Association Between the Atherogenic Index of Plasm and 10-year Mortality in Patients with Acute Coronary Syndrome: Prospective Cohort Study

Association Between the Atherogenic Index of Plasm and 10-year Mortality in Patients with Acute Coronary Syndrome: Prospective Cohort Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Association Between the Atherogenic Index of Plasm and 10-year Mortality in Patients with Acute Coronary Syndrome: Prospective Cohort Study Junwen WANG, Xuan Zhou, Teng HU, Yuyang YE, Yifei ZHAO, Xuefeng CHEN, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7645153/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background The atherogenic index of plasma (AIP) is a potential marker for cardiovascular risk, but its association with mortality in acute coronary syndrome (ACS) patients remains unclear. This study aimed to evaluate the relationship between AIP and mortality risk in ACS patients, with stratified analyses by sex and age. Methods This cohort study enrolled 2,200 patients with ACS, stratified according to AIP quartiles. The association between AIP levels and all-cause mortality was evaluated using Cox proportional hazards regression models during the 10-year follow-up period. Additionally, sex- and age-stratified analyses were performed, and potential non-linear relationships were examined using two-piecewise linear regression models. Results Higher AIP levels were associated with reduced mortality risk (HR: 0.58, 95% CI: 0.42–0.79, P < 0.001), consistent in adjusted models (Model 1: HR: 0.53, 95% CI: 0.35–0.79, P = 0.002; Model 2: HR: 0.54, 95% CI: 0.36–0.81, P = 0.003). In sex-stratified analyses, no significant association was found in females, but males in the highest AIP quartile had a lower mortality risk (HR: 0.64, 95% CI: 0.43–0.95, P = 0.03). Patients aged > 65 years also showed a significant association (HR: 0.55, 95% CI: 0.37–0.84, P = 0.005). Non-linear analyses revealed a threshold effect in females, with an inflection point at 0.08. Conclusion Elevated AIP levels were associated with reduced mortality risk in ACS patients, particularly in males and older individuals. These findings suggest AIP may aid in risk stratification and personalized management of ACS, warranting further validation. Atherogenic Index of Plasm Mortality Acute Coronary Syndrome Cohort Study Figures Figure 1 Figure 2 Figure 3 Introduction Acute coronary syndrome (ACS) is a serious medical condition marked by an abrupt decrease in blood supply to the heart, which can cause myocardial ischemia and possibly lead to a heart attack or other major cardiac issues 1 . Included in ACS are ST-segment elevation myocardial infarction (STEMI), non-STEMI (NSTEMI), and unstable angina (UA) 2 .In recent years, researchers have made some progress in exploring new biomarkers. For example, cardiac troponin (cTn), a traditional marker of myocardial injury, plays a crucial role in the risk assessment of ACS, but its prognostic value is not absolute 3 . Some new biomarkers, such as bradykinin B1 (NPs), long non-coding RNAs, and growth differentiation factor-15 (GDF-15), have shown additional prognostic value in ACS patients 3 , 4 . In addition, combining high-sensitivity cardiac troponin with other biomarkers such as glucose, red blood cell distribution width, and estimated glomerular filtration rate can more effectively identify high-risk patients 5 . While these biomarkers are linked to risk stratification in ACS, there is a notable paucity of studies concentrating on this patient group that utilize clinically valuable indicators to predict adverse outcomes associated with ACS. Consequently, the identification of reliable biomarkers is essential for the early detection, risk stratification, and targeted interventions necessary to prevent or manage the disease. The Atherogenic Index of Plasma (AIP) is determined by taking the logarithm of the quotient of plasma triglycerides and high-density lipoprotein cholesterol (Log (TG/HDL-C)) 6, 7 . Elevated AIP levels have been independently correlated with a higher incidence of undiagnosed diabetes, particularly among individuals with normal weight or elevated low-density lipoprotein cholesterol levels 8 . Both AIP and VAI have shown a positive association with an increased risk of cardiovascular diseases (CVDs), highlighting their potential utility as predictors for identifying high-risk subgroups within the general population 9 . A U-shaped relationship has been observed between AIP and CVD-specific mortality, indicating that both low and high AIP levels are associated with an elevated risk of mortality in hypertensive patients 10 . A prospective community-based cohort study assessed the predictive value of cumulative AIP for cardiovascular outcomes, revealing significant associations between higher cumulative AIP and increased risks of major adverse cardiac events, stroke, and myocardial infarction, independent of traditional cardiovascular risk factors 11 . Several studies have explored the relationship between AIP and various CVDs, including atherosclerosis, and