Albumin-to-Globulin ratio as an independent risk factor for predicting prognostic risk in patients with acute coronary syndrome undergoing percutaneous coronary intervention

Albumin-to-Globulin ratio as an independent risk factor for predicting prognostic risk in patients with acute coronary syndrome undergoing percutaneous coronary intervention | 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 Albumin-to-Globulin ratio as an independent risk factor for predicting prognostic risk in patients with acute coronary syndrome undergoing percutaneous coronary intervention Linlin Wang, Shuang Xie, Aoxue Mei, Ying Fu, Xinchen Wang, Ge Song, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6715881/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 18 Jul, 2025 Read the published version in BMC Cardiovascular Disorders → Version 1 posted 10 You are reading this latest preprint version Abstract Purpose: Acute coronary syndromes (ACS) is a leading cause of death worldwide. Albumin and globulin are the main components of serum proteins. The albumin-to-globulin ratio (AGR) is often used to assess nutritional status. However, the clinical significance of the AGR in predicting the prognosis of patients with ACS remains unclear. Patients and methods: A total of 1408 patients with ACS who underwent percutaneous coronary intervention (PCI) were consecutively enrolled between January 2016 and December 2018 at The Affiliated Hospital of Chengde Medical University. The follow-up endpoints were defined as cardiac death or recurrent acute myocardial infarction. Results: A total of 1363 patients responded in the follow-up period, of whom 49 had MACEs. AGR was significantly different between the MACEs and non-MACE groups. The area under the curve for the AGR was 0.619 (p =0.004, 95% confidence interval [CI]: 0.542–0.697). The optimal cut-off value for the AGR was determined to be 1.350 using Youden’s index. The cumulative survival rate of the low AGR group was significantly lower than that of the high AGR group, according to the Kaplan-Meier curve (log-rank p=0.008). Multivariate Cox proportional hazards model showed age ≥60 years, HR:2.689 (95%CI:1.288-5.615, p=0.008), left ventricular ejection fraction (LVEF) <40%, HR: 3.527, (95%CI: 1.357–9.164, p=0.010), and AGR<1.350, HR: 2.180, (95%CI: 1.078–4.407, p=0.030) were all independent risk factors. A restricted cubic spline showed that a decreasing AGR was correlated with increasing risk of MACEs. Conclusion: AGR<1.350 is an independent prognostic risk factor for patients with ACS undergoing PCI and may be a valuable clinical marker for identifying high-risk patients. Clinical trial number: not applicable. acute coronary syndrome albumin-to-globulin ratio recurrent myocardial infarction percutaneous coronary intervention major adverse cardiovascular events Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Acute coronary syndromes (ACS) encompass a range of conditions associated with suddenly reduced blood flow to the heart, including ST-segment elevation myocardial infarction (STEMI), non-ST-segment elevation myocardial infarction (NSTEMI), and unstable angina (UA). ACS continue to be the leading cause of mortality worldwide despite significant advancements in medical science [ 1 ] . The albumin-to-globulin ratio (AGR) has recently garnered attention as an important prognostic biomarker of cardiovascular and other systemic diseases. The serum albumin-to-globulin ratio can reflect the inflammatory status and nutritional condition of patients, providing insights into disease prognosis and potential therapeutic outcomes [ 2 , 3 ] . Previous studies highlighted its prognostic significance in various malignancies. Meta-analyses and systematic reviews have corroborated that a low pretreatment AGR correlates with poor overall survival and a high incidence of lymph node metastasis, making it a versatile biomarker across disciplines [ 9 – 11 ] . This cross-disciplinary applicability also underscores the fundamental biological pathways shared between systemic inflammation and disease progression, reinforcing the potential of the AGR as a multifaceted prognostic tool. Moreover, low AGR levels have been associated with worse clinical outcomes in cardiovascular diseases, including heart failure (HF), high incidences of in-stent restenosis, and revascularization events after percutaneous coronary intervention (PCI) [ 2 – 8 ] . However, while substantial evidence supports a correlation between low AGR and adverse health outcomes, the ability of the AGR to predict prognosis of patients with ACS undergoing PCI remains unknown. We aimed to further delineate the prognostic role of the AGR in predicting the risk of MACEs in patients with ACS undergoing PCI. Material and Methods Study Population In the present study, 1408 patients with ACS who underwent PCI between January 2016 and December 2018 at the Department of Cardiology, The Affiliated Hospital of Chengde Medical University (Hebei, China) were consecutively enrolled. All patients underwent coronary angiography and PCI. The procedures were performed by an experienced cardiologist. The inclusion criteria were as follows: (1) age ≥ 18 years; (2) diagnosis of clinical types of ACS, including STEMI, NSTEMI, and UA, according to the 2013 ACCF/AHA Guideline for the Management of ST-Elevation Myocardial Infarction and 2014 AHA/ACC Guideline for the Management of Patients with Non-ST-Elevation Acute Coronary Syndromes; (3) stenosis of at least 50% of the luminal diameter in at least one major coronary artery branch after coronary angiography. The exclusion criteria were as follows: (1) death during hospitalization; (2) critical structural heart disease; (3) severe inflammatory infectious disease; (4) connective tissue disease; (5) secondary coronary vasculitis; (5) presence of other heart diseases causing angina pectoris, such as hypertrophic cardiomyopathy, myocarditis, and; severe valvular heart disease; (6) severe liver and kidney disease (creatinine clearance < 15 ml/min)), (7) missing data exceeding 10%; (8) history of CABG, etc. This study was approved by the Ethics Committee of the Affiliated Hospital of Chengde Medical University (approval Number: CYFYLL2021036) and conducted according to the tenets of the Declaration of Helsinki. All the participants provided informed consent. Baseline Demographics and Clinical Characteristics The cardiovascular research team collected all the demographic data and clinical characteristics of the enrolled patients. Hypertension was defined as a systolic blood pressure ≥ 140 mmHg and/or diastolic blood pressure ≥ 90 mmHg at rest or a previous diagnosis of hypertension during antihypertensive therapy [ 12 ] . Type 2 diabetes mellitus (DM) was de-fined as diabetes symptoms with random blood glucose level ≥ 11.1 mmol/L, fasting plasma glucose level ≥ 7.0 mmol/L, or 2-h oral glucose tolerance test level ≥ 11.1 mmol/L or as no diabetes symptoms with at least twice the blood glucose level meeting the abovementioned criteria [ 13 ] . Dyslipidemia was defined as serum total cholesterol level ≥ 5.18 mmol/L, high-density lipoprotein cholesterol (HDL-C) level ≤ 1.04 mmol/L, low-density lipoprotein cholesterol (LDL-C) level ≥ 3.37 mmol/L, or triglyceride level ≥ 1.7 mmol/L, or a previous diagnosis of dyslipidemia in medication [ 14 ] . Follow-Up and Endpoints Follow-up visits were completed by cardiovascular physicians, and each follow-up adhered to the principle of standardization to control bias. Follow-up data were collected via clinical visits at 1, 3, 6, and 12 months, and annually thereafter. The primary study endpoints were MACEs, including cardiac death and recurrent acute myocardial infarction (AMI). Statistical Analysis The Kolmogorov-Smirnov test was employed to assess the normality of continuous variables, with normally distributed and non-normally distributed variables reported as mean ± standard deviation and median with interquartile range, respectively. Differences in non-normally distributed continuous variables between the MACEs and non-MACE groups were analyzed using the Mann-Whitney U-test. Categorical variables were expressed as counts (%) and compared using the χ² test. The Kaplan-Meier method was used to evaluate the incidence of adverse cardiovascular events across groups, while the log-rank test was used to assess differences between the two groups. Receiver operating characteristic (ROC) curves were used to determine the diagnostic value of AGR, with Youden's index (sensitivity + specificity − 1) employed to identify the optimal cutoff point. Additionally, time-dependent ROC curves were used to examine temporal correlations. The dose-response relationship between the AGR and MACEs in patients with ACS undergoing PCI was illustrated using restricted cubic spline (RCS) curves. Univariate and multivariate Cox proportional hazards models were used to evaluate predictive capacity of AGR for prognostic risk. All statistical analyses were performed using SPSS (version 26, SPSS Inc., Chicago, IL, USA), GraphPad Prism 8.0 (GraphPad Software Inc., La Jolla, CA, USA), and R version 4.3.3. Statistical significance was set at p < 0.05. Results Patients’ Characteristics Of the 1408 enrolled patients with ACS, 45 were excluded: 4 patients with ACS who had infectious diseases, 2 patients with ACS who had blood system diseases, and 3 patients with AMI who had malignant tumors. Furthermore, 36 patients with ACS lost to follow-up. Ultimately, 1363 patients with ACS who responded during the follow-up period and were included in the analysis, with a median follow-up duration of 1123 days (3.0 years). Among the 1363 patients, 49 experienced MACEs, 26 died, and 23 required rehospitalization for AMI recurrence. Table 1 shows the baseline characteristics of the patients with ACS who underwent PCI in the MACEs and non-MACE groups. The MACEs group and non-MACEs group showed significant differences in the proportion of patients in terms of age (62.59±8.02 vs 58.66±10.22), family history of CAD (2 [4.1%] vs 193 [14.7%]), the WBC count (9.79±3.84 vs 8.65 ±3.36), creatine kinase MB (CK-MB) (38.00 [14.00–193.96] vs 16.00 [10.00–47.94]), left ventricular ejection fraction (LVEF) (53.00±10.36 vs 57.44 ±8.83), unstable angina (UA) (12 [24.5%] vs 568 [43.2%]), ST-segment elevation myocardial infarction (STEMI) (30 [61.2%] vs 548 [41.7%], and AGR (1.29±0.18 vs 1.38 ± 0.19) (all p<0.05). Table 1 Baseline patient characteristics of the MACEs and non-MACEs groups Variables MACEs group （n=49） Non-MACEs group （n=1314） χ2/Z p-value Demographic Male 34 (69.4%) 981 (74.7%) 0.690 0.406 Age (years) 62.59 ±8.02 58.66 ±10.22 -2.753 0.005 Dyslipidemia 27 (55.1%) 754 (57.4%) 0.100 0.751 Hypertension 26 (53.1%) 792 (60.3%) 1.024 0.312 Diabetes mellitus 14 (28.6%) 333 (25.3%) 0.260 0.610 Ischemic stroke 11 (22.4%) 189 (14.4%) 2.454 0.117 Smoking 23 (46.9%) 677 (51.5%) 0.397 0.529 Family history of CAD 2 (4.1%) 193 (14.7%) 4.335 0.037 Laboratory data WBC (10 9 /L) 9.79 ±3.84 8.65±3.36 -2.259 0.024 HGB (g/L) 144.57±14.17 146.16±15.13 -0.786 0.432 PLT (10 9 /L) 194.57±68.09 216.86±63.16 -1.878 0.060 TC (mmol/L) 4.59±1.10 4.42±1.08 -0.995 0.320 TG (mmol/L) 1.43 (0.85,2.46) 1.60 (1.02,2.39) -1.234 0.217 HDL-C (mmol/L) 1.17±0.34 1.12±0.31 -1.005 0.315 LDL-C (mmol/L) 2.49±0.92 2.41±0.85 -0.871 0.384 CK-MB (U/L) 38.00 (14.00,193.96) 16.00 (10.00,48.94) -3.489 ＜0.001 Cr (umol/L) 70.34±16.63 70.29±28.79 -0.490 0.624 UA (umol/L) 333.26±80.08 330.79±92.13 -0.668 0.504 TP (g/L) 69.49 ±7.41 71.06 ±6.12 -1.913 0.056 ALB (g/L) 38.86 ±3.84 40.96 ±3.70 -3.794 ＜0.001 AGR 1.29±0.18 1.38±0.19 -2.843 0.004 LVEF 53.00±10.36 57.44±8.83 -2.957 0.003 Clinical classification of ACS UA 12 (24.5%) 568 (43.2%) 6.784 0.009 STEMI 30 (61.2%) 548 (41.7%) 7.370 0.007 Non-STEMI 7 (14.3%) 198 (15.1%) 0.023 0.880 Coronary angiography 1 vessel lesion 12 (24.5%) 420 (32.0%) 0.735 0.391 2 vessels lesion 12 (24.5%) 422 (32.1%) 0.147 0.701 3 vessels lesion 25 (51.0%) 472 (35.9%) 1.441 0.230 Note : Data are presented as n (%) or as the median [range]. Abbreviations : MACEs, major adverse cardiovascular events; CAD, coronary artery disease; WBC, white blood cell; HGB, Hemoglobin; PLT, Platelet; TC, total cholesterol; TG, triglyceride; HDL‐C, high‐density lipoprotein cholesterol; LDL‐C, low density lipoprotein cholesterol; CK-MB, creatine kinase MB; Cr, creatinine; UA, uric acid; TP, total protein; ALB, albumin; AGR, albumin to globulin ratio; LVEF, left ventricular ejection fraction; UA, unstable angina; STEMI, ST-segment elevation myocardial infarction; Non-STEMI, non-ST-segment elevation myocardial infarction. Receiver Operating Characteristic (ROC) Curve, Survival Analysis ,and Time-Dependent ROC. The ROC curve was plotted to test the ability of AGR to predict MACEs. The AUC for the AGR was 0.619 (p =0.004, 95% confidence interval [CI]: 0.542–0.697) (Figure 1). Based on Youden’s index, the optimal diagnostic cut-off value for AGR was 1.350, with a sensitivity of 69.40% and a specificity of 53.50%. Therefore, patients with ACS were divided into low AGR (<1.350) and high AGR (≥1.350) groups based on the cut-off values. The Kaplan–Meier curve ( Figure 2 ) showed that the low AGR group had significantly lower cumulative survival than the high AGR group (log-rank p = 0.008). Next, we performed a time-dependent ROC analysis to test the changes in prediction ability over time. Figure 3 A shows time-dependent ROC curves. The 1-, 2-, 3-, and 5-year AUCs were 0.607, 0.624, 0.617, and 0.610, respectively. As shown in Figure 3 B , the time-dependent AUC indicated that the AGR had similar predictive power for MACE incidence at different time periods. AGR showed good ability to predict MACEs. Univariate and Multivariate Cox Hazard Proportional Models As shown in Table 1 , age, family history of CAD, WBC count, CK-MB level, AGR, LVEF, UA level, and STEMI showed significant differences. We converted continuous variables into categorical variables and performed univariate COX analysis. The results showed significant differences (all p<0.05) ( Table 2 ). Additionally, we considered the clinical prognostic significance of the factors mentioned in the univariate COX hazard proportional analysis and age, LVEF<40%, and AGR<1.350 in multivariate Cox hazard proportional models. The results were age HR:2.689 (95%CI:1.288-5.615, p=0.008); LVEF<40%, HR: 3.527, (95%CI: 1.357–9.164, p=0.010) and AGR<1.350, HR: 2.180, (95%CI: 1.078–4.407, p=0.030) ( Table 3 Figure 4 ). Therefore, an AGR<1.350 was correlated with the risk of MACEs in patients with ACS. Table 2 Univariate Cox Hazard Proportional Model for Predictive Factors of MACEs Variables Univariate HR (95% CI) p-value Age ≥60 years 2.089 (1.150–3.796) 0.016 Family history of CAD 0.242 (0.059–0.996) 0.049 WBC≥10 10 9 /L 2.134 (1.219–3.736) 0.008 CK-MB≥32 U/L 2.495 (1.411–4.410) 0.002 LVEF<40% 5.055 (1.977–12.928) 0.001 AGR <1.350 2.472 (1.346–4.540) 0.004 UA 0.421 (0.219–0.807) 0.009 STEMI 2.224 (1.252–3.952) 0.006 Table 3 Multivariate Cox Hazard Proportional Model for Predictive Factors of MACEs Variables Multivariate HR (95% CI) p-value Age category 2.689 (1.288–5.615) 0.008 LVEF<40% 3.527 (1.357–9.164) 0.010 AGR <1.350 2.180 (1.078–4.407) 0.030 Abbreviations : MACEs, major adverse cardiovascular events; CAD, coronary artery disease; WBC, white blood cell; CK-MB, creatine kinase MB; LVEF, left ventricular ejection fraction; AGR, albumin to globulin ratio; UA, unstable angina; STEMI, ST-segment elevation myocardial infarction. Restricted Cubic Spline (RCS) We also visualized the correlation between the AGR and MACEs using the RCS. A low AGR was associated with an increased risk of MACEs (p =0.009 and p =0.864; Figure 5 ). The plots show that decreasing AGR levels correlated with increasing risk of MACEs. Thus, a low AGR was an independent risk factor for patients with ACS who underwent PCI. Discussion In the present study, we investigated the predictive ability of the AGR for prognostic risk in patients with ACS undergoing PCI. The main findings of our study were as follows: (1) low AGR was associated with poor prognosis and was an independent risk factor for PCI in ACS patients; (2) patients with ACS who underwent PCI had lower AGR and cumulative survival rate than those in controls; (3) the diagnostic efficiency of AGR was relatively stable and still has value with the increase in time; (4) AGR was a good predictor of mortality in ACS patients after PCI; and (5) low AGR was significantly associated with age and lower LVEF. To our knowledge, this is the first study to analyze the correlation between this novel AGR index and prognosis in patients with ACS who underwent PCI. Albumin, which is synthesized in the liver, functions as a negative acute-phase reactant and is an accessible and reliable biomarker of the basal metabolome. It is also a significant indicator of inflammation, infection, and nutritional status [15-17] . Emerging evidence underscores hypoalbuminemia, characterized by low serum albumin levels, as a critical prognostic marker for various diseases. A study involving 438 patients with acutely decompensated heart failure (ADHF) demonstrated that low serum albumin concentrations correlate with an increased risk of mortality in this cohort [18] . Reduced serum albumin levels are associated with adverse clinical outcomes. A comprehensive analysis of 1,070 cancer patients showed a strong association between hypoalbuminemia and both venous thromboembolism (VTE) and mortality [19] . The relationship between hypoalbuminemia and disease outcomes can be partially attributed to the involvement of albumin in inflammatory processes that enhance capillary permeability, facilitating the translocation of albumin into the interstitial space while increasing its distribution volume. Furthermore, inflammation diminishes the half-life of albumin, resulting in a decrease in its total mass, despite elevated synthesis rates [4] . As inflammatory responses intensify, serum albumin levels decline significantly, indicating that physiological reactions to inflammation substantially reduce circulating albumin concentrations [21] . Moreover, age-related reductions in liver volume and blood flow [22] contribute to a decrease in albumin production over time. Additionally, research has indicated that during immune responses associated with disease states, amino acids from available proteins, including albumin, are repurposed to synthesize acute-phase proteins [23] . Concurrently, external stressors impose greater strain on bodily systems [24] , correlating with heightened risks of major diseases such as cancer, cardiovascular disorders, neurodegenerative conditions, malnutrition syndromes, hepatic dysfunctions, and renal pathologies [4,25,26] . These interconnected processes culminate in hypoalbuminemia, which exacerbates physiological responses to critical events, such as surgical interventions or chemotherapy regimens, adversely affecting quality of life and longevity. Globulin is a significant component of serum proteins and encompasses a diverse array of proteins, including enzymes, immunoglobulins, and acute phase proteins [27,28] . It plays a crucial role in disease prognosis because of its association with the inflammatory and immune responses. Investigations into gastric cancer have revealed that low pretreatment serum globulin levels may serve as predictors of favorable prognostic outcomes [29]. Specifically, low globulin concentrations are correlated with improved survival rates, contrasting sharply with the adverse prognostic implications associated with reduced albumin levels [30,31] . In addition to oncology, the involvement of globulin in autoimmune and inflammatory diseases, such as myasthenia gravis (MG), has been examined. This study indicates that elevated globulin levels at admission are independent risk factors for relapse and suboptimal long-term treatment efficacy, suggesting that globulin levels could inform therapeutic strategies for managing autoimmune disorders [32] . The incorporation of globulin into predictive models for disease outcomes highlights its clinical utility and underscores the need to delineate its precise role in the inflammatory cascade and immune modulation. The Albumin-to-Globulin Ratio (AGR), calculated as the ratio of serum levels of albumin relative to globulin concentration, has emerged as a promising prognostic indicator across multiple studies, owing to its reflection of systemic inflammation and nutritional status, particularly in oncology. Previous research has suggested that AGR may be more predictive of mortality than isolated measurements of serum albumin alone [33] . This biomarker has been recognized as an independent predictor of not only mortality, but also disease progression across diverse clinical settings [34-37] . For instance, numerous investigations have shown that a low AGR correlates with poor survival outcomes in various cancer types. Furthermore, its significance extends beyond oncological contexts; studies indicate that a diminished AGR is associated with reduced overall survival (OS) and progression-free survival (PFS). In critically ill patients requiring intensive care, AGR demonstrated a significant correlation with 28-day mortality rates, further establishing its predictive capacity for outcomes in acute life-threatening situations [35] . Additionally, the AGR has been linked to other health conditions, including heart failure, cirrhosis, liver dysfunction, nephrotic syndrome, chronic kidney disease [38] ,as well as chronic inflammation [20] . In conclusion, the AGR represents a cost-effective biomarker for assessing patient prognosis across various diseases; consistently high values correlate positively with favorable prognoses. In the context of cardiovascular health, the AGR is a significant prognostic factor. Cardiovascular diseases, particularly those associated with atherosclerosis and coronary artery events, remain the leading causes of morbidity and mortality worldwide. Increasing evidence suggests that AGR plays a pivotal role in these pathologies and has been proposed as an indicator of systemic inflammation and oxidative stress, both critical factors in the development of atherosclerosis and cardiovascular events. Inflammation is fundamental to the initiation and progression of atherosclerosis, which is characterized as a chronic inflammatory condition affecting vascular structures and is significantly implicated in coronary heart disease (CHD) and acute coronary syndromes (ACS) [39-42] . The contribution of the immune system, particularly through allergic inflammatory cells, further emphasizes the inflammatory nature inherent