs-CRP, and hs-cTnI at admission), and clinical variables, patients in the higher binary category of SAA had an increased risk of HF events (adjusted model 1 HR: 2.833, 95% CI: 1.320–6.084, P = 0.008; adjusted model 2 HR: 2.735, 95% CI: 1.275–5.867, P = 0.010; adjusted model 3 HR: 2.903, 95% CI: 1.366–6.172, P = 0.006) and a higher risk of CS and/or cardiac death events (adjusted model 1 HR: 2.843, 95% CI: 1.020–7.922, P = 0.046; adjusted model 2 HR: 2.824, 95% CI: 1.019–7.821, P = 0.046; adjusted model 3 HR: 3.056, 95% CI: 1.098–8.505, P = 0.032). As a continuous variable, each 1-SD increase in SAA was associated with increased risk of HF events (unadjusted HR: 3.595, 95% CI: 2.384–5.420, P < 0.001; adjusted model 1 HR: 2.694, 95% CI: 1.754–4.136, P < 0.001; adjusted model 2 HR: 2.676, 95% CI: 1.736–4.126, P < 0.001; adjusted model 3 HR: 2.694, 95% CI: 1.777–4.086, P < 0.001) and CS and/or cardiac death events (unadjusted HR: 3.032, 95% CI: 1.774–5.183, P < 0.001; adjusted model 1 HR: 2.508, 95% CI: 1.413–4.452, P = 0.002; adjusted model 2 HR: 2.639, 95% CI: 1.465–4.754, P = 0.001; adjusted model 3 HR: 2.181, 95% CI: 1.282–3.713, P = 0.004). This association remained significant for SAA after adjusting for risk scores, prognostic biomarkers, and clinical variables (Fig. 2 ; Supplementary Table S3 ). Kaplan–Meier analysis showed that when SAA was grouped by tertiles, the incidence of the two outcomes increased progressively with an increase in group grades (HF events [0.93% vs. 13.51% vs. 26.85%, log-rank P < 0.001]; CS and/or cardiac death events [0.00% vs. 7.21% vs. 14.82%, log-rank P < 0.001) (Fig. 3 ) . Incremental prognostic value of SAA In the ROC curve analysis, SAA demonstrated the superior ability to discriminate adverse cardiovascular events compared with risk scores and prognostic biomarkers (Supplementary Figure S2) . Furthermore, SAA provided incremental information for predicting both endpoints when added to the combination of GRACE score and the three risk biomarkers (ΔC-statistic: 0.084, 95% CI: 0.048–0.119, P < 0.001; ΔC-statistic: 0.088, 95% CI: 0.169–0.169, P < 0.001; Table 2 ). Moreover, NRI and IDI analyses showed that adding SAA to known predictors improved event classification ( Table 2 ) . These results indicated that SAA added substantial discrimination/reclassification value over the established risk score, known biomarkers, and clinical variables. Table 2 Discrimination and reclassification improvement by SAA for risk prediction of clinical events. Discrimination Reclassification C-statistics (95%CI) △C-statistics (95%CI) P Value NRI (95%CI) P Value IDI (95%CI) P Value HF events Model 1 0.767(0.698–0.836) reference / reference / reference / Model 1 + SAA 0.851(0.804–0.898) 0.084(0.048–0.119) < 0.001 0.592(0.288–0.896) < 0.001 0.152(0.080–0.225) < 0.001 Model 2 0.783(0.721–0.844) reference / reference / reference / Model 2 + SAA 0.863(0.816–0.909) 0.080(0.046–0.114) < 0.001 0.688(0.392–0.984) < 0.001 0.163(0.092–0.235) < 0.001 Model 3 0.769(0.684–0.854) reference / reference / reference / Model 3 + SAA 0.872(0.825–0.918) 0.103(0.026–0.180) 0.009 0.707(0.408–1.07) < 0.001 0.152(0.081–0.222) < 0.001 CS and/or cardiac death Model 1 0.758(0.658–0.858) reference / reference / reference / Model 1 + SAA 0.846(0.777–0.915) 0.088(0.169–0.169) 0.033 0.603(0.202–1.005) 0.003 0.142(0.042–0.241) 0.005 Model 2 0.785(0.702–0.868) reference / reference / reference / Model 2 + SAA 0.858(0.790–0.925) 0.073(-0.005-0.151) 0.066 0.759(0.368–1.150) < 0.001 0.164(0.063–0.265) 0.002 Model 3 0.826(0.728–0.923) reference / reference / reference / Model 3 + SAA 0.892(0.840–0.944) 0.066(0.011–0.121) 0.019 0.447(0.038–0.856) 0.032 0.106(0.011–0.201) 0.029 The C-statistic indicates the difference compared to the “reference model.” Reference model 1 includes the GRACE score and three risk biomarkers (NT-proBNP, hs-CRP, and hs-cTnI at admission); model 2 includes the TIMI risk score and the same three risk biomarkers, model 3 includes clinical variables: age, male, frailty, anterior MI, Killip class > 1, and LVEF. Net reclassification improvement (NRI) was assessed using the continuous NRIs. Reclassification of patients who did or did not reach the clinical endpoint is shown. NRI: net reclassification improvement; IDI: integrated discrimination improvement Subgroup analysis According to the results presented in Fig. 4 , SAA showed reliable predictive performance for 30‑day HF events among all prespecified subgroups, and no significant interaction was found in the statistical testing (all P for interaction > 0.05). Notably, a differential predictive pattern emerged regarding frailty. In frail patients, the association remained non-significant (HR: 1.558, 95% CI: 0.913–2.657, P = 0.104), whereas SAA exhibited a stronger predictive capacity in non-frail patients with STEMI (HR: 5.477, 95% CI: 2.460–12.193, P < 0.001), and the interaction reached statistical significance (P for interaction = 0.019). Meanwhile, SAA levels in frail older patients with STEMI were (964.58 ± 680.23) ng/mL, significantly higher than those in non-frail patients (664.31 ± 414.85) ng/mL (P < 0.001; Supplementary Figure S3 ). The potential mechanism underlying this differential result may be associated with the widespread inflammaging in frail patients. Additionally, the poor prognosis of frail patients is driven by multiple factors, such as decreased reserve function of multiple organs and reduced tolerance to complications, leading to a relative decrease in the weight of inflammatory markers in the prognostic evaluation system. Discussion To investigate the predictive value of SAA for 30-day MACE, we enrolled older patients aged ≥ 60 years with STEMI who underwent primary PCI. Our findings demonstrated that SAA levels in older patients with STEMI were considerably higher than those in HCs. The elevated SAA levels were independently associated with an increased risk of HF, CS, and cardiac death within 30 days. Furthermore, the integration of SAA into conventional risk assessment systems markedly improved the predictive efficacy for adverse prognosis, with a more robust predictive value particularly observed in non-frail patients. This finding provides a novel biomarker basis for risk stratification and prognostic evaluation in older patients with STEMI, and holds important clinical significance (Central Illustration) . AMI triggers waves of circulating inflammatory cells, which are, in part, beneficial but harmful when in excess.[ 4 , 30 ] The first wave of inflammation occurs within 24 h of AMI onset, characterized primarily by the presence of polymorphonuclear neutrophils in the damaged myocardium, which is associated with an expanded infarct size and reduced LVEF.[ 31 ] Subsequently, a second wave of inflammatory response gradually develops, dominated by the recruitment of macrophages that appear to play an important role in clearing cellular debris and promoting myocardial healing.[ 4 ] Heart failure continues to be a serious and difficult complication among AMI patients, with its incidence varying between 7% and 38% based on different diagnostic standards.[ 32 , 33 ] This condition is closely linked to poorer short-term and long-term prognoses.[ 34 , 35 ] Following myocardial infarction, unfavorable left ventricular remodeling forms the structural basis of ischemic heart failure, involving complex short‑term and long‑term changes in ventricular size, shape, function, and cellular‑molecular characteristics.[ 36 , 37 ] Early-phase left ventricular remodeling is primarily attributed to cardiomyocyte apoptosis and necrosis, as well as myocardial ischemia–reperfusion injury induced by extensive infarction. This process leads to thinning and dilatation of the infarcted myocardial wall, during which the initial inflammatory response exerts a pivotal role. As an acute-phase inflammatory protein synthesized in the liver, SAA can rapidly increase in response to inflammatory stimuli. Its dynamic range is substantially greater than that of hs-CRP, and it has been shown to be a more sensitive inflammatory marker in non-cardiovascular inflammatory diseases such as infections and autoimmune diseases.[ 38 ] This study found that SAA levels in older patients with STEMI were considerably higher than those in HCs, and further elevated in patients who developed HF, CS, or cardiac death. This is consistent with the "early response" characteristic of SAA in inflammatory reactions—local inflammation triggered by plaque rupture can rapidly activate the systemic inflammatory cascade, leading to increased SAA synthesis. Higher SAA levels may reflect a more intense inflammatory response and a wider range of myocardial injury, thereby increasing the risk of adverse events. Previous studies have shown that hs-CRP is associated with cardiac events and left ventricular dysfunction in older patients with AMI.[ 14 , 39 – 41 ] However, using Cox regression analysis, we found that even after adjusting for risk scores, traditional prognostic markers, and clinical variables, SAA remained independently associated with 30-day HF events, CS, and/or cardiac death. These findings suggest that compared with hs-CRP, SAA is more capable of reflecting the inflammatory burden and prognostic outcomes in older patients with STEMI, which may be attributed to two factors. First, after the onset of STEMI, hs-CRP reaches its peak at 24–48 hours.[ 42 ] In contrast, SAA increases significantly within 4–6 hours of STEMI onset and exhibits an earlier peak time than hs-CRP, which enables more precise capture of the dynamic changes in the early inflammatory response.[ 38 ] Second, SAA is not only an inflammatory marker but may also be involved in the pathophysiology of AMI by promoting neutrophil chemotaxis and enhancing vascular endothelial injury.[ 43 ] The 2025 American College of Cardiology clinical practice guidelines[ 23 ] recommend the application of the GRACE 2.0 score or TIMI risk score for risk stratification in patients after myocardial infarction, which confirms the irreplaceable clinical value of these classical assessment tools. However, the guidelines also point out that the clinical efficacy of these risk scores in practical application and their impact on the long-term clinical outcomes of patients have not been fully investigated and verified. Meanwhile, its applicability in older patients remains limited. Older adults often have age-related comorbidities such as frailty and malnutrition, which are difficult to fully capture using traditional risk scores, resulting in the underestimation of some high-risk patients. Through ROC, C-statistic, NRI, and IDI analyses, we found that the discriminatory power of SAA was superior to that of traditional risk assessment tools (GRACE and TIMI), prognostic biomarkers, and clinical variable models such as age, frailty, and others. This finding indicates that SAA can serve as a complementary indicator to conventional risk assessment tools, helping clinicians more accurately identify high-risk populations among older patients with STEMI. Subgroup analysis revealed considerable heterogeneity in the predictive value of SAA for 30-day HF events among older patients with STEMI between the frail and non-frail subgroups. In non-frail patients, SAA levels were strongly correlated with the risk of HF, whereas in frail patients, this association was not statistically significant. The potential mechanism underlying this differential finding may be associated with the unique pathophysiological characteristics of frail patients. Frail populations generally exhibit a state of chronic low-grade inflammation associated with "inflammaging," and their baseline SAA levels may already be in the pathologically elevated range. This masks the acute-phase SAA elevation signal in AMI, thereby impairing its prognostic predictive efficacy.[ 44 , 45 ] Additionally, the poor prognosis of frail patients is driven by multiple factors, such as decreased reserve function of multiple organs and reduced tolerance to complications, leading to a relatively decreased weight of inflammatory markers in the prognostic evaluation system. This observation is consistent with those of previous studies on the cardiovascular prognosis of frail patients.