STEMI following primary percutaneous coronary intervention (PCI) 12 , 13 .Although previous studies have investigated the association between AIP and CVD, research on the prognostic value of AIP in CVD remains limited. The prognostic value of AIP in patients with ACS is still unclear and requires further investigation. Therefore, our study aims to explore the prognostic value of AIP in ACS patients. Methods Study design and population This study is a single-center prospective observational study conducted at West China Hospital, Sichuan Province, China, from December 10, 2010, to December 31, 2012. All study participants completed a 10-year follow-up period. The inclusion criteria for the study were defined as follows: participants had to be aged 18 years or older and present within five days (preferably within 72 hours) of pain onset. A consecutive cohort of 2250 subjects admitted for ACS and undergoing coronary angiography (CAG) were recruited for the study. They were required to have a primary diagnosis of NSTEMI, STEMI, or UA. Enrolled patients exhibited symptoms indicative of angina pectoris, such as chest pain or dyspnea, and met at least one of the following conditions: (i) Ischemic changes in an electrocardiogram (ECG) can include ongoing or fluctuating ST-segment shifts, T-wave inversions, or the appearance of a new left bundle branch block; (ii) Evidence of higher than normal conventional or high-sensitivity troponin levels, based on local laboratory reference values, showing a variation in enzyme levels; (iii) a past of coronary heart disease (CHD), characterized by previous myocardial infarction (MI), coronary artery bypass grafting (CABG), percutaneous coronary intervention, or documented 50% or more stenosis in a coronary artery as shown in earlier reports 14, 15 . The study was registered at the Chinese Clinical Trial Registry (www.chictr.org.cn/) database (ChiCTR2100049313). This study was approved by the Ethics Committee of West China Hospital, Sichuan University. Data collection and definitions Baseline demographic and clinical data were extracted from participants' medical records, encompassing variables such as age, gender, height, weight, body mass index (BMI), heart rate, diastolic and systolic blood pressure, and medical history, including conditions like hypertension, diabetes, hyperlipidemia, smoking, alcohol consumption, chronic kidney disease (CKD), heart failure, hyperuricemia, and stroke. Additionally, coronary angiographic findings, electrocardiogram (ECG) results, and medication history (aspirin, clopidogrel, statins, β-blockers, ACE inhibitors, angiotensin receptor blockers, and calcium channel blockers) were documented. Laboratory parameters were obtained from venous blood samples collected after an overnight fast upon admission, including fasting blood glucose (FBG), hemoglobin A1c (HbA1c), total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), serum creatinine (SCr), C-reactive protein (CRP), alanine aminotransferase (ALT), aspartate aminotransferase (AST), estimated glomerular filtration rate (eGFR), and hemoglobin (Hb) levels. The estimated glomerular filtration rate (eGFR) was determined utilizing the Modification of Diet in Renal Disease (MDRD) Study equation. Hypertension was characterized by a systolic blood pressure (SBP) of 140 mmHg or higher, and/or a diastolic blood pressure (DBP) of 90 mmHg or higher, or by a confirmed diagnosis of hypertension accompanied by the use of antihypertensive medications 16 . Hyperuricemia was defined as a uric acid (UA) concentration exceeding 420 µmol/L in males and 360 µmol/L in females 17 . Determination of AIP and grouping The Atherogenic Index of Plasma (AIP) is determined by calculating the base 10 logarithm of the ratio of triglycerides (TG) to high-density lipoprotein cholesterol (HDL-C), both expressed in molar concentrations (mmol/L), using the formula: log10(TG/HDL-C) 18 . Subsequently, patients were categorized into two groups based on the median AIP value: low AIP ([-0.954, 0.112]) and high AIP [(0.112, 1.309)], with the first quartile (Q1) comprising 1,099 patients (49.95%) and the second quartile (Q2) comprising 1,101 patients (50.05%). Furthermore, patients were stratified into quartiles according to their AIP values: Q1 ([-0.954, -0.074], 551 patients, 25.05%), Q2 ([-0.074, 0.112], 548 patients, 24.91%), Q3 ([0.112, 0.303], 549 patients, 24.95%), and Q4 ([0.303, 1.309], 552 patients, 25.09%). Follow-up and clinical endpoints The principal endpoint of the study was all-cause mortality, assessed over a 10-year follow-up period commencing at the time of enrollment. Follow-up data were acquired through patient identification and contact information collected during hospitalization, communication with family members, and re-admission records from the hospital information system (HIS). Telephone follow-ups were conducted a minimum of three times, with comprehensive documentation of endpoint events. The follow-up period concluded after 10 years of observation, upon the occurrence of patient death, or at the termination of the