to coronary artery disease [40,43] . Moreover, the effects of oxidative stress mediated by the antioxidant properties of SA cannot be overlooked. It functions as a crucial antioxidant that protects against oxidative damage prevalent in inflammatory processes and cardiovascular diseases [44] . AGR has emerged as an essential factor influencing prognosis related to cardiovascular events, highlighting its significance when assessing systemic health deterioration [7,45] Sufficient evidence suggests that AGR can serve as a predictor of mortality and adverse events in various cardiovascular conditions, including NSTEMI, heart failure, and post-PCI outcomes. An elevated AGR has been linked to low rates of adverse cardiovascular outcomes, and this relationship remains significant even after adjusting for various confounders [2,6-8,33] . These findings suggest that the AGR could function as a clinical marker for stratifying risk among patients undergoing PCI, thereby identifying those who may benefit from more intensive monitoring or interventional strategies. This finding is consistent with our results. We used several methods to investigate the correlation between the AGR and prognosis. The results showed that AGR may be a useful clinical indicator for predicting the risk of MACE in patients with ACS after PCI. Multivariate Cox proportional hazards model analysis revealed that age, LVEF <40%, and low AGR were the main prognostic factors. Age is a pivotal determinant of both clinical presentation and outcomes of acute coronary syndrome (ACS). The odds of mortality increase nearly exponentially with advancing age [46] . Contemporary research emphasizes a nuanced understanding of cardiovascular aging—termed "vascular age." This concept posits that physiological aging within the cardiovascular system, particularly concerning the coronary vasculature, may more accurately reflect disease burden than chronological age alone [47] . Among patients with a preserved ejection fraction, older cohorts experience higher mortality rates, underscoring the inherent vulnerability associated with advanced age [48] . The existing literature has revealed significant age-related differences in prognosis, presentation patterns, and management intensity. Understanding these mechanisms may illuminate pathways to mitigate age-related risks while optimizing long-term outcomes after PCI. LVEF serves as an important biomarker for classifying the severity of left ventricular dysfunction, influencing therapeutic decisions and predicting prognosis in patients with ACS undergoing PCI [49,50] . Malebranche et al. emphasized the frequent assessment of LV function among patients with ACS, noting that individuals presenting with initially preserved LVEF seldom deteriorate to levels necessitating advanced heart failure interventions [49] . Furthermore, the correlation between reduced LVEF and short-term mortality in patients with ACS experiencing cardiogenic shock was corroborated by Harjola et al. [51] . This suggests that, even in acute settings characterized by pronounced hemodynamic compromise, LVEF retains its prognostic value. The current literature indicates that while LVEF serves as an established predictor of outcomes in ACS, various factors such as patient age, comorbid conditions, and concurrent heart failure modulate its prognostic significance. Addressing these gaps could enhance risk stratification alongside tailored therapeutic interventions, ultimately improving clinical outcomes. In conclusion, the AGR appears to play a significant role in predicting prognostic risk among patients with acute coronary syndrome undergoing percutaneous coronary intervention. Although it shows promise as a biomarker for risk assessment and therapeutic targeting, further research is imperative to validate its clinical utility and elucidate the underlying biological pathways involved. Addressing these gaps could facilitate the development of improved individualized treatment strategies for patients at heightened risk of cardiovascular events due to systemic inflammation and altered protein levels. Study limitations Our study had some limitations. First, our data were obtained from a single center in China, and the sample size was relatively small. Second, the study was retrospective and inherently biased, because electronic medical records may contain incorrect or nonexistent information about individual patients. Third, the cut-off values for the factors included may vary by patient population and may not apply to patients in other countries. Fourth, despite numerous adjustments to the models, residual or unmeasured confounding factors could have influenced the conclusion. Finally, while this study showed a relationship between the AGR and prognostic risk in patients with ACS who underwent PCI, the exact mechanism underlying the correlation between lower AGR values and the risk of MACEs should be further explored. Conclusion The AGR was independently associated with a higher risk of cardiac death and recurrent acute myocardial infarction in patients with ACS who underwent PCI. This indicator combines inflammation, infection, and nutritional factors, and is easy to obtain, convenient, and effective. It may serve as a useful biomarker for identifying patients with ACS at an increased risk of MACEs. Abbreviations ACS Acute coronary syndrome AGR Albumin-to-Globulin ratio AUC Area under the curve CAD Coronary heart disease Cr Creatinine LVEF Left ventricular ejection fraction UA Unstable angina; STEMI ST-segment elevation myocardial infarction Non-STEMI Non-ST-segment elevation myocardial infarction MACEs Major adverse cardiovascular events PCI Percutaneous coronary intervention RCS Restricted cubic spline ROC Receiver operating characteristic Declarations Author Contributions: All the authors contributed to the preparation of the manuscript and approved the final version. Study design: Linlin Wang and Ying Zhang. Acquisition of data: Linlin Wang, Shuang Xie, Aoxue Mei, Xinchen Wang ,Ge Song and Ying Fu. Data analysis and interpretation: Linlin Wang, Xinchen Wang, Shuang Xie and Ying Zhang. Manuscript drafting and critical revision of the manuscript for important intellectual content: Linlin Wang, Lixian Sun and Ying Zhang. Statistical analysis: Linlin Wang, Xinchen Wang, Ge Song and Ying Zhang. Supervision: Ying Zhang. Funding: This study was supported by the Government Funded Clinical Medicine Talent Training Project (grant number ZF2023252 to Dr. Ying Zhang). Institutional Review Board Statement: This study was approved by the Ethics Committee of the Affiliated Hospital of Chengde Medical University (approval Number: CYFYLL2021036) and conducted according to the tenets of the Declaration of Helsinki. Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. Data Availability Statement: All data generated or analysed during this study are included in this published article [and its supplementary information files]. Acknowledgments: The authors would like to thank the doctors and nurses of the Cardiology Research Team at the Affiliated Hospital of Chengde Medical University for their assistance. Conflicts of Interest: The authors declare no conflict of interest. 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Azab, Basem N et al. “Value of the pretreatment albumin to globulin ratio in predicting long-term mortality in breast cancer patients.” American journal of surgery vol. 206,5 (2013): 764-70. doi:10.1016/j.amjsurg.2013.03.007 Wang, Yun-Ting et al. “Low Pretreatment Albumin-to-Globulin Ratios Predict Poor Survival Outcomes in Patients with Head and Neck Cancer: A Systematic Review and Meta-analysis.” Journal of Cancer vol. 14,2 281-289. 9 Jan. 2023, doi:10.7150/jca.80955 Cai, Ying et al. “Prognostic value of the albumin-globulin ratio and albumin-globulin score in patients with multiple myeloma.” The Journal of international medical research vol. 49,3 (2021): 300060521997736. doi:10.1177/0300060521997736 Liu, Bin et al. “Albumin-Globulin Ratio Is an Independent Determinant of 28-Day Mortality in Patients with Critical Illness.” Disease markers vol. 2021 9965124. 25 Aug. 2021, doi:10.1155/2021/9965124 Suh, B et al. “Low albumin-to-globulin ratio associated with cancer incidence and mortality in generally healthy adults.” Annals of oncology : official journal of the European Society for Medical Oncology vol. 25,11 (2014): 2260-2266. doi:10.1093/annonc/mdu274 Park, Jane et al. “Predictive value of serum albumin-to-globulin ratio for incident chronic kidney disease: A 12-year community-based prospective study.” PloS one vol. 15,9 e0238421. 2 Sep. 2020, doi:10.1371/journal.pone.0238421 Soeki, Takeshi, and Masataka Sata. “Inflammatory Biomarkers and Atherosclerosis.” International heart journal vol. 57,2 (2016): 134-9. doi:10.1536/ihj.15-346 Niccoli, Giampaolo et al. “Role of Allergic Inflammatory Cells in Coronary Artery Disease.” Circulation vol. 138,16 (2018): 1736-1748. doi:10.1161/CIRCULATIONAHA.118.035400 Golia, Enrica et al. “Inflammation and cardiovascular disease: from pathogenesis to therapeutic target.” Current atherosclerosis reports vol. 16,9 (2014): 435. doi:10.1007/s11883-014-0435-z Tate, Amit R, and Gundu H R Rao. “Inflammation: Is It a Healer, Confounder, or a Promoter of Cardiometabolic Risks?.” Biomolecules vol. 14,8 948. 6 Aug. 2024, doi:10.3390/biom14080948 Ozben, Beste, and Okan Erdogan. “The role of inflammation and allergy in acute coronary syndromes.” Inflammation & allergy drug targets vol. 7,3 (2008): 136-44. doi:10.2174/187152808785748128 Makoto Anraku, et al."Redox properties of serum albumin."BBA - General Subjects 1830.12(2013):5465-5472. doi: 0.1016/j.bbagen.2013.04.036. Beamer, N et al. “Fibrinogen and the albumin-globulin ratio in recurrent stroke.” Stroke vol. 24,8 (1993): 1133-9. doi:10.1161/01.str.24.8.1133 Rosengren, Annika et al. “Age, clinical presentation, and outcome of acute coronary syndromes in the Euroheart acute coronary syndrome survey.” European heart journal vol. 27,7 (2006): 789-95. doi:10.1093/eurheartj/ehi774 Cuocolo, Alberto et al. “Coronary vascular age comes of age.” Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology vol. 24,6 (2017): 1835-1836. doi:10.1007/s12350-017-1078-6 Kwok, Chun Shing et al. “Effect of age on the prognostic value of left ventricular function in patients with acute coronary syndrome: A prospective registry study.” European heart journal. Acute cardiovascular care vol. 6,2 (2017): 191-198. doi:10.1177/2048872615623038 Malebranche, Daniel et al. “Patterns of Left-Ventricular Function Assessment in Patients With Acute Coronary Syndromes.” CJC open vol. 3,6 733-740. 1 Feb. 2021, doi:10.1016/j.cjco.2020.12.028 Khaled, Sheeren, and Rajaa Matahen. “Cardiovascular risk factors profile in patients with acute coronary syndrome with particular reference to left ventricular ejection fraction.” Indian heart journal vol. 70,1 (2018): 45-49. doi:10.1016/j.ihj.2017.05.019 Harjola, Veli-Pekka et al. “Clinical picture and risk prediction of short-term mortality in cardiogenic shock.” European journal of heart failure vol. 17,5 (2015): 501-9. doi:10.1002/ejhf.260. Additional Declarations No competing interests reported. Supplementary Files AGR.xlsx Cite Share Download PDF Status: Published Journal Publication published 18 Jul, 2025 Read the published version in BMC Cardiovascular Disorders → Version 1 posted Editorial decision: Revision requested 17 Jun, 2025 Reviews received at journal 16 Jun, 2025 Reviewers agreed at journal 16 Jun, 2025 Reviews received at journal 06 Jun, 2025 Reviewers agreed at journal 06 Jun, 2025 Reviewers invited by journal 28 May, 2025 Editor invited by journal 26 May, 2025 Editor assigned by journal 24 May, 2025 Submission checks completed at journal 24 May, 2025 First submitted to journal 21 May, 2025 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. 