[ 46 ] The results of the present study carry notable value for clinical practice. In non-frail older patients with STEMI, SAA may serve as a preferentially recommended prognostic biomarker to inform the development of individualized treatment strategies. However, in frail patients, relying solely on SAA levels is insufficient for accurate prognostic assessment. Instead, a comprehensive risk evaluation system should be established by integrating standardized frailty assessment tools,[ 47 ] such as FI, Clinical Frailty Scale, and the Frailty Assessment Scale for Heart Failure. Future studies may further explore the combined predictive model of "inflammatory markers + frailty assessment" to improve the accuracy of risk stratification for frail older patients with STEMI, thereby providing evidence-based support for optimizing clinical management strategies. Study limitations Our study has some limitations. First, although this study adopted a single‑center design, the adequate sample size provided sufficient statistical power for the primary composite endpoint analysis. However, the analysis of the exploratory frailty subgroup may have been limited by insufficient statistical power. In the future, we will expand the sample size to conduct a study on frailty assessment in patients with MI. Second, only the admission SAA level was measured; dynamic monitoring of SAA trends at different time points after AMI (e.g., 24 h and 72 h post-PCI) was not performed. However, given the characteristics of SAA—its early rise and rapid recovery—we believe the SAA level at admission should be the optimal time point for prediction. Finally, this study enrolled only patients with STEMI who underwent primary PCI, and did not include patients with non-ST-segment elevation myocardial infarction (NSTEMI). The primary rationale for this selection was that our study focused on early serum SAA alterations in AMI. In this registry analysis, however, the mean time from symptom onset to blood collection was 48 hours for NSTEMI patients.[ 48 ] Moreover, left ventricular remodeling and inflammation constitute the main pathophysiological mechanisms underlying adverse cardiovascular events in NSTEMI. We therefore hypothesize that SAA possesses comparable prognostic value in NSTEMI, although further validation is required. Conclusions SAA can independently predict 30‑day adverse cardiovascular events in older STEMI patients and serves as a valuable prognostic biomarker, particularly in non‑frail individuals. Abbreviations AMI Acute myocardial infarction MI myocardial infarction STEMI ST-segment elevation MI NSTEMI non-ST-segment elevation myocardial infarction HCs healthy controls PCI percutaneous coronary intervention SAA Serum amyloid A hs-CRP High-sensitivity C-reactive protein hs-cTnI High-sensitivity troponin I NT-proBNP N-terminal pro-B-type natriuretic peptide MACE major adverse cardiovascular events HF heart failure CS cardiogenic shock SD standard deviation HR hazard ratio Cis confidence intervals ROC receiver operating characteristic NRIs net reclassification indices IDI integrated discrimination improvement ECG electrocardiographic GRACE The Global Registry of Acute Coronary Events TIMI Thrombolysis in Myocardial Infarction FI frailty index LVEF left ventricular ejection fraction Declarations Ethics approval and consent to participate The study protocol was approved by the Ethics Committee of Henan Provincial People’s Hospital (Ethical Approval No. 15 of 2022) and was conducted in accordance with the principles of the Declaration of Helsinki. Written informed consent was obtained from all participants. Consent for publication Not applicable. Competing interests The authors declare no competing interests. Funding This work was supported by grants from the Natural Science Foundation of Henan Province (252300421607) and the Medical Science and Technology Research Initiative of the Henan Provincial Health Commission (LHGJ20240076). Author Contribution XY contributed to study conceptualization, data analysis, and manuscript drafting. XW and CC participated in data collection, analysis, and result interpretation. XX, XC, and YW assisted with statistical analysis and findings interpretation. XX and XC revised the manuscript. PQ and HD offered critical guidance throughout the study, supervised the project, and revised the final manuscript. All authors read and approved the final version. Acknowledgement We are particularly grateful to all of the patients with acute myocardial infarction and volunteers who participated in the present study. Data Availability The datasets used and analyzed during the current study are available fromthe corresponding author on reasonable request. References Anderson JL, Morrow DA. Acute Myocardial Infarction. N Engl J Med. 2017;376(21):2053–64. Imbesi A, Greco A, Spagnolo M, Laudani C, Raffo C, Finocchiaro S, Mazzone PM, Landolina D, Mauro MS, Cutore L, et al. Targeting Inflammation After Acute Myocardial Infarction. J Am Coll Cardiol. 2025;86(15):1146–69. Bhatt DL, Lopes RD, Harrington RA. Diagnosis and Treatment of Acute Coronary Syndromes. JAMA. 2022;327(7):662–75. Matter MA, Paneni F, Libby P, Frantz S, Stähli BE, Templin C, Mengozzi A, Wang Y-J, Kündig TM, Räber L, et al. Inflammation in acute myocardial infarction: the good, the bad and the ugly. Eur Heart J. 2024;45(2):89–103. 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Wilhelmsen L, Welin L, Svärdsudd K, Wedel H, Eriksson H, Hansson PO, Rosengren A. Secular changes in cardiovascular risk factors and attack rate of myocardial infarction among men aged 50 in Gothenburg, Sweden. Accurate prediction using risk models. J Intern Med. 2008;263(6):636–43. Rosengren A. Better treatment and improved prognosis in elderly patients with AMI: but do registers tell the whole truth? Eur Heart J. 2012;33(5):562–3. Ng LL, Sandhu JK, Narayan H, Quinn PA, Squire IB, Davies JE, Struck J, Bergmann A, Maisel A, Jones DJ. Pro-substance p for evaluation of risk in acute myocardial infarction. J Am Coll Cardiol. 2014;64(16):1698–707. Cung TT, Morel O, Cayla G, Rioufol G, Garcia-Dorado D, Angoulvant D, Bonnefoy-Cudraz E, Guérin P, Elbaz M, Delarche N, et al. Cyclosporine before PCI in Patients with Acute Myocardial Infarction. N Engl J Med. 2015;373(11):1021–31. Salini V, Saggini A, Maccauro G, Caraffa A, Shaik-Dasthagirisaheb YB, Conti P. Inflammatory Markers: Serum Amyloid A, Fibrinogen and C-Reactive Protein — A Revisited Study. EUR J INFLAMM. 2011;9(2):95–102. Seferović PM, Ašanin M, Ristić AD. Acute stress disorder and C-reactive protein in patients with acute myocardial infarction. Eur J Prev Cardiol. 2018;25(7):702–5. Reindl M, Reinstadler SJ, Feistritzer H-J, Klug G, Tiller C, Mair J, Mayr A, Jaschke W, Metzler B. Relation of inflammatory markers with myocardial and microvascular injury in patients with reperfused ST-elevation myocardial infarction. Eur Heart J Acute Cardiovasc Care. 2016;6(7):640–9. Park JJ, Yoon M, Cho H-W, Cho H-J, Kim KH, Yang DH, Yoo B-S, Kang S-M, Baek SH, Jeon E-S, et al. C-reactive protein and statins in heart failure with reduced and preserved ejection fraction. Front Cardiovasc Med. 2022;9:1064967. Daios S, Anastasiou V, Moysidis DV, Didagelos M, Papazoglou AS, Gogos C, Stalikas N, Alexiadis E, Theodoropoulos KC, Ztriva E, et al. The Prognostic Role of C-Reactive Protein Velocity in Patients with First Acute Myocardial Infarction. J Clin Med. 2025;14(21):7633. Wang X, Chai H, Wang Z, Lin PH, Yao Q, Chen C. Serum amyloid A induces endothelial dysfunction in porcine coronary arteries and human coronary artery endothelial cells. Am J Physiol Heart Circ Physiol. 2008;295(6):H2399–408. Li X, Li C, Zhang W, Wang Y, Qian P, Huang H. Inflammation and aging: signaling pathways and intervention therapies. Signal Transduct Target Ther. 2023;8(1):239. Ferrucci L, Fabbri E. Inflammageing: chronic inflammation in ageing, cardiovascular disease, and frailty. Nat Rev Cardiol. 2018;15(9):505–22. Rosano GMC, Spoletini I, Vitale C. Frailty in Heart Failure: Implications for Management. Card Fail Rev. 2018;4(2):104–6. Chao Y-C, Liu C-Y, Hung H-F, Lee C-M, Hsu S-P, Chiou A-F. Frailty Assessment Scale for Heart Failure. J Cardiovasc Nurs. 2024:1–13. Yang X, Du X, Ma K, Li G, Liu Z, Rong W, Miao H, Zhu F, Cui Q, Wu S, et al. Circulating miRNAs Related to Long-term Adverse Cardiovascular Events in STEMI Patients: A Nested Case-Control Study. Can J Cardiol. 2021;37(1):77–85. Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterial.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 07 Apr, 2026 Editor assigned by journal 06 Apr, 2026 Editor invited by journal 12 Mar, 2026 Submission checks completed at journal 12 Mar, 2026 First submitted to journal 12 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-9015088","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":621638692,"identity":"41f34bec-f684-4fd9-a913-4d37f687af6e","order_by":0,"name":"Xinying Yang","email":"","orcid":"","institution":"Henan Provincial People’s Hospital, People’s Hospital of Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Xinying","middleName":"","lastName":"Yang","suffix":""},{"id":621638701,"identity":"edeb16fc-1331-4fe0-97d3-cc4345a5cdbb","order_by":1,"name":"Xinhui Wang","email":"","orcid":"","institution":"Henan Provincial People’s Hospital, People’s Hospital of Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Xinhui","middleName":"","lastName":"Wang","suffix":""},{"id":621638704,"identity":"8f6f33cc-3e61-4c67-b0d8-08f00d712751","order_by":2,"name":"Chen Chen","email":"","orcid":"","institution":"Henan Provincial People’s Hospital, People’s Hospital of Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Chen","middleName":"","lastName":"Chen","suffix":""},{"id":621638705,"identity":"e9cc9776-b2c9-4c3b-ae1e-ad5ca1820971","order_by":3,"name":"Xianjing Xu","email":"","orcid":"","institution":"Henan Provincial People’s Hospital, People’s Hospital of Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Xianjing","middleName":"","lastName":"Xu","suffix":""},{"id":621638708,"identity":"3a24780b-ac62-4b54-9168-a3ddcfb38391","order_by":4,"name":"Xuanchao Cao","email":"","orcid":"","institution":"Henan Provincial People’s Hospital, People’s Hospital of Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Xuanchao","middleName":"","lastName":"Cao","suffix":""},{"id":621638710,"identity":"031e27a1-5eeb-4304-b319-5d500284e223","order_by":5,"name":"Yunfei Wang","email":"","orcid":"","institution":"Henan Provincial People’s Hospital, People’s Hospital of Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Yunfei","middleName":"","lastName":"Wang","suffix":""},{"id":621638714,"identity":"669ffda9-b094-46b2-a340-66956a22741c","order_by":6,"name":"Peng Qian","email":"","orcid":"","institution":"Henan Provincial People’s Hospital, People’s Hospital of Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Peng","middleName":"","lastName":"Qian","suffix":""},{"id":621638715,"identity":"427506ff-3322-4379-81dd-3930077c18e7","order_by":7,"name":"Hongyan Duan","email":"data:image/png;base64,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","orcid":"","institution":"Henan Provincial People’s Hospital, People’s Hospital of Zhengzhou University","correspondingAuthor":true,"prefix":"","firstName":"Hongyan","middleName":"","lastName":"Duan","suffix":""}],"badges":[],"createdAt":"2026-03-03 02:54:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9015088/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9015088/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106871130,"identity":"87cd83ed-1b84-47f1-84e6-3fd949ab18dd","added_by":"auto","created_at":"2026-04-14 09:44:26","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":120137,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDistribution of SAA levels in the older STEMI patients.