study. Statistical analysis Continuous variables were represented as mean ± standard deviation, while categorical variables were presented as counts and percentages. To evaluate differences in continuous variables across groups, either a t-test or ANOVA was employed. For categorical variables, Pearson’s chi-squared test or Fisher’s exact test was used, depending on suitability. The study utilized multivariate Cox regression analyses, with AIP as the independent variable and mortality as the dependent variable, to ascertain whether AIP serves as an independent predictor of all-cause mortality. Restricted cubic spline (RCS) functions were employed to illustrate the relationship between positive AIP and overall survival. The optimal number of knots was determined based on the lowest Akaike Information Criterion, with three knots strategically placed on positive AIP. Change points were identified using piecewise linear regression modeling. Furthermore, sensitivity analyses were conducted across various subgroups, including gender, age categories, smoking status, alcohol consumption, and comorbidities such as stroke, diabetes mellitus, hypertension, chronic kidney disease, hyperlipidemia, hyperuricemia, and peripheral artery disease. Statistical analyses were performed utilizing R version 4.1.3 (R Foundation for Statistical Computing, Vienna, Austria), with statistical significance determined at a two-tailed p-value threshold of <0.05. Kaplan-Meier survival curves were constructed employing the 'ggsurvplot' function from the 'ggplot2' package in conjunction with the 'survfit' function from the 'survival' package. To investigate the non-linear association between AIP and all-cause mortality, the 'rcm' package was employed, and visualizations were generated using the 'ggplot2' package. Ethical Considerations Approval for the study protocol was granted by the institutional review board of West China Hospital, following the Declaration of Helsinki (approval number 2012(243)). Written informed consent was secured from every participant before they were enrolled in the study. Patients visiting the hospital for follow-up will be reimbursed for their travel expenses. Results Baseline characteristics A total of 2250 individuals who underwent CAG and met the diagnostic criteria for ACS were initially included in the study, but 50 were excluded based on exclusion criteria (eFigure 1). Ultimately, 2200 individuals were included in the final analysis of this study (eFigure 1). The mean age of the total population was 64.36 ± 11.16 years, with significant differences observed across quartiles (Q1: 67.39 ± 10.55 years; Q2: 65.54 ± 10.77 years; Q3: 63.33 ± 10.63 years; Q4: 61.20 ± 11.68 years; P < 0.001) (Table 1). The average height was 164.42 ± 6.10 cm, with lower values in Q1 (163.72 ± 5.71 cm) and higher values in Q4 (165.08 ± 5.53 cm; P < 0.01) (Table 1). The mean systolic blood pressure (SBP) was 129.88 ± 22.60 mmHg, with no significant differences across quartiles (P = 0.23) (Table 1). However, diastolic blood pressure (DBP) showed a significant increase from Q1 (74.98 ± 13.35 mmHg) to Q4 (77.80 ± 13.22 mmHg; P < 0.01) (Table 1). The proportion of females decreased across quartiles (Q1: 25.41%; Q4: 19.20%), while the proportion of males increased correspondingly (Q1: 74.59%; Q4: 80.80%; P = 0.07) (Table 1). The distribution of diagnoses, including NSTEMI, STEMI, and UA, did not differ significantly across quartiles (P = 0.45) (Table 1). SBP was similar between the two groups based on the AIP median (129.94 ± 23.07 mmHg vs. 129.82 ± 22.13 mmHg; P = 0.91). In contrast, DBP was significantly higher in the high-AIP group (77.19 ± 13.48 mmHg vs. 75.96 ± 13.09 mmHg; P = 0.03) (eTable 1). Urea and estimated glomerular filtration rate showed no significant differences between the groups. The sex distribution showed a slightly higher proportion of males in the high-AIP group (80.11% vs. 76.71%; P = 0.06) (eTable 1). Smoking prevalence was notably higher in the low-AIP group (43.40% vs. 36.60%; P < 0.01) (eTable 1). The distribution of acute coronary syndrome subtypes, including NSTEMI, STEMI, and UA, was similar between the groups (P = 0.99) (eTable 1). Association between clinical outcomes and AIP To evaluate overall survival, the log-rank test was utilized to compare Kaplan-Meier survival curves among the study groups, as shown in Figure 1. The analysis revealed that patients in the Q4 group had the lowest mortality risk compared to those in the other quartile groups (P = 0.014, Figure 1A). Additionally, the low AIP group demonstrated a significantly higher mortality risk than the high AIP group (P = 0.012, Figure 1B). In the crude model, higher AIP was significantly associated with a lower mortality risk (HR: 0.58, 95% CI: 0.42–0.79, P< 0.001). The trend remained consistent in Model 1 (HR: 0.53, 95% CI: 0.35–0.79, P = 0.002) and Model 2 (HR: 0.54, 95% CI: 0.36–0.81, P = 0.003). A significant trend was observed across AIP quartiles (p for trend = 0.02 in Model 2). The association between the AIP and mortality risk was evaluated