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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-6715881","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":463184459,"identity":"8805431a-2594-4893-a4a4-b1dc8f9e0d12","order_by":0,"name":"Linlin Wang","email":"","orcid":"","institution":"The Affiliated Hospital of Chengde Medical University","correspondingAuthor":false,"prefix":"","firstName":"Linlin","middleName":"","lastName":"Wang","suffix":""},{"id":463184460,"identity":"1be8b2d2-e1f2-4367-9251-c4e253deb8a4","order_by":1,"name":"Shuang Xie","email":"","orcid":"","institution":"The Affiliated Hospital of Chengde Medical 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Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuUlEQVRIiWNgGAWjYFACxgdAwoaHn72BaC3MBkAiTUay5wBpWg7bGNxwIFID/4xkNsmfbed5GG4wMH74mEOEFokbyWwSkm23eRhnNzBLztxGhBYDifxjEoZALcwyB9iYeYnTArQlse0cD5tEAilaDrYd4OEhWovEmcfMlg3nknkkeA42E+cX/vZkxps/yuzs7Y83H/zwkRgtDAIJLBKMbCAWYwMx6kHWHGD+wPCHSMWjYBSMglEwMgEAObQyWtnT+VwAAAAASUVORK5CYII=","orcid":"","institution":"The Affiliated Hospital of Chengde Medical University","correspondingAuthor":true,"prefix":"","firstName":"Ying","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2025-05-21 11:08:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6715881/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6715881/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12872-025-04983-3","type":"published","date":"2025-07-18T16:05:08+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":83772782,"identity":"7401c61e-0070-4955-937d-f602af0ab104","added_by":"auto","created_at":"2025-06-02 13:01:13","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":27525,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver Operating Characteristic (ROC) Curve.\u003c/p\u003e\n\u003cp\u003eAbbreviations: AGR, albumin-to-globulin ratio.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6715881/v1/2d487d3cc4df16284938966f.png"},{"id":83773420,"identity":"7bdbd849-1382-4f06-aba0-5cb92da33200","added_by":"auto","created_at":"2025-06-02 13:09:13","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":28488,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan–Meier curves of cumulative survival by AGR in ACS patients undergoing PCI (log-rank p = 0.008).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6715881/v1/018f2e93de945058fe59c352.png"},{"id":83772781,"identity":"556a7575-cfa4-47f0-ac2d-cae7595a2c5e","added_by":"auto","created_at":"2025-06-02 13:01:13","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":54743,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(A) \u003c/strong\u003eTime-dependent receiver operating characteristic plotted by R. \u003cstrong\u003eFigure 3(B)\u003c/strong\u003eAUC tends to increase with time.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6715881/v1/cf0d6761187b281e9a683c6f.png"},{"id":83772091,"identity":"4983233b-e194-434c-b7fe-69fc85506cd4","added_by":"auto","created_at":"2025-06-02 12:53:13","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":32490,"visible":true,"origin":"","legend":"\u003cp\u003eForest graphs according to Cox proportional hazards regression model to test the risk factors for MACEs.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6715881/v1/664d512967af72adc895d521.png"},{"id":83772094,"identity":"9c3ea190-2b0a-4508-9849-a9b165d7d51e","added_by":"auto","created_at":"2025-06-02 12:53:13","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":23463,"visible":true,"origin":"","legend":"\u003cp\u003eRestricted cubic spline (RCS).\u003c/p\u003e\n\u003cp\u003eAbbreviations: MACEs, major adverse cardiovascular events; AGR, albumin-to-globulin ratio;\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6715881/v1/294f08b26564fb1f4a4a9bfe.png"},{"id":88506083,"identity":"69c106fc-6fe7-48d3-8381-8530c92a43dd","added_by":"auto","created_at":"2025-08-07 07:30:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1091717,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6715881/v1/7b1b0176-88aa-455d-9840-d19746986bfa.pdf"},{"id":83772097,"identity":"363698ec-4705-4fa2-88aa-8d010128c970","added_by":"auto","created_at":"2025-06-02 12:53:14","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":479409,"visible":true,"origin":"","legend":"","description":"","filename":"AGR.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6715881/v1/1fa3671e6037b1d5ffa57483.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Albumin-to-Globulin ratio as an independent risk factor for predicting prognostic risk in patients with acute coronary syndrome undergoing percutaneous coronary intervention","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAcute coronary syndromes (ACS) encompass a range of conditions associated with suddenly reduced blood flow to the heart, including ST-segment elevation myocardial infarction (STEMI), non-ST-segment elevation myocardial infarction (NSTEMI), and unstable angina (UA). ACS continue to be the leading cause of mortality worldwide despite significant advancements in medical science \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe albumin-to-globulin ratio (AGR) has recently garnered attention as an important prognostic biomarker of cardiovascular and other systemic diseases. The serum albumin-to-globulin ratio can reflect the inflammatory status and nutritional condition of patients, providing insights into disease prognosis and potential therapeutic outcomes \u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Previous studies highlighted its prognostic significance in various malignancies. Meta-analyses and systematic reviews have corroborated that a low pretreatment AGR correlates with poor overall survival and a high incidence of lymph node metastasis, making it a versatile biomarker across disciplines \u003csup\u003e[\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. This cross-disciplinary applicability also underscores the fundamental biological pathways shared between systemic inflammation and disease progression, reinforcing the potential of the AGR as a multifaceted prognostic tool. Moreover, low AGR levels have been associated with worse clinical outcomes in cardiovascular diseases, including heart failure (HF), high incidences of in-stent restenosis, and revascularization events after percutaneous coronary intervention (PCI) \u003csup\u003e[\u003cspan additionalcitationids=\"CR3 CR4 CR5 CR6 CR7\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. However, while substantial evidence supports a correlation between low AGR and adverse health outcomes, the ability of the AGR to predict prognosis of patients with ACS undergoing PCI remains unknown. We aimed to further delineate the prognostic role of the AGR in predicting the risk of MACEs in patients with ACS undergoing PCI.\u003c/p\u003e"},{"header":"Material and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Population\u003c/h2\u003e \u003cp\u003eIn the present study, 1408 patients with ACS who underwent PCI between January 2016 and December 2018 at the Department of Cardiology, The Affiliated Hospital of Chengde Medical University (Hebei, China) were consecutively enrolled.\u003c/p\u003e \u003cp\u003eAll patients underwent coronary angiography and PCI. The procedures were performed by an experienced cardiologist. The inclusion criteria were as follows: (1) age\u0026thinsp;\u0026ge;\u0026thinsp;18 years; (2) diagnosis of clinical types of ACS, including STEMI, NSTEMI, and UA, according to the 2013 ACCF/AHA Guideline for the Management of ST-Elevation Myocardial Infarction and 2014 AHA/ACC Guideline for the Management of Patients with Non-ST-Elevation Acute Coronary Syndromes; (3) stenosis of at least 50% of the luminal diameter in at least one major coronary artery branch after coronary angiography.\u003c/p\u003e \u003cp\u003eThe exclusion criteria were as follows: (1) death during hospitalization; (2) critical structural heart disease; (3) severe inflammatory infectious disease; (4) connective tissue disease; (5) secondary coronary vasculitis; (5) presence of other heart diseases causing angina pectoris, such as hypertrophic cardiomyopathy, myocarditis, and; severe valvular heart disease; (6) severe liver and kidney disease (creatinine clearance\u0026thinsp;\u0026lt;\u0026thinsp;15 ml/min)), (7) missing data exceeding 10%; (8) history of CABG, etc.\u003c/p\u003e \u003cp\u003e This study was approved by the Ethics Committee of the Affiliated Hospital of Chengde Medical University (approval Number: CYFYLL2021036) and conducted according to the tenets of the Declaration of Helsinki. All the participants provided informed consent.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eBaseline Demographics and Clinical Characteristics\u003c/h3\u003e\n\u003cp\u003eThe cardiovascular research team collected all the demographic data and clinical characteristics of the enrolled patients. Hypertension was defined as a systolic blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;140 mmHg and/or diastolic blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;90 mmHg at rest or a previous diagnosis of hypertension during antihypertensive therapy \u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. Type 2 diabetes mellitus (DM) was de-fined as diabetes symptoms with random blood glucose level\u0026thinsp;\u0026ge;\u0026thinsp;11.1 mmol/L, fasting plasma glucose level\u0026thinsp;\u0026ge;\u0026thinsp;7.0 mmol/L, or 2-h oral glucose tolerance test level\u0026thinsp;\u0026ge;\u0026thinsp;11.1 mmol/L or as no diabetes symptoms with at least twice the blood glucose level meeting the abovementioned criteria \u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. Dyslipidemia was defined as serum total cholesterol level\u0026thinsp;\u0026ge;\u0026thinsp;5.18 mmol/L, high-density lipoprotein cholesterol (HDL-C) level\u0026thinsp;\u0026le;\u0026thinsp;1.04 mmol/L, low-density lipoprotein cholesterol (LDL-C) level\u0026thinsp;\u0026ge;\u0026thinsp;3.37 mmol/L, or triglyceride level\u0026thinsp;\u0026ge;\u0026thinsp;1.7 mmol/L, or a previous diagnosis of dyslipidemia in medication \u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\n\u003ch3\u003eFollow-Up and Endpoints\u003c/h3\u003e\n\u003cp\u003eFollow-up visits were completed by cardiovascular physicians, and each follow-up adhered to the principle of standardization to control bias. Follow-up data were collected via clinical visits at 1, 3, 6, and 12 months, and annually thereafter. The primary study endpoints were MACEs, including cardiac death and recurrent acute myocardial infarction (AMI).