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSAA levels are shown for patients in the older STEMI patients (n=327) who experienced (A) HF events or (B) CS and/or cardiac death within 30 days after STEMI. Values indicate those who did (red) or did not (blue) experience MACE within 30 days of STEMI.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-9015088/v1/d36fcb68eb19960102388baa.png"},{"id":106871129,"identity":"a1bf0d83-091a-4978-b69c-60806f61c925","added_by":"auto","created_at":"2026-04-14 09:44:26","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":284631,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHazard ratios for adverse clinical events in the SAA cohort.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUnadjusted and adjusted hazard ratios (HRs) obtained by Cox proportional hazards regression analysis are shown for patients in the SAA cohort (n=327) who experienced (A) HF events or (B) CS and/or cardiac death within 30 days after STEMI. SAA levels were measured at baseline. Model 1 was adjusted for GRACE scoreand three risk biomarkers (NT-proBNP, hs-CRP, and hs-cTnI at admission); model 2 was adjusted for TIMI risk score and three same three risk biomarkers; Model 3 was adjusted for clinical variables included age, male, frailty, anterior MI, Killip class\u0026gt;1, and LVEF.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-9015088/v1/38577d95c5c5a7874fbd1aa0.png"},{"id":106871154,"identity":"593efb89-8940-4d8a-9b64-2c644a42291e","added_by":"auto","created_at":"2026-04-14 09:44:33","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":167474,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIncidence of adverse clinical events compared to the levels of SAA.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKaplan-Meier curves illustrating the timing of HF events (A) and CS and/or cardiac death (B) in the tertile of SAA levels in the elderly STEMI patients (n=327).\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-9015088/v1/33b215cc5df97962d6ae5ad4.png"},{"id":106870983,"identity":"ca6bb1cf-0ea0-4d23-8f84-32b554c81b64","added_by":"auto","created_at":"2026-04-14 09:44:11","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":133726,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSubgroup analysis of the prognostic values of the SAA level.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePlots show adjusted hazard ratios (HRs) per-1SD for SAA levels as prognostic biomarker for HF events in different patient subgroups in elderly STEMI patients (n=327). HRs are adjusted for GRACE and three risk biomarkers (NT-proBNP, hs-CRP, and hs-cTnI at admission).\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-9015088/v1/edb96b76b7e00cc717510ab5.png"},{"id":106960884,"identity":"2938dadf-8290-4fb7-8035-6fc170f6c2f3","added_by":"auto","created_at":"2026-04-15 09:23:30","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":210964,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version.\u003c/p\u003e","description":"","filename":"image5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9015088/v1/89ca7af5f45984b12546779f.jpeg"},{"id":106963144,"identity":"dd7b98f8-5435-445f-8a10-f5b4431ccfa2","added_by":"auto","created_at":"2026-04-15 09:42:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2205453,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9015088/v1/ac628a0e-cb6b-440c-aad4-028bdf4a4a9b.pdf"},{"id":106870984,"identity":"fe1571ee-35dd-43e7-82e3-df01542aea22","added_by":"auto","created_at":"2026-04-14 09:44:12","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":278173,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-9015088/v1/138fb9dcd1f77d5958433ac9.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Relationship Between Serum Amyloid A Protein and Short-Term Adverse Cardiovascular Outcomes in Older Patients with STEMI: A Prospective Study","fulltext":[{"header":"Background","content":"\u003cp\u003eAcute myocardial infarction (AMI) is a prevalent cardiac emergency associated with considerable risks of morbidity and mortality.[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] Although considerable advances have been made in reperfusion strategies, pharmacologic interventions and the management of modifiable risk factors, adverse cardiovascular events persist following AMI.[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] The factors determining this residual risk\u0026mdash;defined as the risk that persists despite optimal control of known modifiable factors\u0026mdash;are still uncertain. Among these determinants, age-related conditions and inflammation play a complex yet increasingly recognized role.[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eThe global population structure has undergone substantial changes, with a rapid increase in both the size and proportion of the older population.[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] The aging process in China has garnered exceptional attention worldwide. The population aged 60 years and over is predicted to nearly double, increasing from 168\u0026nbsp;million (12.4%) in 2010 to 402\u0026nbsp;million (28%) by 2040.[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] Aging is a high-risk factor for increased mortality and complication rates after percutaneous coronary intervention (PCI),[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] yet evidence-based risk assessment tools for the older population remain lacking. Notably, age-related comorbidities such as frailty and sarcopenia[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] not only exacerbate systemic inflammation but also create therapeutic dilemmas in clinical practice, thereby impairing treatment efficacy.[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eAMI is primarily caused by the rupture of coronary atherosclerotic plaques, which triggers thrombosis that rapidly occludes the coronary lumen, leading to persistent myocardial ischemia, hypoxia, and necrosis.[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] Inflammatory response exerts a pivotal regulatory role in the pathological progression of AMI.[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] Myocardial ischemic injury can induce a substantial release of inflammatory biomarkers. These markers are not only involved in the infarct repair of myocardial tissue but may also lead to secondary damage in the ischemic myocardial region.[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] High-sensitivity C-reactive protein (hs-CRP), and other relevant indicators are pivotal clinical inflammatory markers in this context and have been thoroughly explored.[\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] Notably, increased hs-CRP levels are linked to more severe myocardial and microvascular injury and subsequent clinical outcomes.[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] In the EPIC-NORFOLK cohort, which followed a large primary prevention population for 20 years, hs-CRP was independently associated with major adverse cardiovascular events.[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] Lin et al. demonstrated that hs-CRP is a prognostic biomarker for poor outcomes in older patients with AMI, yet the association between hs-CRP and frailty was not analyzed in their study.[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eDuring in vivo inflammation, hepatic synthesis of serum amyloid A protein (SAA) and hs-CRP is upregulated by inflammatory cytokines.[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] Both markers were significantly and independently associated with subsequent major adverse cardiovascular events, including acute myocardial infarction, congestive heart failure, and stroke. Notably, however, only SAA showed an independent but moderate association with angiographically confirmed coronary artery disease.[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] Johnson et al. observed that SAA may be complementary to hs-CRP.[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] Furthermore, SAA may serve as a superior marker of disease activity, featuring a broader dynamic range and faster response, and it denotes a distinct type of acute-phase response compared to hs-CRP.[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] However, evidence remains remarkably limited concerning the prognostic significance of serum SAA for outcomes in older patients with AMI.\u003c/p\u003e \u003cp\u003eThis study specifically fills this critical knowledge gap by assessing SAA\u0026rsquo;s predictive ability for 30-day follow-up major cardiovascular events (MACE) in this high-risk population.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and participants\u003c/h2\u003e \u003cp\u003eWe conducted a prospective cohort study enrolling 327 patients aged\u0026thinsp;\u0026ge;\u0026thinsp;60 years with ST-segment elevation myocardial infarction (STEMI) from the Acute Coronary Syndrome Genetics and Biomarkers Registry Study (ARSGB-ACS) (ClinicalTrials.gov identifier: NCT03752515). All patients were admitted within 12 h of chest pain onset and underwent primary PCI at Henan Provincial People\u0026rsquo;s Hospital from March 4, 2021, to July 16, 2022. Primary PCI and adjunctive pharmacological treatment were performed according to contemporary guidelines.[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] Patients presenting with STEMI combined with cardiogenic shock (CS), as well as those receiving rescue PCI following unsuccessful thrombolysis, were excluded from the study.[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] Additional exclusion criteria included a history of malignancy, cardiomyopathy, valvular heart disease, infectious diseases, renal insufficiency, or systemic inflammatory disease, as these comorbidities may substantially alter SAA levels.[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eHealthy control subjects (HCs) were free of cardiovascular disease, as confirmed by detailed medical history, thorough physical examination, ECG, and echocardiography.\u003c/p\u003e \u003cp\u003e This study was approved by the Ethics Committee of Henan Provincial People\u0026rsquo;s Hospital. All procedures were conducted in accordance with the Declaration of Helsinki. All participants provided written informed consent, and this study was conducted in accordance with the STROBE checklist (\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eSupplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMeasurement of SAA and other biomarkers\u003c/h3\u003e\n\u003cp\u003eVenous blood samples were collected into anticoagulant-free serum collection tubes, and the samples were immediately centrifuged at 2000 \u0026times;g for 10 minutes within one hour after collection. All serum specimens were aliquoted and preserved at \u0026minus;\u0026thinsp;80\u0026deg;C in a low-temperature refrigerator, and the SAA concentrations were determined in a single blind batch assay subsequently. Serum SAA concentrations were determined using a commercial enzyme-linked immunosorbent assay kit (R\u0026amp;D Systems Europe, Abingdon, OX, UK). N-terminal pro-B-type natriuretic peptide (NT-proBNP) levels were measured using the Vitros 5600 NT-proBNP ECI assay (Ortho Clinical Diagnostics). High-sensitivity troponin I (hs-cTnI) levels were determined by chemiluminescence assay. hs-CRP levels were measured using a turbidimetric inhibition immunoassay. All biomarker assessments were conducted by investigators blinded to the patients\u0026rsquo; clinical features and endpoints.\u003c/p\u003e\n\u003ch3\u003eCalculation of risk score\u003c/h3\u003e\n\u003cp\u003eThe Global Registry of Acute Coronary Events (GRACE) score[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] and the Thrombolysis in Myocardial Infarction (TIMI) risk score,[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] which were used to calculate the risk of in-hospital mortality or nonfatal myocardial infarction, are widely used prediction tools that have already been reported elsewhere.\u003c/p\u003e\n\u003ch3\u003eFrailty assessment\u003c/h3\u003e\n\u003cp\u003eBased on previous literature, we selected 28 clinical variables and constructed a frailty index (FI) using a deficit accumulation model.