using three statistical models: the unadjusted model, Model 1 (adjusted for age, sex, body mass index, smoking status, alcohol consumption, systolic blood pressure, diastolic blood pressure, and estimated glomerular filtration rate), and Model 2 (further adjusted for diabetes mellitus, hypertension, hyperlipidemia, and medications including aspirin, clopidogrel, statins, β-blockers, angiotensin-converting enzyme inhibitors, and angiotensin II receptor blockers)(Table 2). The analysis was stratified by sex (male and female) and age groups (18–44 years, 45–64 years, >65 years) (Table 2). In terms of overall mortality risk, the crude model indicated that a higher AIP was significantly associated with a reduced mortality risk (HR: 0.58, 95% CI: 0.42–0.79, P < 0.001) (Table 2). This inverse relationship persisted in Model 1 (HR: 0.53, 95% CI: 0.35–0.79, P = 0.002) and Model 2 (HR: 0.54, 95% CI: 0.36–0.81, P = 0.003) (Table 2). A significant trend was detected across AIP quartiles (P for trend = 0.02 in Model 2) (Table 2). In the sex-stratified analysis, no significant association was found between AIP and mortality risk among female participants across all models. In Model 2, the hazard ratio for the highest versus lowest quartile was 0.49 (95% CI: 0.23–1.03, P = 0.06), and the trend analysis was not significant (P for trend = 0.22) (Table 2). An inverse relationship of statistical significance was identified between AIP and mortality risk among male. In Model 2, the HR for the highest compared to the lowest quartile was 0.64 (95% confidence interval [CI]: 0.43–0.95, P = 0.03), with a significant trend observed across quartiles (P for trend = 0.03) (Table 2). A significant association was observed in the >65 years age group. In Model 2, the hazard ratio (HR) for the highest versus lowest quartile was 0.55 (95% CI: 0.37–0.84, P = 0.005), and the trend analysis further corroborated the inverse relationship (P for trend = 0.01) (Table 2). Association Between AIP and Mortality Risk Across Different Subgroups In male patients, elevated AIP levels were associated with a decreased risk of mortality (HR: 0.566 [0.393–0.816]) (Figure 2A). Similarly, higher AIP levels correlated with a reduced mortality risk in patients without a history of stroke (HR: 0.558 [0.406–0.766]) or dyslipidemia (HR: 0.652 [0.467–0.911]) (Figure 2A). The prognostic significance of AIP for outcomes was consistent across conditions such as diabetes, hypertension, and chronic kidney disease (CKD) (Figure 2A). Male patients with elevated AIP levels exhibited a lower mortality risk compared to those with lower AIP levels (HR: 0.793 [0.645–0.974]) (Figure 2B). Non-smokers with high AIP levels demonstrated a reduced mortality risk relative to those with low AIP levels (HR: 0.790 [0.660–0.946]) (Figure 2B). Among diabetic patients, those with elevated AIP levels showed a decreased mortality risk compared to those with lower levels (HR: 0.764 [0.611–0.957]) (Figure 2B). Patients without peripheral vascular disease or hypertension and with high AIP levels experienced a lower mortality risk than those with low AIP levels (Figure 2B). In males, the highest quartile of AIP (Q4) was associated with a significant reduction in mortality risk (HR = 0.670, P = 0.009), whereas no significant associations were observed in the second (Q2) or third quartiles (Q3). Among patients without a history of stroke, a higher PAI in Q4 corresponded to a decreased mortality risk (HR = 0.655, P = 0.002) (eTable 2). Smokers in Q4 also experienced a significant reduction in mortality risk (HR = 0.660, P = 0.016), with no significant associations noted in Q2 or Q3 (eTable 2). Diabetic patients exhibited a significant decrease in mortality risk in Q4 (HR = 0.567, P = 0.001). Similarly, hypertensive patients demonstrated a significant reduction in mortality risk in Q4 (HR = 0.631, P = 0.039) (eTable 2). Restricted cubic spline curve between the AIP and mortality risk Analyses employing AIP as the independent variable across various models demonstrated a significant non-linear relationship (Figure 3). In Model 3, the overall P-value was 0.021, while the non-linearity P-value (NL-P value) was 0.529. The two-piecewise regression model identified an inflection point at 0.112. For AIP values less than 0.11, the association was not statistically significant (HR = 0.704, 95% CI: 0.332–1.492, P = 0.359) (Figure 3). Conversely, for AIP values equal to or greater than 0.11, a significant reduction in risk was observed (HR = 0.39, 95% CI: 0.178–0.854, P = 0.019) (Figure 3). A significant association between increased AIP and reduced outcome risk was observed, with heterogeneity noted across genders and models. In the female subgroup, Model 3 had an overall P-value of 0.143 and an NL-P value of 0.677.The two-piecewise regression model identified an inflection point at 0.08(eFigure 2).For AIP < 0.08, the association was borderline significant (HR = 0.187, 95% CI: 0.034–1.027, P = 0.054) (eFigure 2).For AIP ≥ 0.08, a significant reduction in risk was evident (HR = 0.147, 95% CI: 0.029–0.739, P = 0.020) (eFigure 