\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eThe Kolmogorov-Smirnov test was employed to assess the normality of continuous variables, with normally distributed and non-normally distributed variables reported as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation and median with interquartile range, respectively. Differences in non-normally distributed continuous variables between the MACEs and non-MACE groups were analyzed using the Mann-Whitney U-test. Categorical variables were expressed as counts (%) and compared using the χ\u0026sup2; test. The Kaplan-Meier method was used to evaluate the incidence of adverse cardiovascular events across groups, while the log-rank test was used to assess differences between the two groups. Receiver operating characteristic (ROC) curves were used to determine the diagnostic value of AGR, with Youden's index (sensitivity\u0026thinsp;+\u0026thinsp;specificity \u0026minus;\u0026thinsp;1) employed to identify the optimal cutoff point. Additionally, time-dependent ROC curves were used to examine temporal correlations. The dose-response relationship between the AGR and MACEs in patients with ACS undergoing PCI was illustrated using restricted cubic spline (RCS) curves. Univariate and multivariate Cox proportional hazards models were used to evaluate predictive capacity of AGR for prognostic risk.\u003c/p\u003e \u003cp\u003eAll statistical analyses were performed using SPSS (version 26, SPSS Inc., Chicago, IL, USA), GraphPad Prism 8.0 (GraphPad Software Inc., La Jolla, CA, USA), and R version 4.3.3. Statistical significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003ePatients\u0026rsquo; Characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOf the 1408 enrolled patients with ACS, 45 were excluded: 4 patients with ACS who had infectious diseases, 2 patients with ACS who had blood system diseases, and 3 patients with AMI who had malignant tumors. Furthermore, 36 patients with ACS lost to follow-up. Ultimately, 1363 patients with ACS who responded during the follow-up period and were included in the analysis, with a median follow-up duration of 1123 days (3.0 years).\u003c/p\u003e\n\u003cp\u003eAmong the 1363 patients, 49 experienced MACEs, 26 died, and 23 required rehospitalization for AMI recurrence. \u003cstrong\u003eTable 1\u003c/strong\u003e shows the baseline characteristics of the patients with ACS who underwent PCI in the MACEs and non-MACE groups. The MACEs group and non-MACEs group showed significant differences in the proportion of patients in terms of age (62.59\u0026plusmn;8.02 vs 58.66\u0026plusmn;10.22), family history of CAD (2 [4.1%] vs 193 [14.7%]), the WBC count (9.79\u0026plusmn;3.84 vs 8.65 \u0026plusmn;3.36), creatine kinase MB (CK-MB) (38.00 [14.00\u0026ndash;193.96] vs 16.00 [10.00\u0026ndash;47.94]), left ventricular ejection fraction (LVEF) (53.00\u0026plusmn;10.36 vs 57.44 \u0026plusmn;8.83), unstable angina (UA) (12 [24.5%] vs 568 [43.2%]), ST-segment elevation myocardial infarction (STEMI) (30 [61.2%] vs 548 [41.7%], and AGR (1.29\u0026plusmn;0.18 vs 1.38 \u0026plusmn; 0.19) (all p\u0026lt;0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Baseline patient characteristics of the MACEs and non-MACEs groups\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"595\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003eMACEs group\u003c/p\u003e\n \u003cp\u003e（n=49）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eNon-MACEs group\u003c/p\u003e\n \u003cp\u003e（n=1314）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026chi;2/Z\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eDemographic\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eMale\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e34 (69.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e981 (74.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.690\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.406\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eAge (years)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e62.59 \u0026plusmn;8.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e58.66 \u0026plusmn;10.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e-2.753\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eDyslipidemia\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e27 (55.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e754 (57.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.751\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eHypertension\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e26 (53.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e792 (60.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e1.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.312\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eDiabetes mellitus\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e14 (28.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e333 (25.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.610\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eIschemic stroke\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e11 (22.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e189 (14.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e2.454\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.117\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eSmoking\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e23 (46.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e677 (51.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.397\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.529\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eFamily history of CAD\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e2 (4.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e193 (14.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e4.335\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eLaboratory data\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eWBC (10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e9.79 \u0026plusmn;3.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e8.65\u0026plusmn;3.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e-2.259\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eHGB (g/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e144.57\u0026plusmn;14.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e146.16\u0026plusmn;15.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e-0.786\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.432\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003ePLT (10\u003csup\u003e9\u003c/sup\u003e/L)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e194.57\u0026plusmn;68.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e216.86\u0026plusmn;63.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e-1.878\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.060\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eTC (mmol/L)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e4.59\u0026plusmn;1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e4.42\u0026plusmn;1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e-0.995\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.320\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eTG (mmol/L)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e1.43 (0.85,2.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e1.60 (1.02,2.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e-1.234\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.217\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eHDL-C (mmol/L)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e1.17\u0026plusmn;0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e1.12\u0026plusmn;0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e-1.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.315\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eLDL-C (mmol/L)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e2.49\u0026plusmn;0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e2.41\u0026plusmn;0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e-0.871\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.384\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eCK-MB (U/L)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e38.00 (14.00,193.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e16.00 (10.00,48.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e-3.489\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eCr (umol/L)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e70.34\u0026plusmn;16.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e70.29\u0026plusmn;28.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e-0.490\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.624\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eUA (umol/L)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e333.26\u0026plusmn;80.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e330.79\u0026plusmn;92.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e-0.668\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.504\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eTP (g/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e69.49 \u0026plusmn;7.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e71.06 \u0026plusmn;6.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e-1.913\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eALB (g/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e38.86 \u0026plusmn;3.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e40.96 \u0026plusmn;3.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e-3.794\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eAGR\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e1.29\u0026plusmn;0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e1.38\u0026plusmn;0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e-2.843\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eLVEF\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e53.00\u0026plusmn;10.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e57.44\u0026plusmn;8.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e-2.957\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eClinical classification of ACS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eUA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e12 (24.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e568 (43.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e6.784\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eSTEMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e30 (61.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e548 (41.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e7.370\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eNon-STEMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e7 (14.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e198 (15.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.880\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eCoronary angiography\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003e1 vessel lesion\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e12 (24.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e420 (32.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.735\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.391\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003e2 vessels lesion\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e12 (24.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e422 (32.