[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] The indicators included in the frailty index (FI) mainly involve diseases, comorbidities, clinical symptoms, physical signs, and laboratory test results. Each selected variable must be associated with aging, predict adverse clinical outcomes, represent a range of organ systems when used together, and not be common to all individuals at a relatively early stage. Binary variables such as diabetes were recorded as 0 for none and 1 for existence. Continuous variables were categorized into binary scores as deemed appropriate. All subjects were assigned a score ranging from 0 to 28. The FI was calculated as the frailty score divided by 28, ranging from 0 to 1. While deficit accumulation\u0026ndash;based frailty exists on a continuous spectrum, earlier studies divided subjects into two categories: those with FI\u0026thinsp;\u0026ge;\u0026thinsp;0.25 (frailty score\u0026thinsp;\u0026ge;\u0026thinsp;7) were classified as frail, and those with FI\u0026thinsp;\u0026lt;\u0026thinsp;0.25 (frailty score\u0026thinsp;\u0026lt;\u0026thinsp;7) as non‑frail.\u003c/p\u003e\n\u003ch3\u003eStudy endpoints\u003c/h3\u003e\n\u003cp\u003eParticipants who experienced cardiac death, new-onset heart failure (HF), or CS within the 30-day follow-up were considered to have developed MACE. All patients included in the study had complete follow-up data. New-onset HF[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] was diagnosed if all of the following criteria were met: (i) normal heart function at admission (Killip class I) and no history of HF, (ii) typical symptoms and signs of HF, (iii) NT-proBNP concentration\u0026thinsp;\u0026gt;\u0026thinsp;900 ng/L, and (iv) treatment with intravenous diuretics. CS[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] was defined as systolic blood pressure\u0026thinsp;\u0026le;\u0026thinsp;90 mmHg for \u0026gt;\u0026thinsp;30 min after exclusion of hypovolemia, with clinical evidence of hypoperfusion, inotrope dependence, or mechanical left ventricular support to correct this situation. Each event was adjudicated by two independent, blinded physician adjudicators. If any discrepancies arose during evaluation, the reviewers discussed and re-evaluated their assignments, and sought a third blinded reviewer for further advice.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eCategorical variables are presented as counts and percentages, while continuous variables are expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD). Categorical variables were compared between groups with the chi-square test, and Fisher\u0026rsquo;s exact test was adopted if the expected frequency in a 2 \u0026times; 2 table was \u0026lt;\u0026thinsp;5. Continuous variables were analyzed using either the Student\u0026rsquo;s t-test or the Mann\u0026ndash;Whitney U test for intergroup comparisons. Schoenfeld residual testing was employed to verify the proportional hazards assumption for each time‑to‑event outcome, and the assumption was satisfied for all biomarker variables. We used univariate and multivariate Cox proportional hazards regression models to evaluate the possible relationships between biomarker concentrations or risk scores and the 30‑day endpoints of the study. Biomarker variables were natural log-transformed before inclusion in the Cox regression models. Crude and adjusted hazard ratios (HRs) along with corresponding 95% confidence intervals (CIs) per 1-SD increment were calculated for each variable. The limited number of events in the present study constrained the number of variables included in the regression models. To adjust for multiple clinical characteristics in the Cox regression analysis, we adjusted for (i) GRACE score and three risk biomarkers (NT-proBNP, hs-CRP, and hs-cTnI at admission), (ii) TIMI risk score and the aforementioned three risk biomarkers, and (iii) clinical variables (age, male sex, frailty, anterior MI, Killip class\u0026thinsp;\u0026gt;\u0026thinsp;1, and left ventricular ejection fraction [LVEF]). Kaplan\u0026ndash;Meier cumulative event curves were generated to illustrate clinical outcomes based on SAA tertiles, with between-group comparisons performed using the log-rank test. We compared the area under the receiver operating characteristic (ROC) curves as a measure of discrimination accuracy to detect 30-day MACE across SAA levels, known risk scores, and biomarkers. To determine whether SAA improved risk prediction, it was added to traditional prognostic biomarkers and to each risk score within the three models described above. Model performance was assessed using the C-statistic, net reclassification indices (NRIs), and integrated discrimination improvement (IDI). We computed the 95% CIs for all variables included in the analysis. A P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant. Given the exploratory design of this study, adjustment for multiple testing was not conducted. We performed all statistical analyses with IBM SPSS 30.0 (SPSS Inc., Chicago, IL, USA) and R 4.5.1 (R Foundation for Statistical Computing, Vienna, Austria).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n\u003ch2\u003eCharacteristics of the study participants\u003c/h2\u003e\n\u003cp\u003eThe characteristics of older patients with STEMI (n\u0026thinsp;=\u0026thinsp;327) and HCs (n\u0026thinsp;=\u0026thinsp;327) are shown in \u003cspan class=\"Underline\"\u003eSupplementary Table S2\u003c/span\u003e. Significant between-group differences were observed in triglyceride, total cholesterol, high-density lipoprotein cholesterol, and glucose levels (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\n\u003cp\u003eOf the 327 enrolled older patients with STEMI, 45 (13.76%) experienced HF events, and 24 (7.34%) developed CS and/or cardiac death events within 30 days.\u003c/p\u003e\n\u003cp\u003eAmong patients who developed HF events within 30 days after enrollment, the proportions of anterior MI (P\u0026thinsp;=\u0026thinsp;0.005), Killip class\u0026thinsp;\u0026gt;\u0026thinsp;1 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and frailty (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were higher; the levels of risk scores (GRACE, TIMI, all P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and risk biomarkers (NT-proBNP, hs-CRP, hs-cTnI, all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were significantly elevated; and the LVEF (P\u0026thinsp;=\u0026thinsp;0.003) was lower \u003cspan class=\"Underline\"\u003e(\u003c/span\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cspan class=\"Underline\"\u003e)\u003c/span\u003e.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eBaseline clinical characteristics according to 30-day HF events in older STEMI patients.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"225\"\u003e\n\u003cp\u003e\u003cstrong\u003e \u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"105\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"227\"\u003e\n\u003cp\u003e\u003cstrong\u003e30-day HF events\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"225\"\u003e\n\u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"105\"\u003e\n\u003cp\u003e\u003cstrong\u003eAll patient\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(n=327)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003e\u003cstrong\u003eYes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(n=45)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u003cstrong\u003eNo\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(n=282)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e\u003cstrong\u003eP Value\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"225\"\u003e\n\u003cp\u003e\u003cstrong\u003eDemographic factors\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"105\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"225\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u0026nbsp; Age, years\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"105\"\u003e\n\u003cp\u003e66.85\u0026plusmn;5.03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003e66.76\u0026plusmn;5.66\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e66.87\u0026plusmn;4.93\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e0.545\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"225\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u0026nbsp; Male, n (%)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"105\"\u003e\n\u003cp\u003e270(82.57)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003e37(82.22)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e233(82.62)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e0.947\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"225\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u0026nbsp; Current smoking, n (%)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"105\"\u003e\n\u003cp\u003e201(61.47)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003e33(73.33)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e168(59.57)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e0.078\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"225\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u0026nbsp; Frailty, n (%)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"105\"\u003e\n\u003cp\u003e78(23.85)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003e30(66.67)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e48(17.02)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"225\"\u003e\n\u003cp\u003e\u003cstrong\u003ePrevious history\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"105\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"225\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u0026nbsp; Hypertension, n (%)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"105\"\u003e\n\u003cp\u003e195(59.63)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003e28(62.22)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e167(59.22)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e0.703\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"225\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u0026nbsp; Hyperlipidaemia, n (%)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"105\"\u003e\n\u003cp\u003e170(51.99)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003e21(46.67)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e149(52.84)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e0.442\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"225\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u0026nbsp; Diabetes mellitus, n (%)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"105\"\u003e\n\u003cp\u003e105(32.11)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003e16(35.56)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e89(31.56)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e0.594\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"225\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u0026nbsp; Myocardial infarction, n (%)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"105\"\u003e\n\u003cp\u003e32(9.79)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003e8(17.78)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e24(8.51)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e0.052\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"225\"\u003e\n\u003cp\u003e\u003cstrong\u003eClinical characteristic\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"105\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"225\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp;Time from symptom onset to blood collection, h\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"105\"\u003e\n\u003cp\u003e6.68\u0026plusmn;3.46\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003e7.69\u0026plusmn;3.82\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e6.52\u0026plusmn;3.37\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e0.051\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"225\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u0026nbsp; Anterior MI, n (%)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"105\"\u003e\n\u003cp\u003e134(40.98)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003e27(60.