2).In the male subgroup, Model 3 showed an overall P-value of 0.078 and an NL-P value of 0.639(eFigure 2). The standard model revealed a significant association between AIP and outcomes (HR = 0.642, 95% CI: 0.436–0.945, P = 0.025) (eFigure 2). In the two-piecewise model, AIP ≥ 0.12 showed a reduction in risk, but this finding did not achieve statistical significance (HR = 0.434, 95% CI: 0.177–1.066, P = 0.069) (eFigure 2). Discussion AIP was associated with mortality risk in patients with ACS, where elevated AIP levels correlated with a decreased risk of mortality. Patients in the highest quartile exhibited a lower mortality risk compared to those in the lowest quartile. A significant inverse relationship was identified between elevated AIP and mortality risk. In sex-stratified analyses, no significant association was observed between AIP and mortality risk in females. Conversely, a significant inverse relationship was evident in males. Among individuals over the age of 65, a significant association was noted, with the hazard ratio comparing the highest to the lowest quartile. Analyses employing AIP as the independent variable across various models demonstrated a significant non-linear relationship. In the female subgroup, a two-piecewise regression model identified an inflection point at 0.08; for AIP values below 0.08, the association was borderline significant. In the male subgroup, a significant association between AIP and mortality risk was observed. The findings of this study align with previous research that has highlighted the significant role of the AIP in predicting mortality risk. AIP, a marker derived from the ratio of triglycerides to high-density lipoprotein cholesterol, has been consistently associated with cardiovascular outcomes. For instance, a study investigating the association of AIP with cardiovascular disease mortality and all-cause mortality in the general US adult population found a J-shaped relationship, indicating that both very low and high levels of AIP could be linked to increased mortality risk 19 . This suggests that maintaining an optimal AIP level is crucial for reducing mortality risk. Furthermore, the reduction in AIP levels may be particularly beneficial in patients with ACS. A study focusing on ACS patients undergoing percutaneous coronary intervention (PCI) demonstrated that a higher AIP was significantly associated with an increased risk of adverse cardiovascular events, including mortality 20 . This indicates that interventions aimed at lowering AIP could potentially reduce specific mortality risks in ACS patients. These findings are further supported by evidence from a community-based cohort study, which demonstrated that higher cumulative AIP levels were associated with an increased risk of major adverse cardiac events, stroke, and myocardial infarction 11 . This reinforces the notion that AIP is a valuable biomarker for assessing cardiovascular risk and mortality. AIP, reflecting the triglycerides-to-HDL-C ratio in plasma, is recognized as an effective marker for predicting atherosclerosis and CVD 21 . Research indicates a strong association between AIP and cardiovascular risk factors, including obesity, blood glucose, and lipid profiles 21 .An elevated at AIP reflects increased TG levels. Previous research has already identified an association between elevated TG levels and a reduced risk of mortality 22 . This phenomenon, known as the "triglyceride paradox," has been partially supported by prior studies 23 , 24 . Our current findings, which demonstrate that increased AIP is associated with a decreased risk of mortality in ACS patients, further corroborate the existence of the triglyceride paradox. The mechanisms underlying the triglyceride paradox remain unclear, but several hypotheses have been proposed. First, low TG levels in ACS patients have been linked to recurrent ischemic events and higher mortality rates 25 – 27 . Second, the "obesity paradox," particularly observed in chronic wasting diseases such as coronary heart disease, suggests that elevated BMI is associated with a reduced risk of mortality. Given that TG levels are significantly correlated with BMI, this may also explain why ACS patients with high AIP in our study exhibited a significantly lower risk of mortality 28 , 29 .Thus, increased AIP may signify improvements in these risk factors, potentially reducing mortality risk. AIP may indicate changes in inflammatory activity that influence cardiovascular risk 30 , 31 .For example, inflammatory cytokines like IL-27 are linked to impaired cardiac function and adverse long-term outcomes in ACS patients 31 .Hyperuricemia is an independent risk factor for elevated AIP, potentially contributing to CVD progression via apolipoprotein inhibition 32 . These findings may partially explain the association observed between AIP and mortality risk in ACS patients in this study. Low AIP levels may indicate normal lipid metabolism, resulting in minimal impact on mortality risk. Conversely, when AIP surpasses a specific threshold (e.g., 0.11), it may indicate