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e0.147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.701\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003e3 vessels lesion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e25 (51.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e472 (35.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e1.441\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.230\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eNote\u003c/strong\u003e: Data are presented as n (%) or as the median [range].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations\u003c/strong\u003e: MACEs, major adverse cardiovascular events; CAD, coronary artery disease; WBC, white blood cell;\u0026nbsp;HGB, Hemoglobin; PLT,\u0026nbsp;Platelet;\u0026nbsp;TC, total cholesterol; TG, triglyceride; HDL‐C, high‐density lipoprotein cholesterol; LDL‐C, low density lipoprotein cholesterol; CK-MB, creatine kinase MB; Cr, creatinine; UA, uric acid; TP, total protein;\u0026nbsp;ALB, albumin; AGR,\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003ealbumin to globulin ratio; LVEF, left ventricular ejection fraction; UA, unstable angina; STEMI, ST-segment elevation myocardial infarction; Non-STEMI, non-ST-segment elevation myocardial infarction.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eReceiver Operating Characteristic (ROC) Curve, Survival Analysis ,and Time-Dependent ROC.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe ROC curve was plotted to test the ability of AGR to predict MACEs. The AUC for the AGR was 0.619 (p =0.004, 95% confidence interval [CI]: 0.542\u0026ndash;0.697) (Figure 1). Based on Youden\u0026rsquo;s index, the optimal diagnostic cut-off value for AGR was 1.350, with a sensitivity of 69.40% and a specificity of 53.50%. Therefore, patients with ACS were divided into low AGR (\u0026lt;1.350) and high AGR (\u0026ge;1.350) groups based on the cut-off values.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe Kaplan\u0026ndash;Meier curve (\u003cstrong\u003eFigure 2\u003c/strong\u003e\u003cstrong\u003e)\u003c/strong\u003e showed that the low AGR group had significantly lower cumulative survival than the high AGR group (log-rank p = 0.008).\u003c/p\u003e\n\u003cp\u003eNext, we performed a time-dependent ROC analysis to test the changes in prediction ability over time. \u003cstrong\u003eFigure 3\u003c/strong\u003e\u003cstrong\u003eA\u003c/strong\u003e shows time-dependent ROC curves. The 1-, 2-, 3-, and 5-year AUCs were 0.607, 0.624, 0.617, and 0.610, respectively. As shown in \u003cstrong\u003eFigure 3\u003c/strong\u003e\u003cstrong\u003eB\u003c/strong\u003e, the time-dependent AUC indicated that the AGR had similar predictive power for MACE incidence at different time periods. AGR showed good ability to predict MACEs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUnivariate and Multivariate Cox Hazard Proportional Models\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs shown in \u003cstrong\u003eTable 1\u003c/strong\u003e, age, family history of CAD, WBC count, CK-MB level, AGR, LVEF, UA level, and STEMI showed significant differences. We converted continuous variables into categorical variables and performed univariate COX analysis. The results showed significant differences (all p\u0026lt;0.05) (\u003cstrong\u003eTable 2\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eAdditionally, we considered the clinical prognostic significance of the factors mentioned in the univariate COX hazard proportional analysis and age, LVEF\u0026lt;40%, and AGR\u0026lt;1.350 in multivariate Cox hazard proportional models. The results were age HR:2.689 (95%CI:1.288-5.615, p=0.008); LVEF\u0026lt;40%, HR: 3.527, (95%CI: 1.357\u0026ndash;9.164, p=0.010) and AGR\u0026lt;1.350, HR: 2.180, (95%CI: 1.078\u0026ndash;4.407, p=0.030) \u003cstrong\u003e(\u003c/strong\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eFigure 4\u003c/strong\u003e). Therefore, an AGR\u0026lt;1.350 was correlated with the risk of MACEs in patients with ACS.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Univariate Cox Hazard Proportional Model for Predictive Factors of MACEs\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eUnivariate HR\u003c/p\u003e\n \u003cp\u003e(95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eAge\u0026nbsp;\u0026ge;60 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003e2.089 (1.150\u0026ndash;3.796)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eFamily history of CAD\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003e0.242 (0.059\u0026ndash;0.996)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003e0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eWBC\u0026ge;10 10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003e2.134 (1.219\u0026ndash;3.736)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eCK-MB\u0026ge;32 U/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003e2.495 (1.411\u0026ndash;4.410)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eLVEF\u0026lt;40%\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003e5.055 (1.977\u0026ndash;12.928)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eAGR \u0026lt;1.350\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003e2.472 (1.346\u0026ndash;4.540)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eUA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e0.421 (0.219\u0026ndash;0.807)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eSTEMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e2.224 (1.252\u0026ndash;3.952)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Multivariate Cox Hazard Proportional Model for Predictive Factors of MACEs\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eMultivariate HR\u003c/p\u003e\n \u003cp\u003e(95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eAge category\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003e2.689 (1.288\u0026ndash;5.615)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eLVEF\u0026lt;40%\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003e3.527 (1.357\u0026ndash;9.164)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003eAGR \u0026lt;1.350\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003e2.180 (1.078\u0026ndash;4.407)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 180px;\"\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations\u003c/strong\u003e: MACEs, major adverse cardiovascular events; CAD, coronary artery disease; WBC, white blood cell; CK-MB, creatine kinase MB; LVEF, left ventricular ejection fraction; AGR, albumin to globulin ratio; UA, unstable angina; STEMI, ST-segment elevation myocardial infarction.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRestricted Cubic Spline\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;(RCS)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe also visualized the correlation between the AGR and MACEs using the RCS. A low AGR was associated with an increased risk of MACEs (p =0.009 and p =0.864;\u0026nbsp;\u003cstrong\u003eFigure 5\u003c/strong\u003e). The plots show that decreasing AGR levels correlated with increasing risk of MACEs. Thus, a low AGR was an independent risk factor for patients with ACS who underwent PCI.\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn the present study, we investigated the predictive ability of the AGR for prognostic risk in patients with ACS undergoing PCI. The main findings of our study were as follows: (1) low AGR was associated with poor prognosis and was an independent risk factor for PCI in ACS patients; (2) patients with ACS who underwent PCI had lower AGR and cumulative survival rate than those in controls; (3) the diagnostic efficiency of AGR was relatively stable and still has value with the increase in time; (4) AGR was a good predictor of mortality in ACS patients after PCI; and (5) low AGR was significantly associated with age and lower LVEF. To our knowledge, this is the first study to analyze the correlation between this novel AGR index and prognosis in patients with ACS who underwent PCI.\u003c/p\u003e\n\u003cp\u003eAlbumin, which is synthesized in the liver, functions as a negative acute-phase reactant and is an accessible and reliable biomarker of the basal metabolome. It is also a significant indicator of inflammation, infection, and nutritional status \u003csup\u003e[15-17]\u003c/sup\u003e. Emerging evidence underscores hypoalbuminemia, characterized by low serum albumin levels, as a critical prognostic marker for various diseases. A study involving 438 patients with acutely decompensated heart failure (ADHF) demonstrated that low serum albumin concentrations correlate with an increased risk of mortality in this cohort \u003csup\u003e[18]\u003c/sup\u003e. Reduced serum albumin levels are associated with adverse clinical outcomes. A comprehensive analysis of 1,070 cancer patients showed a strong association between hypoalbuminemia and both venous thromboembolism (VTE) and mortality \u003csup\u003e[19]\u003c/sup\u003e. The relationship between hypoalbuminemia and disease outcomes can be partially attributed to the involvement of albumin in inflammatory processes that enhance capillary permeability, facilitating the translocation of albumin into the interstitial space while increasing its distribution volume. Furthermore, inflammation diminishes the half-life of albumin, resulting in a decrease in its total mass, despite elevated synthesis rates \u003csup\u003e[4]\u003c/sup\u003e. As inflammatory responses intensify, serum albumin levels decline significantly, indicating that physiological reactions to inflammation substantially reduce circulating albumin concentrations \u003csup\u003e[21]\u003c/sup\u003e. Moreover, age-related reductions in liver volume and blood flow \u003csup\u003e[22]\u003c/sup\u003e contribute to a decrease in albumin production over time. Additionally, research has indicated that during immune responses associated with disease states, amino acids from available proteins, including albumin, are repurposed to synthesize acute-phase proteins \u003csup\u003e[23]\u003c/sup\u003e. Concurrently, external stressors impose greater strain on bodily systems \u003csup\u003e[24]\u003c/sup\u003e, correlating with heightened risks of major diseases such as cancer, cardiovascular disorders, neurodegenerative conditions, malnutrition syndromes, hepatic dysfunctions, and renal pathologies \u003csup\u003e[4,25,26]\u003c/sup\u003e. These interconnected processes culminate in hypoalbuminemia, which exacerbates physiological responses to critical events, such as surgical interventions or chemotherapy regimens, adversely affecting quality of life and longevity.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGlobulin is a significant component of serum proteins and encompasses a diverse array of proteins, including enzymes, immunoglobulins, and acute phase proteins \u003csup\u003e[27,28]\u003c/sup\u003e. It plays a crucial role in disease prognosis because of its association with the inflammatory and immune responses. Investigations into gastric cancer have revealed that low pretreatment serum globulin levels may serve as predictors of favorable prognostic outcomes \u003csup\u003e[29].