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e107(37.94)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.005\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"225\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u0026nbsp; SBP, mmHg\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"105\"\u003e\n\u003cp\u003e120.18\u0026plusmn;16.49\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003e116.00\u0026plusmn;19.34\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e120.85\u0026plusmn;15.93\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e0.144\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"225\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u0026nbsp; Heart rate, bpm\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"105\"\u003e\n\u003cp\u003e74.54\u0026plusmn;12.74\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003e78.83\u0026plusmn;15.88\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e73.93\u0026plusmn;12.09\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e0.081\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"225\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u0026nbsp; Killip class\u0026gt;1, n (%)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"105\"\u003e\n\u003cp\u003e74(22.63)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003e29(64.44)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e45(15.96)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"225\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u0026nbsp; LVEF, %\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"105\"\u003e\n\u003cp\u003e53.44\u0026plusmn;8.47\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003e49.60\u0026plusmn;11.87\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e54.06\u0026plusmn;7.64\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.003\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"225\"\u003e\n\u003cp\u003e\u003cstrong\u003eCoronary Angiography\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"105\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"225\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u0026nbsp; 1-vessel disease, n (%)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"105\"\u003e\n\u003cp\u003e106(32.42)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003e13(28.89)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e93(32.98)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e0.586\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"225\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u0026nbsp; 2-vessel disease, n (%)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"105\"\u003e\n\u003cp\u003e105(32.11)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003e14(31.11)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e91(32.27)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e0.877\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"225\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u0026nbsp; 3-vessel disease, n (%)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"105\"\u003e\n\u003cp\u003e116(35.47)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003e18(40.00)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e98(34.75)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e0.494\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"225\"\u003e\n\u003cp\u003e\u003cstrong\u003eRisk score (point)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"105\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"225\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp; GRACE\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"105\"\u003e\n\u003cp\u003e160.53\u0026plusmn;21.23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003e175.69\u0026plusmn;25.52\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e158.11\u0026plusmn;19.45\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"225\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp; TIMI\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"105\"\u003e\n\u003cp\u003e4.34\u0026plusmn;2.07\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003e6.00\u0026plusmn;2.24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e4.07\u0026plusmn;1.92\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"225\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;Laboratory results (Admission)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"105\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"225\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u0026nbsp; Triglyceride, mmol/L\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"105\"\u003e\n\u003cp\u003e1.69\u0026plusmn;1.14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003e1.74\u0026plusmn;0.92\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e1.68\u0026plusmn;1.17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e0.295\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"225\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u0026nbsp; Total cholesterol, mmol/L\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"105\"\u003e\n\u003cp\u003e4.43\u0026plusmn;1.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003e4.70\u0026plusmn;1.08\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e4.39\u0026plusmn;1.05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e0.142\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"225\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u0026nbsp; HDL cholesterol, mmol/L\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"105\"\u003e\n\u003cp\u003e1.04\u0026plusmn;0.25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003e1.02\u0026plusmn;0.31\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e1.04\u0026plusmn;0.24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e0.479\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"225\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u0026nbsp; LDL cholesterol, mmol/L\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"105\"\u003e\n\u003cp\u003e2.77\u0026plusmn;0.87\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003e2.93\u0026plusmn;0.89\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e2.75\u0026plusmn;0.87\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e0.328\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"225\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u0026nbsp; Fasting Glucose, mmol/L\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"105\"\u003e\n\u003cp\u003e7.45\u0026plusmn;2.99\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003e8.34\u0026plusmn;4.37\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e7.31\u0026plusmn;2.69\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e0.294\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"225\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u0026nbsp; NT-proBNP, ng/L\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"105\"\u003e\n\u003cp\u003e941.35\u0026plusmn;977.73\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003e1834.16\u0026plusmn;1448.75\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e798.88\u0026plusmn;795.22\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"225\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u0026nbsp; hs-CRP, mg/L\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"105\"\u003e\n\u003cp\u003e14.19\u0026plusmn;13.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003e19.91\u0026plusmn;14.30\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e13.28\u0026plusmn;12.57\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.006\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"225\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u0026nbsp; creatinine, \u0026mu;mol/L\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"105\"\u003e\n\u003cp\u003e82.69\u0026plusmn;49.36\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003e84.88\u0026plusmn;30.45\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e82.34\u0026plusmn;51.78\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e0.353\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"225\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u0026nbsp; hs-cTnI, ng/ml\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"105\"\u003e\n\u003cp\u003e3.64\u0026plusmn;5.49\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003e8.09\u0026plusmn;8.63\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e2.93\u0026plusmn;4.43\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"225\"\u003e\n\u003cp\u003e\u003cstrong\u003eMedication\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"105\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"225\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp; Aspirin, n (%)\u0026nbsp; \u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"105\"\u003e\n\u003cp\u003e327(100.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003e45(100.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e282(100.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e/\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"225\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp; Clopidogrel, n (%)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"105\"\u003e\n\u003cp\u003e263(80.43)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003e36(80.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e227(80.50)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e0.938\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"225\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp; Ticagrelor, n (%) \u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"105\"\u003e\n\u003cp\u003e64(19.57)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003e9(20.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e55(19.50)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e0.938\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"225\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp; Any DAPT, n (%)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"105\"\u003e\n\u003cp\u003e327(100.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003e45(100.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e282(100.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e/\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"225\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp; Statin, n (%)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"105\"\u003e\n\u003cp\u003e325(99.39)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003e44(97.78)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e281(99.65)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e0.257\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"225\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp; ACE inhibit or ARB, n (%)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"105\"\u003e\n\u003cp\u003e248(75.84)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003e35(77.78)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e213(75.53)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e0.268\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"225\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp; Beta-blockers, n (%)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"105\"\u003e\n\u003cp\u003e302(92.35)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003e39(86.67)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e263(93.26)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e0.132\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eData are presented as absolute number (percentage) or mean \u0026plusmn; standard deviation. Bold indicates \u003cem\u003eP\u003c/em\u003e \u0026lt;0.05. ACEI, angiotensin-converting enzyme inhibitor; ARB, angiotensin-receptor blocker; DAPT: Dual Antiplatelet Therapy; GRACE, global registry of acute coronary events; HDL, high-density lipoprotein; hs-CRP, high-sensitivity C-reactive protein; hs-cTnI, high-sensitivity cardiac troponin I; HF: heart failure; LDL, low-density lipoprotein; LVEF, left ventricular ejection fraction; MI, myocardial infarction; NT-proBNP: N-terminal pro-B-type natriuretic peptide; SBP, systolic blood pressure; STEMI: ST-segment elevation myocardial infarction; TIMI, thrombolysis in myocardial infarction.