lipid metabolism abnormalities, substantially elevating the risk of CVD and mortality 33 , 34 . Moreover, interactions between AIP and other metabolic indicators may modulate its association with mortality risk. For instance, AIP is strongly correlated with insulin resistance and obesity, both of which independently elevate CVD and mortality risks 35 , 36 .Thus, the nonlinear association between AIP and mortality risk may represent underlying complex metabolic interactions. Our study revealed an inverse association between the at AIP and all-cause mortality in patients with ACS, contrasting with previous reports. Changing AIP levels could be a strategic approach to mitigating specific mortality risks, particularly in patients with ACS, thereby improving clinical outcomes 11 , 19 , 20 , 37 . In the general Chinese population, elevated AIP levels are associated with an increased risk of stroke and ischemic stroke, but not significantly linked to intracerebral hemorrhage 38 . These differences may stem from variations in study populations, particularly between ACS patients and the general population. Differences in disease progression stages may also affect research outcomes, as ACS patients are generally in the acute phase, while the general population may be in chronic or asymptomatic stages. This may explain the relationship between AIP and mortality risk in ACS patients. This discrepancy may be attributable to several unique characteristics of our study population. First, the high prevalence (90%) of dual antiplatelet therapy (aspirin and clopidogrel) in our cohort may have mitigated the prothrombotic effects associated with elevated AIP, as previous studies have established a link between increased AIP and heightened platelet reactivity, which predisposes to ischemic complications including stent thrombosis and in-stent restenosis 39 – 43 . Second, the widespread use of statins (> 90% of patients), known to modulate AIP components by reducing triglycerides and elevating high-density lipoprotein cholesterol 44 , 45 , may have modified the atherogenic potential of AIP. Third, we observed a graded increase in beta-blocker utilization across AIP quartiles, reaching nearly 70% in the highest AIP group. Given the established mortality benefit of beta-blockers in ACS patients, this treatment pattern may have contributed to the observed inverse association 46 , 47 . While these findings suggest a potential protective association between elevated AIP and mortality risk in ACS patients, the underlying mechanisms require further elucidation through large-scale prospective studies and randomized controlled trials. Our findings underscore the need to integrate AIP into risk stratification models for ACS patients, particularly for male and elderly populations. Current guidelines primarily focus on traditional lipid parameters, such as LDL-C and HDL-C, while overlooking composite indices like AIP. Incorporating AIP into routine clinical assessments may enhance the precision of mortality risk prediction and inform individualized therapeutic strategies. Additionally, sex-stratified approaches in lipid management should be emphasized, with targeted interventions for males at higher AIP thresholds. Future public health initiatives should also prioritize educational campaigns to raise awareness about the prognostic value of composite lipid indices in cardiovascular care. Limitation This single-center study may introduce patient selection bias. While data on discharge medications were included in the long-term mortality risk analysis, information on subsequent medication adjustments during follow-up was unavailable. Additionally, post-discharge bleeding and ischemic complications, which could markedly influence patient prognosis, were not recorded. Coronary vascular information was not included in our multivariate analysis, which might have slightly affected the results. Nonetheless, the study population comprised patients with unstable angina, acute STEMI, and acute NSTEMI. The inclusion of these ACS subtypes, which differ in the severity of vascular lesions, partially mitigates potential biases. Conclusion In patients with ACS, AIP was associated with mortality risk, wherein higher AIP levels corresponded to a decreased likelihood of death. Specifically, patients in the highest quartile of AIP exhibited a significantly reduced mortality risk compared to those in the lowest quartile. A pronounced inverse correlation was observed between elevated AIP levels and mortality risk. Stratified analyses by sex revealed a significant inverse relationship between AIP and mortality risk in males, whereas this association was not significant in females. Among individuals aged over 65, a robust association was identified, with the hazard ratio comparing the highest to the lowest quartiles. Analyses utilizing AIP as the independent variable across various models demonstrated a significant non-linear relationship. Declarations Authors’ contributions WJW and YP participated in study conception and design. WJW, MJ, YYY, CXF, and HXR performed the