\u003c/sup\u003e Specifically, low globulin concentrations are correlated with improved survival rates, contrasting sharply with the adverse prognostic implications associated with reduced albumin levels\u003csup\u003e\u0026nbsp;[30,31]\u003c/sup\u003e. In addition to oncology, the involvement of globulin in autoimmune and inflammatory diseases, such as myasthenia gravis (MG), has been examined. This study indicates that elevated globulin levels at admission are independent risk factors for relapse and suboptimal long-term treatment efficacy, suggesting that globulin levels could inform therapeutic strategies for managing autoimmune disorders \u003csup\u003e[32]\u003c/sup\u003e. The incorporation of globulin into predictive models for disease outcomes highlights its clinical utility and underscores the need to delineate its precise role in the inflammatory cascade and immune modulation.\u003c/p\u003e\n\u003cp\u003eThe Albumin-to-Globulin Ratio (AGR), calculated as the ratio of serum levels of albumin relative to globulin concentration, has emerged as a promising prognostic indicator across multiple studies, owing to its reflection of systemic inflammation and nutritional status, particularly in oncology. Previous research has suggested that AGR may be more predictive of mortality than isolated measurements of serum albumin alone \u003csup\u003e[33]\u003c/sup\u003e. This biomarker has been recognized as an independent predictor of not only mortality, but also disease progression across diverse clinical settings \u003csup\u003e[34-37]\u003c/sup\u003e. For instance, numerous investigations have shown that a low AGR correlates with poor survival outcomes in various cancer types. Furthermore, its significance extends beyond oncological contexts; studies indicate that a diminished AGR is associated with reduced overall survival (OS) and progression-free survival (PFS). In critically ill patients requiring intensive care, AGR demonstrated a significant correlation with 28-day mortality rates, further establishing its predictive capacity for outcomes in acute life-threatening situations\u003csup\u003e\u0026nbsp;[35]\u003c/sup\u003e. Additionally, the AGR has been linked to other health conditions, including heart failure, cirrhosis, liver dysfunction, nephrotic syndrome, chronic kidney disease \u003csup\u003e[38]\u003c/sup\u003e,as well as chronic inflammation \u003csup\u003e[20]\u003c/sup\u003e. In conclusion, the AGR represents a cost-effective biomarker for assessing patient prognosis across various diseases; consistently high values correlate positively with favorable prognoses.\u003c/p\u003e\n\u003cp\u003eIn the context of cardiovascular health, the AGR is a significant prognostic factor.\u003c/p\u003e\n\u003cp\u003eCardiovascular diseases, particularly those associated with atherosclerosis and coronary artery events, remain the leading causes of morbidity and mortality worldwide. Increasing evidence suggests that AGR plays a pivotal role in these pathologies and has been proposed as an indicator of systemic inflammation and oxidative stress, both critical factors in the development of atherosclerosis and cardiovascular events. Inflammation is fundamental to the initiation and progression of atherosclerosis, which is characterized as a chronic inflammatory condition affecting vascular structures and is significantly implicated in coronary heart disease (CHD) and acute coronary syndromes (ACS) \u003csup\u003e[39-42]\u003c/sup\u003e. The contribution of the immune system, particularly through allergic inflammatory cells, further emphasizes the inflammatory nature inherent to coronary artery disease \u003csup\u003e[40,43]\u003c/sup\u003e. Moreover, the effects of oxidative stress mediated by the antioxidant properties of SA cannot be overlooked. It functions as a crucial antioxidant that protects against oxidative damage prevalent in inflammatory processes and cardiovascular diseases \u003csup\u003e[44]\u003c/sup\u003e. AGR has emerged as an essential factor influencing prognosis related to cardiovascular events, highlighting its significance when assessing systemic health deterioration \u003csup\u003e[7,45]\u003c/sup\u003e Sufficient evidence suggests that AGR can serve as a predictor of mortality and adverse events in various cardiovascular conditions, including NSTEMI, heart failure, and post-PCI outcomes. An elevated AGR has been linked to low rates of adverse cardiovascular outcomes, and this relationship remains significant even after adjusting for various confounders \u003csup\u003e[2,6-8,33]\u003c/sup\u003e. These findings suggest that the AGR could function as a clinical marker for stratifying risk among patients undergoing PCI, thereby identifying those who may benefit from more intensive monitoring or interventional strategies.\u003c/p\u003e\n\u003cp\u003eThis finding is consistent with our results. We used several methods to investigate the correlation between the AGR and prognosis. The results showed that AGR may be a useful clinical indicator for predicting the risk of MACE in patients with ACS after PCI. Multivariate Cox proportional hazards model analysis revealed that age, LVEF \u0026lt;40%, and low AGR were the main prognostic factors.\u003c/p\u003e\n\u003cp\u003eAge is a pivotal determinant of both clinical presentation and outcomes of acute coronary syndrome (ACS). The odds of mortality increase nearly exponentially with advancing age \u003csup\u003e[46]\u003c/sup\u003e. Contemporary research emphasizes a nuanced understanding of cardiovascular aging\u0026mdash;termed \u0026quot;vascular age.\u0026quot; This concept posits that physiological aging within the cardiovascular system, particularly concerning the coronary vasculature, may more accurately reflect disease burden than chronological age alone \u003csup\u003e[47]\u003c/sup\u003e. Among patients with a preserved ejection fraction, older cohorts experience higher mortality rates, underscoring the inherent vulnerability associated with advanced age \u003csup\u003e[48]\u003c/sup\u003e. The existing literature has revealed significant age-related differences in prognosis, presentation patterns, and management intensity. Understanding these mechanisms may illuminate pathways to mitigate age-related risks while optimizing long-term outcomes after PCI.\u003c/p\u003e\n\u003cp\u003eLVEF serves as an important biomarker for classifying the severity of left ventricular dysfunction, influencing therapeutic decisions and predicting prognosis in patients with ACS undergoing PCI \u003csup\u003e[49,50]\u003c/sup\u003e. Malebranche et al. emphasized the frequent assessment of LV function among patients with ACS, noting that individuals presenting with initially preserved LVEF seldom deteriorate to levels necessitating advanced heart failure interventions\u003csup\u003e\u0026nbsp;[49]\u003c/sup\u003e. Furthermore, the correlation between reduced LVEF and short-term mortality in patients with ACS experiencing cardiogenic shock was corroborated by Harjola et al. \u003csup\u003e[51]\u003c/sup\u003e. This suggests that, even in acute settings characterized by pronounced hemodynamic compromise, LVEF retains its prognostic value. The current literature indicates that while LVEF serves as an established predictor of outcomes in ACS, various factors such as patient age, comorbid conditions, and concurrent heart failure modulate its prognostic significance. Addressing these gaps could enhance risk stratification alongside tailored therapeutic interventions, ultimately improving clinical outcomes.\u003c/p\u003e\n\u003cp\u003eIn conclusion, the AGR appears to play a significant role in predicting prognostic risk among patients with acute coronary syndrome undergoing percutaneous coronary intervention. Although it shows promise as a biomarker for risk assessment and therapeutic targeting, further research is imperative to validate its clinical utility and elucidate the underlying biological pathways involved. Addressing these gaps could facilitate the development of improved individualized treatment strategies for patients at heightened risk of cardiovascular events due to systemic inflammation and altered protein levels.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy limitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur study had some limitations. First, our data were obtained from a single center in China, and the sample size was relatively small. Second, the study was retrospective and inherently biased, because electronic medical records may contain incorrect or nonexistent information about individual patients. Third, the cut-off values for the factors included may vary by patient population and may not apply to patients in other countries. Fourth, despite numerous adjustments to the models, residual or unmeasured confounding factors could have influenced the conclusion. Finally, while this study showed a relationship between the AGR and prognostic risk in patients with ACS who underwent PCI, the exact mechanism underlying the correlation between lower AGR values and the risk of MACEs should be further explored.\u0026nbsp;\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe AGR was independently associated with a higher risk of cardiac death and recurrent acute myocardial infarction in patients with ACS who underwent PCI. This indicator combines inflammation, infection, and nutritional factors, and is easy to obtain, convenient, and effective. It may serve as a useful biomarker for identifying patients with ACS at an increased risk of MACEs.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"524\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eACS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 420px;\"\u003e\n \u003cp\u003eAcute coronary syndrome\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eAGR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 420px;\"\u003e\n \u003cp\u003eAlbumin-to-Globulin ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eAUC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 420px;\"\u003e\n \u003cp\u003eArea under the curve\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eCAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 420px;\"\u003e\n \u003cp\u003eCoronary heart disease\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eCr\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 420px;\"\u003e\n \u003cp\u003eCreatinine\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eLVEF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 420px;\"\u003e\n \u003cp\u003eLeft ventricular ejection fraction\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eUA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 420px;\"\u003e\n \u003cp\u003eUnstable angina;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eSTEMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 420px;\"\u003e\n \u003cp\u003eST-segment elevation myocardial infarction\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eNon-STEMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 420px;\"\u003e\n \u003cp\u003eNon-ST-segment elevation myocardial infarction\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eMACEs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 420px;\"\u003e\n \u003cp\u003eMajor adverse cardiovascular events\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003ePCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 420px;\"\u003e\n \u003cp\u003ePercutaneous coronary intervention\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eRCS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 420px;\"\u003e\n \u003cp\u003eRestricted cubic spline\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eROC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 420px;\"\u003e\n \u003cp\u003eReceiver operating characteristic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u003c/strong\u003e All the authors contributed to the preparation of the manuscript and approved the final version.