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n\u003ch2\u003eExpression of SAA in older patients with STEMI and HCs\u003c/h2\u003e\n\u003cp\u003eThe serum level of SAA is significantly elevated in older patients with STEMI (735.94\u0026thinsp;\u0026plusmn;\u0026thinsp;506.60) ng/mL than in HCs (427.58\u0026thinsp;\u0026plusmn;\u0026thinsp;273.70) ng/mL (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; \u003cspan class=\"Underline\"\u003eSupplementary Figure \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e)\u003c/span\u003e.\u003c/p\u003e\n\u003cp\u003eData are displayed in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, we compared the SAA expression levels among older patients with STEMI and found that those who experienced 30-day HF events ([1372.08\u0026thinsp;\u0026plusmn;\u0026thinsp;869.66] ng/mL vs. [634.42\u0026thinsp;\u0026plusmn;\u0026thinsp;322.75] ng/mL, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), CS and/or cardiac death events ([1528.62\u0026thinsp;\u0026plusmn;\u0026thinsp;1048.41] ng/mL vs. [673.15\u0026thinsp;\u0026plusmn;\u0026thinsp;373.41] ng/mL, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) had significantly higher SAA levels than those without adverse events.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n\u003ch2\u003eSurvival analysis of SAA in older patients with STEMI\u003c/h2\u003e\n\u003cp\u003eTo illustrate the association between serum SAA and the major adverse cardiovascular events, we conducted univariable and multivariable Cox regression analyses. As shown in \u003cspan class=\"Underline\"\u003eSupplementary Table S3\u003c/span\u003e, after adjusting for risk scores, prognostic biomarkers (NT-proBNP, hs-CRP, and hs-cTnI at admission), and clinical variables, patients in the higher binary category of SAA had an increased risk of HF events (adjusted model 1 HR: 2.833, 95% CI: 1.320\u0026ndash;6.084, P\u0026thinsp;=\u0026thinsp;0.008; adjusted model 2 HR: 2.735, 95% CI: 1.275\u0026ndash;5.867, P\u0026thinsp;=\u0026thinsp;0.010; adjusted model 3 HR: 2.903, 95% CI: 1.366\u0026ndash;6.172, P\u0026thinsp;=\u0026thinsp;0.006) and a higher risk of CS and/or cardiac death events (adjusted model 1 HR: 2.843, 95% CI: 1.020\u0026ndash;7.922, P\u0026thinsp;=\u0026thinsp;0.046; adjusted model 2 HR: 2.824, 95% CI: 1.019\u0026ndash;7.821, P\u0026thinsp;=\u0026thinsp;0.046; adjusted model 3 HR: 3.056, 95% CI: 1.098\u0026ndash;8.505, P\u0026thinsp;=\u0026thinsp;0.032).\u003c/p\u003e\n\u003cp\u003eAs a continuous variable, each 1-SD increase in SAA was associated with increased risk of HF events (unadjusted HR: 3.595, 95% CI: 2.384\u0026ndash;5.420, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; adjusted model 1 HR: 2.694, 95% CI: 1.754\u0026ndash;4.136, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; adjusted model 2 HR: 2.676, 95% CI: 1.736\u0026ndash;4.126, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; adjusted model 3 HR: 2.694, 95% CI: 1.777\u0026ndash;4.086, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and CS and/or cardiac death events (unadjusted HR: 3.032, 95% CI: 1.774\u0026ndash;5.183, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; adjusted model 1 HR: 2.508, 95% CI: 1.413\u0026ndash;4.452, P\u0026thinsp;=\u0026thinsp;0.002; adjusted model 2 HR: 2.639, 95% CI: 1.465\u0026ndash;4.754, P\u0026thinsp;=\u0026thinsp;0.001; adjusted model 3 HR: 2.181, 95% CI: 1.282\u0026ndash;3.713, P\u0026thinsp;=\u0026thinsp;0.004). This association remained significant for SAA after adjusting for risk scores, prognostic biomarkers, and clinical variables (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e; \u003cspan class=\"Underline\"\u003eSupplementary Table S3\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eKaplan\u0026ndash;Meier analysis showed that when SAA was grouped by tertiles, the incidence of the two outcomes increased progressively with an increase in group grades (HF events [0.93% vs. 13.51% vs. 26.85%, log-rank P\u0026thinsp;\u0026lt;\u0026thinsp;0.001]; CS and/or cardiac death events [0.00% vs. 7.21% vs. 14.82%, log-rank P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cspan class=\"Underline\"\u003e)\u003c/span\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n\u003ch2\u003eIncremental prognostic value of SAA\u003c/h2\u003e\n\u003cp\u003eIn the ROC curve analysis, SAA demonstrated the superior ability to discriminate adverse cardiovascular events compared with risk scores and prognostic biomarkers \u003cspan class=\"Underline\"\u003e(Supplementary Figure S2)\u003c/span\u003e. Furthermore, SAA provided incremental information for predicting both endpoints when added to the combination of GRACE score and the three risk biomarkers (\u0026Delta;C-statistic: 0.084, 95% CI: 0.048\u0026ndash;0.119, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; \u0026Delta;C-statistic: 0.088, 95% CI: 0.169\u0026ndash;0.169, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Moreover, NRI and IDI analyses showed that adding SAA to known predictors improved event classification \u003cspan class=\"Underline\"\u003e(\u003c/span\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cspan class=\"Underline\"\u003e)\u003c/span\u003e. These results indicated that SAA added substantial discrimination/reclassification value over the established risk score, known biomarkers, and clinical variables.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eDiscrimination and reclassification improvement by SAA for risk prediction of clinical events.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eDiscrimination\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003eReclassification\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eC-statistics (95%CI)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e△C-statistics (95%CI)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eP Value\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eNRI (95%CI)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eP Value\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eIDI (95%CI)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eP Value\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHF events\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eModel 1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.767(0.698\u0026ndash;0.836)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ereference\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e/\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ereference\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e/\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ereference\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e/\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eModel 1\u0026thinsp;+\u0026thinsp;SAA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.851(0.804\u0026ndash;0.898)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.084(0.048\u0026ndash;0.119)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.592(0.288\u0026ndash;0.896)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.152(0.080\u0026ndash;0.225)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eModel 2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.783(0.721\u0026ndash;0.844)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ereference\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e/\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ereference\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e/\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ereference\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e/\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eModel 2\u0026thinsp;+\u0026thinsp;SAA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.863(0.816\u0026ndash;0.909)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.080(0.046\u0026ndash;0.114)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.688(0.392\u0026ndash;0.984)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.163(0.092\u0026ndash;0.235)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eModel 3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.769(0.684\u0026ndash;0.854)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ereference\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e/\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ereference\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e/\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ereference\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e/\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eModel 3\u0026thinsp;+\u0026thinsp;SAA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.872(0.825\u0026ndash;0.918)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.103(0.026\u0026ndash;0.180)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.009\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.707(0.408\u0026ndash;1.07)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.152(0.081\u0026ndash;0.222)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCS and/or cardiac death\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eModel 1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.758(0.658\u0026ndash;0.858)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ereference\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e/\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ereference\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e/\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ereference\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e/\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eModel 1\u0026thinsp;+\u0026thinsp;SAA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.846(0.777\u0026ndash;0.915)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.088(0.169\u0026ndash;0.169)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.033\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.603(0.202\u0026ndash;1.005)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.003\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.142(0.042\u0026ndash;0.241)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.005\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eModel 2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.785(0.702\u0026ndash;0.868)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ereference\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e/\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ereference\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e/\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ereference\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e/\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eModel 2\u0026thinsp;+\u0026thinsp;SAA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.858(0.790\u0026ndash;0.925)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.073(-0.005-0.151)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.066\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.759(0.368\u0026ndash;1.150)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.164(0.063\u0026ndash;0.265)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eModel 3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.826(0.728\u0026ndash;0.923)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ereference\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e/\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ereference\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e/\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ereference\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e/\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eModel 3\u0026thinsp;+\u0026thinsp;SAA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.892(0.840\u0026ndash;0.944)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.066(0.011\u0026ndash;0.121)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.019\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.447(0.038\u0026ndash;0.856)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.032\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.106(0.011\u0026ndash;0.201)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.029\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"8\"\u003eThe C-statistic indicates the difference compared to the \u0026ldquo;reference model.