\u003c/p\u003e\n\u003cp\u003eStudy design: Linlin Wang and Ying Zhang.\u003c/p\u003e\n\u003cp\u003eAcquisition of data: Linlin Wang, Shuang Xie, Aoxue Mei, Xinchen Wang ,Ge Song and Ying Fu.\u003c/p\u003e\n\u003cp\u003eData analysis and interpretation: Linlin Wang, Xinchen Wang, Shuang Xie and Ying Zhang.\u003c/p\u003e\n\u003cp\u003eManuscript drafting and critical revision of the manuscript for important intellectual content:\u003c/p\u003e\n\u003cp\u003eLinlin Wang, Lixian Sun and Ying Zhang.\u003c/p\u003e\n\u003cp\u003eStatistical analysis: Linlin Wang, Xinchen Wang, Ge Song and Ying Zhang.\u003c/p\u003e\n\u003cp\u003eSupervision: Ying Zhang.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e This study was supported by the Government Funded Clinical Medicine Talent Training Project (grant number ZF2023252 to Dr. Ying Zhang).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInstitutional Review Board Statement:\u0026nbsp;\u003c/strong\u003eThis study was approved by the Ethics Committee of the Affiliated Hospital of Chengde Medical University (approval Number: CYFYLL2021036) and conducted according to the tenets of the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent Statement:\u0026nbsp;\u003c/strong\u003eInformed consent was obtained from all subjects involved in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement:\u0026nbsp;\u003c/strong\u003eAll data generated or analysed during this study are included in this published article [and its supplementary information files].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u0026nbsp;\u003c/strong\u003eThe authors would like to thank the doctors and nurses of the Cardiology Research Team at the Affiliated Hospital of Chengde Medical University for their assistance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest:\u003c/strong\u003e The authors declare no conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eBergmark B. 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Tumour biology : the journal of the International Society for Oncodevelopmental Biology and Medicine vol. 37,3 (2016): 3905-11. doi:10.1007/s13277-015-3778-3\u003c/li\u003e\n \u003cli\u003eZhou, Tao et al. \u0026ldquo;Pretreatment albumin globulin ratio has a superior prognostic value in laryngeal squamous cell carcinoma patients: a comparison study.\u0026rdquo; Journal of Cancer vol. 10,3 594-601. 1 Jan. 2019, doi:10.7150/jca.28817\u003c/li\u003e\n \u003cli\u003eJiang, Zhi et al. \u0026ldquo;Serum globulin in children with myasthenia gravis: predicting relapse and prognosis.\u0026rdquo; Neurological research vol. 46,7 (2024): 669-678. doi:10.1080/01616412.2024.2340883.\u003c/li\u003e\n \u003cli\u003eAzab, Basem N et al.\u0026nbsp;\u0026ldquo;Value of the pretreatment albumin to globulin ratio in predicting long-term mortality in breast cancer patients.\u0026rdquo; American journal of surgery vol. 206,5 (2013): 764-70. doi:10.1016/j.amjsurg.2013.03.007\u003c/li\u003e\n \u003cli\u003eWang, Yun-Ting et al.\u0026nbsp;\u0026ldquo;Low Pretreatment Albumin-to-Globulin Ratios Predict Poor Survival Outcomes in Patients with Head and Neck Cancer: A Systematic Review and Meta-analysis.\u0026rdquo; Journal of Cancer vol. 14,2 281-289. 9 Jan. 2023, doi:10.7150/jca.80955\u003c/li\u003e\n \u003cli\u003eCai, Ying et al. \u0026ldquo;Prognostic value of the albumin-globulin ratio and albumin-globulin score in patients with multiple myeloma.\u0026rdquo; The Journal of international medical research vol. 49,3 (2021): 300060521997736. doi:10.1177/0300060521997736\u003c/li\u003e\n \u003cli\u003eLiu, Bin et al. \u0026ldquo;Albumin-Globulin Ratio Is an Independent Determinant of 28-Day Mortality in Patients with Critical Illness.\u0026rdquo; Disease markers vol. 2021 9965124. 25 Aug. 2021, doi:10.1155/2021/9965124\u003c/li\u003e\n \u003cli\u003eSuh, B et al. \u0026ldquo;Low albumin-to-globulin ratio associated with cancer incidence and mortality in generally healthy adults.\u0026rdquo; Annals of oncology : official journal of the European Society for Medical Oncology vol. 25,11 (2014): 2260-2266. doi:10.1093/annonc/mdu274\u003c/li\u003e\n \u003cli\u003ePark, Jane et al. \u0026ldquo;Predictive value of serum albumin-to-globulin ratio for incident chronic kidney disease: A 12-year community-based prospective study.\u0026rdquo; PloS one vol. 15,9 e0238421. 2 Sep. 2020, doi:10.1371/journal.pone.0238421\u003c/li\u003e\n \u003cli\u003eSoeki, Takeshi, and Masataka Sata. \u0026ldquo;Inflammatory Biomarkers and Atherosclerosis.\u0026rdquo; International heart journal vol. 57,2 (2016): 134-9. doi:10.1536/ihj.15-346\u003c/li\u003e\n \u003cli\u003eNiccoli, Giampaolo et al. \u0026ldquo;Role of Allergic Inflammatory Cells in Coronary Artery Disease.\u0026rdquo; Circulation vol. 138,16 (2018): 1736-1748. doi:10.1161/CIRCULATIONAHA.118.035400\u003c/li\u003e\n \u003cli\u003eGolia, Enrica et al. \u0026ldquo;Inflammation and cardiovascular disease: from pathogenesis to therapeutic target.\u0026rdquo; Current atherosclerosis reports vol. 16,9 (2014): 435. doi:10.1007/s11883-014-0435-z\u003c/li\u003e\n \u003cli\u003eTate, Amit R, and Gundu H R Rao.\u0026nbsp;\u0026ldquo;Inflammation: Is It a Healer, Confounder, or a Promoter of Cardiometabolic Risks?.\u0026rdquo; Biomolecules vol. 14,8 948. 6 Aug. 2024, doi:10.3390/biom14080948\u003c/li\u003e\n \u003cli\u003eOzben, Beste, and Okan Erdogan. \u0026ldquo;The role of inflammation and allergy in acute coronary syndromes.\u0026rdquo; 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European heart journal. Acute cardiovascular care vol. 6,2 (2017): 191-198. doi:10.1177/2048872615623038\u003c/li\u003e\n \u003cli\u003eMalebranche, Daniel et al. \u0026ldquo;Patterns of Left-Ventricular Function Assessment in Patients With Acute Coronary Syndromes.\u0026rdquo; CJC open vol. 3,6 733-740. 1 Feb. 2021, doi:10.1016/j.cjco.2020.12.028\u003c/li\u003e\n \u003cli\u003eKhaled, Sheeren, and Rajaa Matahen. \u0026ldquo;Cardiovascular risk factors profile in patients with acute coronary syndrome with particular reference to left ventricular ejection fraction.\u0026rdquo; Indian heart journal vol. 70,1 (2018): 45-49. doi:10.1016/j.ihj.2017.05.019\u003c/li\u003e\n \u003cli\u003eHarjola, Veli-Pekka et al. \u0026ldquo;Clinical picture and risk prediction of short-term mortality in cardiogenic shock.\u0026rdquo; European journal of heart failure vol. 17,5 (2015): 501-9. doi:10.1002/ejhf.260.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"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 coronary syndrome, albumin-to-globulin ratio, recurrent myocardial infarction, percutaneous coronary intervention, major adverse cardiovascular events","lastPublishedDoi":"10.21203/rs.3.rs-6715881/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6715881/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003ePurpose:\u003c/strong\u003e Acute coronary syndromes (ACS) is a leading cause of death worldwide. Albumin and globulin are the main components of serum proteins. The albumin-to-globulin ratio (AGR) is often used to assess nutritional status. However, the clinical significance of the AGR in predicting the prognosis of patients with ACS remains unclear.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePatients and methods:\u003c/strong\u003e A total of 1408 patients with ACS who underwent percutaneous coronary intervention (PCI) were consecutively enrolled between January 2016 and December 2018 at The Affiliated Hospital of Chengde Medical University. The follow-up endpoints were defined as cardiac death or recurrent acute myocardial infarction.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eA total of 1363 patients responded in the follow-up period, of whom 49 had MACEs. AGR was significantly different between the MACEs and non-MACE groups. The area under the curve for the AGR was 0.619 (p =0.004, 95% confidence interval [CI]: 0.542–0.697). The optimal cut-off value for the AGR was determined to be 1.350 using Youden’s index. The cumulative survival rate of the low AGR group was significantly lower than that of the high AGR group, according to the Kaplan-Meier curve (log-rank p=0.008). Multivariate Cox proportional hazards model showed age ≥60 years, HR:2.689 (95%CI:1.288-5.615, p=0.008), left ventricular ejection fraction (LVEF) \u0026lt;40%, HR: 3.527, (95%CI: 1.357–9.164, p=0.010), and AGR\u0026lt;1.350, HR: 2.180, (95%CI: 1.078–4.407, p=0.030) were all independent risk factors. A restricted cubic spline showed that a decreasing AGR was correlated with increasing risk of MACEs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e AGR\u0026lt;1.350 is an independent prognostic risk factor for patients with ACS undergoing PCI and may be a valuable clinical marker for identifying high-risk patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number: \u003c/strong\u003enot applicable.\u003c/p\u003e","manuscriptTitle":"Albumin-to-Globulin ratio as an independent risk factor for predicting prognostic risk in patients with acute coronary syndrome undergoing percutaneous coronary intervention","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-02 12:53:09","doi":"10.21203/rs.3.rs-6715881/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-06-17T17:46:35+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-16T15:17:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"29898005142475241086403869441773480452","date":"2025-06-16T11:50:46+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-06T23:35:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"133268509097785679761133742389120708298","date":"2025-06-06T14:12:05+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-28T10:27:14+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-05-26T13:35:10+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-24T06:34:28+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-24T06:33:47+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cardiovascular Disorders","date":"2025-05-21T11:04:56+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0ed5dbed-6a0b-49f8-af83-064424e2e2d3","owner":[],"postedDate":"June 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-08-07T07:12:30+00:00","versionOfRecord":{"articleIdentity":"rs-6715881","link":"https://doi.org/10.1186/s12872-025-04983-3","journal":{"identity":"bmc-cardiovascular-disorders","isVorOnly":false,"title":"BMC Cardiovascular Disorders"},"publishedOn":"2025-07-18 16:05:08","publishedOnDateReadable":"July 18th, 2025"},"versionCreatedAt":"2025-06-02 12:53:09","video":"","vorDoi":"10.1186/s12872-025-04983-3","vorDoiUrl":"https://doi.org/10.1186/s12872-025-04983-3","workflowStages":[]},"version":"v1","identity":"rs-6715881","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6715881","identity":"rs-6715881","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}