\u0026rdquo; Reference model 1 includes the GRACE score and three risk biomarkers (NT-proBNP, hs-CRP, and hs-cTnI at admission); model 2 includes the TIMI risk score and the same three risk biomarkers, model 3 includes clinical variables: age, male, frailty, anterior MI, Killip class\u0026thinsp;\u0026gt;\u0026thinsp;1, and LVEF. Net reclassification improvement (NRI) was assessed using the continuous NRIs. Reclassification of patients who did or did not reach the clinical endpoint is shown. NRI: net reclassification improvement; IDI: integrated discrimination improvement\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n\u003ch2\u003eSubgroup analysis\u003c/h2\u003e\n\u003cp\u003eAccording to the results presented in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e, SAA showed reliable predictive performance for 30‑day HF events among all prespecified subgroups, and no significant interaction was found in the statistical testing (all P for interaction\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Notably, a differential predictive pattern emerged regarding frailty. In frail patients, the association remained non-significant (HR: 1.558, 95% CI: 0.913\u0026ndash;2.657, P\u0026thinsp;=\u0026thinsp;0.104), whereas SAA exhibited a stronger predictive capacity in non-frail patients with STEMI (HR: 5.477, 95% CI: 2.460\u0026ndash;12.193, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and the interaction reached statistical significance (P for interaction\u0026thinsp;=\u0026thinsp;0.019). Meanwhile, SAA levels in frail older patients with STEMI were (964.58\u0026thinsp;\u0026plusmn;\u0026thinsp;680.23) ng/mL, significantly higher than those in non-frail patients (664.31\u0026thinsp;\u0026plusmn;\u0026thinsp;414.85) ng/mL (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; \u003cspan class=\"Underline\"\u003eSupplementary Figure S3\u003c/span\u003e). The potential mechanism underlying this differential result may be associated with the widespread inflammaging in frail patients. Additionally, the poor prognosis of frail patients is driven by multiple factors, such as decreased reserve function of multiple organs and reduced tolerance to complications, leading to a relative decrease in the weight of inflammatory markers in the prognostic evaluation system.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eTo investigate the predictive value of SAA for 30-day MACE, we enrolled older patients aged\u0026thinsp;\u0026ge;\u0026thinsp;60 years with STEMI who underwent primary PCI. Our findings demonstrated that SAA levels in older patients with STEMI were considerably higher than those in HCs. The elevated SAA levels were independently associated with an increased risk of HF, CS, and cardiac death within 30 days. Furthermore, the integration of SAA into conventional risk assessment systems markedly improved the predictive efficacy for adverse prognosis, with a more robust predictive value particularly observed in non-frail patients. This finding provides a novel biomarker basis for risk stratification and prognostic evaluation in older patients with STEMI, and holds important clinical significance \u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003e(Central Illustration)\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eAMI triggers waves of circulating inflammatory cells, which are, in part, beneficial but harmful when in excess.[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] The first wave of inflammation occurs within 24 h of AMI onset, characterized primarily by the presence of polymorphonuclear neutrophils in the damaged myocardium, which is associated with an expanded infarct size and reduced LVEF.[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] Subsequently, a second wave of inflammatory response gradually develops, dominated by the recruitment of macrophages that appear to play an important role in clearing cellular debris and promoting myocardial healing.[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eHeart failure continues to be a serious and difficult complication among AMI patients, with its incidence varying between 7% and 38% based on different diagnostic standards.[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] This condition is closely linked to poorer short-term and long-term prognoses.[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] Following myocardial infarction, unfavorable left ventricular remodeling forms the structural basis of ischemic heart failure, involving complex short‑term and long‑term changes in ventricular size, shape, function, and cellular‑molecular characteristics.[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] Early-phase left ventricular remodeling is primarily attributed to cardiomyocyte apoptosis and necrosis, as well as myocardial ischemia\u0026ndash;reperfusion injury induced by extensive infarction. This process leads to thinning and dilatation of the infarcted myocardial wall, during which the initial inflammatory response exerts a pivotal role.\u003c/p\u003e \u003cp\u003eAs an acute-phase inflammatory protein synthesized in the liver, SAA can rapidly increase in response to inflammatory stimuli. Its dynamic range is substantially greater than that of hs-CRP, and it has been shown to be a more sensitive inflammatory marker in non-cardiovascular inflammatory diseases such as infections and autoimmune diseases.[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] This study found that SAA levels in older patients with STEMI were considerably higher than those in HCs, and further elevated in patients who developed HF, CS, or cardiac death. This is consistent with the \"early response\" characteristic of SAA in inflammatory reactions\u0026mdash;local inflammation triggered by plaque rupture can rapidly activate the systemic inflammatory cascade, leading to increased SAA synthesis. Higher SAA levels may reflect a more intense inflammatory response and a wider range of myocardial injury, thereby increasing the risk of adverse events. Previous studies have shown that hs-CRP is associated with cardiac events and left ventricular dysfunction in older patients with AMI.[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan additionalcitationids=\"CR40\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] However, using Cox regression analysis, we found that even after adjusting for risk scores, traditional prognostic markers, and clinical variables, SAA remained independently associated with 30-day HF events, CS, and/or cardiac death.\u003c/p\u003e \u003cp\u003eThese findings suggest that compared with hs-CRP, SAA is more capable of reflecting the inflammatory burden and prognostic outcomes in older patients with STEMI, which may be attributed to two factors. First, after the onset of STEMI, hs-CRP reaches its peak at 24\u0026ndash;48 hours.[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] In contrast, SAA increases significantly within 4\u0026ndash;6 hours of STEMI onset and exhibits an earlier peak time than hs-CRP, which enables more precise capture of the dynamic changes in the early inflammatory response.[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] Second, SAA is not only an inflammatory marker but may also be involved in the pathophysiology of AMI by promoting neutrophil chemotaxis and enhancing vascular endothelial injury.[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eThe 2025 American College of Cardiology clinical practice guidelines[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] recommend the application of the GRACE 2.0 score or TIMI risk score for risk stratification in patients after myocardial infarction, which confirms the irreplaceable clinical value of these classical assessment tools. However, the guidelines also point out that the clinical efficacy of these risk scores in practical application and their impact on the long-term clinical outcomes of patients have not been fully investigated and verified. Meanwhile, its applicability in older patients remains limited. Older adults often have age-related comorbidities such as frailty and malnutrition, which are difficult to fully capture using traditional risk scores, resulting in the underestimation of some high-risk patients. Through ROC, C-statistic, NRI, and IDI analyses, we found that the discriminatory power of SAA was superior to that of traditional risk assessment tools (GRACE and TIMI), prognostic biomarkers, and clinical variable models such as age, frailty, and others. This finding indicates that SAA can serve as a complementary indicator to conventional risk assessment tools, helping clinicians more accurately identify high-risk populations among older patients with STEMI.\u003c/p\u003e \u003cp\u003eSubgroup analysis revealed considerable heterogeneity in the predictive value of SAA for 30-day HF events among older patients with STEMI between the frail and non-frail subgroups. In non-frail patients, SAA levels were strongly correlated with the risk of HF, whereas in frail patients, this association was not statistically significant. The potential mechanism underlying this differential finding may be associated with the unique pathophysiological characteristics of frail patients. Frail populations generally exhibit a state of chronic low-grade inflammation associated with \"inflammaging,\" and their baseline SAA levels may already be in the pathologically elevated range. This masks the acute-phase SAA elevation signal in AMI, thereby impairing its prognostic predictive efficacy.[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e] Additionally, the poor prognosis of frail patients is driven by multiple factors, such as decreased reserve function of multiple organs and reduced tolerance to complications, leading to a relatively decreased weight of inflammatory markers in the prognostic evaluation system. This observation is consistent with those of previous studies on the cardiovascular prognosis of frail patients.[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e] The results of the present study carry notable value for clinical practice. In non-frail older patients with STEMI, SAA may serve as a preferentially recommended prognostic biomarker to inform the development of individualized treatment strategies. However, in frail patients, relying solely on SAA levels is insufficient for accurate prognostic assessment. Instead, a comprehensive risk evaluation system should be established by integrating standardized frailty assessment tools,[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e] such as FI, Clinical Frailty Scale, and the Frailty Assessment Scale for Heart Failure. Future studies may further explore the combined predictive model of \"inflammatory markers\u0026thinsp;+\u0026thinsp;frailty assessment\" to improve the accuracy of risk stratification for frail older patients with STEMI, thereby providing evidence-based support for optimizing clinical management strategies.\u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eStudy limitations\u003c/h2\u003e \u003cp\u003eOur study has some limitations. First, although this study adopted a single‑center design, the adequate sample size provided sufficient statistical power for the primary composite endpoint analysis. However, the analysis of the exploratory frailty subgroup may have been limited by insufficient statistical power. In the future, we will expand the sample size to conduct a study on frailty assessment in patients with MI. Second, only the admission SAA level was measured; dynamic monitoring of SAA trends at different time points after AMI (e.g., 24 h and 72 h post-PCI) was not performed. However, given the characteristics of SAA\u0026mdash;its early rise and rapid recovery\u0026mdash;we believe the SAA level at admission should be the optimal time point for prediction. Finally, this study enrolled only patients with STEMI who underwent primary PCI, and did not include patients with non-ST-segment elevation myocardial infarction (NSTEMI). The primary rationale for this selection was that our study focused on early serum SAA alterations in AMI. In this registry analysis, however, the mean time from symptom onset to blood collection was 48 hours for NSTEMI patients.[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e] Moreover, left ventricular remodeling and inflammation constitute the main pathophysiological mechanisms underlying adverse cardiovascular events in NSTEMI. We therefore hypothesize that SAA possesses comparable prognostic value in NSTEMI, although further validation is required.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eSAA can independently predict 30‑day adverse cardiovascular events in older STEMI patients and serves as a valuable prognostic biomarker, particularly in non‑frail individuals.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAMI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAcute myocardial infarction\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emyocardial infarction\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSTEMI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eST-segment elevation MI\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNSTEMI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003enon-ST-segment elevation myocardial infarction\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHCs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ehealthy controls\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003epercutaneous coronary intervention\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSAA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSerum amyloid A\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ehs-CRP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHigh-sensitivity C-reactive protein\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ehs-cTnI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHigh-sensitivity troponin I\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-B-type natriuretic peptide\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMACE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emajor adverse cardiovascular events\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eheart failure\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecardiogenic shock\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003estandard deviation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ehazard ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCis\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003econfidence intervals\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eROC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ereceiver operating characteristic\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNRIs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003enet reclassification indices\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIDI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eintegrated discrimination improvement\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eECG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eelectrocardiographic\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGRACE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eThe Global Registry of Acute Coronary Events\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTIMI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eThrombolysis in Myocardial Infarction\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003efrailty index\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 \u003c/div\u003e"},{"header":"Declarations","content":" \u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003e The study protocol was approved by the Ethics Committee of Henan Provincial People\u0026rsquo;s Hospital (Ethical Approval No. 15 of 2022) and was conducted in accordance with the principles of the Declaration of Helsinki. Written informed consent was obtained from all participants.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was supported by grants from the Natural Science Foundation of Henan Province (252300421607) and the Medical Science and Technology Research Initiative of the Henan Provincial Health Commission (LHGJ20240076).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eXY contributed to study conceptualization, data analysis, and manuscript drafting. XW and CC participated in data collection, analysis, and result interpretation. XX, XC, and YW assisted with statistical analysis and findings interpretation. XX and XC revised the manuscript. PQ and HD offered critical guidance throughout the study, supervised the project, and revised the final manuscript. All authors read and approved the final version.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe are particularly grateful to all of the patients with acute myocardial infarction and volunteers who participated in the present study.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets used and analyzed during the current study are available fromthe corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAnderson JL, Morrow DA. Acute Myocardial Infarction. N Engl J Med. 2017;376(21):2053\u0026ndash;64.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eImbesi A, Greco A, Spagnolo M, Laudani C, Raffo C, Finocchiaro S, Mazzone PM, Landolina D, Mauro MS, Cutore L, et al. Targeting Inflammation After Acute Myocardial Infarction. J Am Coll Cardiol. 2025;86(15):1146\u0026ndash;69.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBhatt DL, Lopes RD, Harrington RA. Diagnosis and Treatment of Acute Coronary Syndromes. JAMA. 2022;327(7):662\u0026ndash;75.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMatter MA, Paneni F, Libby P, Frantz S, St\u0026auml;hli BE, Templin C, Mengozzi A, Wang Y-J, K\u0026uuml;ndig TM, R\u0026auml;ber L, et al. Inflammation in acute myocardial infarction: the good, the bad and the ugly. Eur Heart J. 2024;45(2):89\u0026ndash;103.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaunderson CED, Brogan RA, Simms AD, Sutton G, Batin PD, Gale CP. Acute coronary syndrome management in older adults: guidelines, temporal changes and challenges. Age Ageing. 2014;43(4):450\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThe L. Population ageing in China: crisis or opportunity? Lancet. 2022;400(10366):1821.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTung BWL, Ng ZY, Kristanto W, Saw KW, Chan S-P, Sia W, Chan KH, Chan M, Kong W, Lee R, et al. Characteristics and outcomes of young patients with ST segment elevation myocardial infarction undergoing primary percutaneous coronary intervention: retrospective analysis in a multiethnic Asian population. Open Heart. 2021;8(1):e001437.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi T, Shi W, Wang G, Jiang Y. Prevalence and risk factors of frailty in older patients with coronary heart disease: A systematic review and meta-analysis. Arch Gerontol Geriatr. 2025;130:105721.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSayed N, Huang Y, Nguyen K, Krejciova-Rajaniemi Z, Grawe AP, Gao T, Tibshirani R, Hastie T, Alpert A, Cui L, et al. An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging. Nat Aging. 2021;1(7):598\u0026ndash;615.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFonseca FAH, Fran\u0026ccedil;a CN, Fonseca HAR, Serra AJ, Izar MC. Key inflammatory players for infarcted mass and cardiac remodeling after acute myocardial infarction. Front Cardiovasc Med. 2025;12:1609705.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOng S-B, Hern\u0026aacute;ndez-Res\u0026eacute;ndiz S, Crespo-Avilan GE, Mukhametshina RT, Kwek X-Y, Cabrera-Fuentes HA, Hausenloy DJ. Inflammation following acute myocardial infarction: Multiple players, dynamic roles, and novel therapeutic opportunities. Pharmacol Ther. 2018;186:73\u0026ndash;87.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSeropian IM, Toldo S, Van Tassell BW, Abbate A. Anti-Inflammatory Strategies for Ventricular Remodeling Following ST-Segment Elevation Acute Myocardial Infarction. 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Cells. 2021;10(7):1676.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu S, Jiang H, Dhuromsingh M, Dai L, Jiang Y, Zeng H. Evaluation of C-reactive protein as predictor of adverse prognosis in acute myocardial infarction after percutaneous coronary intervention: A systematic review and meta-analysis from 18,715 individuals. Front Cardiovasc Med. 2022;9:1013501.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKraaijenhof JM, Nurmohamed NS, Nordestgaard AT, Reeskamp LF, Stroes ESG, Hovingh GK, Boekholdt SM, Ridker PM. Low-density lipoprotein cholesterol, C-reactive protein, and lipoprotein(a) universal one-time screening in primary prevention: the EPIC-Norfolk study. Eur Heart J. 2025;46(39):3875\u0026ndash;84.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLin X, Fan Q, Li Q, Bo X, Chen S, Wu X. Inflammatory markers guide early risk stratification and prognosis in elderly patients with acute myocardial infarction. 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Can J Cardiol. 2021;37(1):77\u0026ndash;85.\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":"acute myocardial infarction, adverse cardiovascular events, older patients, prospective study, serum amyloid A protein","lastPublishedDoi":"10.21203/rs.3.rs-9015088/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9015088/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDespite improved treatments for myocardial infarction (MI), residual risk persists in older patients, mainly attributed to age- associated comorbidities and inflammation. Serum amyloid A (SAA) lacks sufficient evidence for predicting short-term clinical endpoints in older patients with ST-segment elevation MI (STEMI).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe prospectively enrolled 327 older patients (≥ 60 years) with STEMI individuals treated with primary percutaneous coronary intervention and 327 healthy controls (HCs). SAA levels were measured at admission. The Global Registry of Acute Coronary Events score, Thrombolysis in Myocardial Infarction risk score, and frailty index were assessed. The primary endpoint was 30-day major adverse cardiovascular events (MACE), defined as cardiac death, heart failure (HF), and cardiogenic shock (CS).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe serum SAA levels in older STEMI patients were significantly higher than those in the HC group ([735.94 ± 506.60] ng/mL vs. [427.58 ± 273.70] ng/mL, P\u0026lt;0.001). Moreover, the SAA expression was further remarkably elevated in patients with 30-day heart failure (HF), cardiogenic shock (CS) or cardiac death events (all P\u0026lt;0.001).\u003c/p\u003e\n\u003cp\u003eAfter adjusting for risk scores, traditional biomarkers, and clinical variables, higher SAA levels independently predicted HF events (all adjusted P \u0026lt; 0.01) and CS and/or cardiac death (all adjusted P \u0026lt; 0.01). The inclusion of SAA in an established risk factor models significantly enhanced C-statistics, net reclassification, and integrated discrimination. SAA strongly predicted HF in non-frail patients (hazard ratio [HR] = 5.477, P \u0026lt; 0.001), but not in frail patients (HR = 1.558, P = 0.104), with a significant interaction (P = 0.019).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSAA is an independent predictor of 30-day MACE in older patients with STEMI and enhances traditional risk assessment, especially in non-frail individuals.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTrial registration:\u003c/strong\u003e ClinicalTrails.gov registration no. 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