acquisition of data. WJW, PY, ZX, SMY, MJ and YJ participated in analysis and interpretation of data. WJW, ZX, MJ and YP drafted the manuscript and YP, WJW, and MJ helped in critical review of the manuscript. All authors read and approved the final manuscript. Ethics approval and consent to participate This study was conducted in accordance with the Declaration of Helsinki and was approved by the Institutional Review Board of West China Hospital, Sichuan University (approval number: 2012(243)). Written informed consent was obtained from all participants prior to their inclusion in the study. Clinical Trial Registry The study was registered at the Chinese Clinical Trial Registry (www.chictr.org.cn/) database (ChiCTR2100049313). Consent for publication If the manuscript is accepted, we approve it for publication in Lipids in Health and Disease. Competing interests The authors declare that they have no competing interests. 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Diagnosis, Treatment, and Long-Term Management of Kawasaki Disease: A Scientific Statement for Health Professionals From the American Heart Association. Circulation . 2017;135:e927-e999. Tables Table 1 and 2 are available in the Supplementary Files section. Supplementary Files Supplementary.docx Table.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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Kaplan-Meier (KM) survival curves and cumulative survival curves were analyzed to compare survival times among groups using the log-rank test. (B) Patients were divided into high and low groups based on the median. In the Kaplan-Meier survival curve, the horizontal axis represents time, and the vertical axis represents survival probability.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7645153/v1/d7cb88288e73f8676fd73784.png"},{"id":93915529,"identity":"1a0c3b03-d923-4650-8e44-acea926d331c","added_by":"auto","created_at":"2025-10-20 08:45:11","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":518646,"visible":true,"origin":"","legend":"\u003cp\u003eSubgroup analysis of the atherogenic index of plasma and mortality risk in patients with acute myocardial infarction.\u003c/p\u003e\n\u003cp\u003e(A) Forest plot of subgroup analysis stratified by serum atherogenic index and mortality. (B) Forest plot of subgroup analysis stratified by high serum atherogenic index and low serum atherogenic index. Diabetes mellitus (DM), hypertension, chronic kidney disease (CKD).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7645153/v1/a211702ebae9fd82e2682fba.png"},{"id":93913478,"identity":"62c36662-31a5-4985-b4e3-6a0773242ffa","added_by":"auto","created_at":"2025-10-20 08:37:11","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":284747,"visible":true,"origin":"","legend":"\u003cp\u003eRestricted cubic spline (RCS) curve of atherogenic index of plasma and HR in patients with acute myocardial infarction.\u003c/p\u003e\n\u003cp\u003eThe dashed lines are the 95% CIs for restricted cubic spline models.\u003c/p\u003e\n\u003cp\u003eModel 1 is unadjusted. Model 2 is adjusted for age, sex, body mass index (BMI), smoking status, alcohol consumption, systolic blood pressure (SBP), diastolic blood pressure (DBP), and estimated glomerular filtration rate (eGFR). Model 3 includes additional adjustments based on Model 2, incorporating variables such as stroke, diabetes mellitus (DM), hypertension, chronic kidney disease (CKD), hyperlipidemia, peripheral vascular disease, hyperuricemia, and the use of medications including aspirin, clopidogrel, statins, β-blockers, angiotensin-converting enzyme inhibitors (ACEI), angiotensin II receptor blockers (ARB), as well as platelet count (PLT), alanine aminotransferase (ALT), aspartate aminotransferase (AST), potassium (K), and glucose (GLU) levels.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7645153/v1/67d988fbbef17ef3f60fccbb.png"},{"id":95222385,"identity":"670c3b9e-2588-4a06-93a0-efc1a3379cae","added_by":"auto","created_at":"2025-11-05 16:20:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1518685,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7645153/v1/437a6769-a87f-4ac4-98fd-59aa34aa119e.pdf"},{"id":93913479,"identity":"a28653f2-0910-400f-ab98-aa879ea8adc0","added_by":"auto","created_at":"2025-10-20 08:37:11","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":351291,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementary.docx","url":"https://assets-eu.researchsquare.com/files/rs-7645153/v1/f9b19cd0f10bdd9a4fc28167.docx"},{"id":93913475,"identity":"5e001712-3000-4848-ac40-149c2f171378","added_by":"auto","created_at":"2025-10-20 08:37:11","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":41319,"visible":true,"origin":"","legend":"","description":"","filename":"Table.docx","url":"https://assets-eu.researchsquare.com/files/rs-7645153/v1/6a420be278dac9e859455dec.docx"}],"financialInterests":"","formattedTitle":"Association Between the Atherogenic Index of Plasm and 10-year Mortality in Patients with Acute Coronary Syndrome: Prospective Cohort Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAcute coronary syndrome (ACS) is a serious medical condition marked by an abrupt decrease in blood supply to the heart, which can cause myocardial ischemia and possibly lead to a heart attack or other major cardiac issues\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Included in ACS are ST-segment elevation myocardial infarction (STEMI), non-STEMI (NSTEMI), and unstable angina (UA)\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e.In recent years, researchers have made some progress in exploring new biomarkers. For example, cardiac troponin (cTn), a traditional marker of myocardial injury, plays a crucial role in the risk assessment of ACS, but its prognostic value is not absolute\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Some new biomarkers, such as bradykinin B1 (NPs), long non-coding RNAs, and growth differentiation factor-15 (GDF-15), have shown additional prognostic value in ACS patients\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. In addition, combining high-sensitivity cardiac troponin with other biomarkers such as glucose, red blood cell distribution width, and estimated glomerular filtration rate can more effectively identify high-risk patients\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. While these biomarkers are linked to risk stratification in ACS, there is a notable paucity of studies concentrating on this patient group that utilize clinically valuable indicators to predict adverse outcomes associated with ACS. Consequently, the identification of reliable biomarkers is essential for the early detection, risk stratification, and targeted interventions necessary to prevent or manage the disease.\u003c/p\u003e\u003cp\u003eThe Atherogenic Index of Plasma (AIP) is determined by taking the logarithm of the quotient of plasma triglycerides and high-density lipoprotein cholesterol (Log (TG/HDL-C))\u003csup\u003e6, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Elevated AIP levels have been independently correlated with a higher incidence of undiagnosed diabetes, particularly among individuals with normal weight or elevated low-density lipoprotein cholesterol levels\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Both AIP and VAI have shown a positive association with an increased risk of cardiovascular diseases (CVDs), highlighting their potential utility as predictors for identifying high-risk subgroups within the general population\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. A U-shaped relationship has been observed between AIP and CVD-specific mortality, indicating that both low and high AIP levels are associated with an elevated risk of mortality in hypertensive patients\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. A prospective community-based cohort study assessed the predictive value of cumulative AIP for cardiovascular outcomes, revealing significant associations between higher cumulative AIP and increased risks of major adverse cardiac events, stroke, and myocardial infarction, independent of traditional cardiovascular risk factors\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Several studies have explored the relationship between AIP and various CVDs, including atherosclerosis, and STEMI following primary percutaneous coronary intervention (PCI)\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e.Although previous studies have investigated the association between AIP and CVD, research on the prognostic value of AIP in CVD remains limited. The prognostic value of AIP in patients with ACS is still unclear and requires further investigation. Therefore, our study aims to explore the prognostic value of AIP in ACS patients.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy design and population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study is a single-center prospective observational study conducted at West China Hospital, Sichuan Province, China, from December 10, 2010, to December 31, 2012.\u0026nbsp;All study participants completed a 10-year follow-up period. The inclusion criteria for the study were defined as follows: participants had to be aged 18 years or older and present within five days (preferably within 72 hours) of pain onset. A consecutive cohort of 2250 subjects admitted for ACS and undergoing coronary angiography (CAG) were recruited for the study. They were required to have a primary diagnosis of NSTEMI, STEMI, or UA. Enrolled patients exhibited symptoms indicative of angina pectoris, such as chest pain or dyspnea, and met at least one of the following conditions: (i) Ischemic changes in an electrocardiogram (ECG) can include ongoing or fluctuating ST-segment shifts, T-wave inversions, or the appearance of a new left bundle branch block; (ii) Evidence of higher than normal conventional or high-sensitivity troponin levels, based on local laboratory reference values, showing a variation in enzyme levels; (iii)\u0026nbsp;a past of coronary heart disease (CHD), characterized by previous myocardial infarction (MI), coronary artery bypass grafting (CABG), percutaneous coronary intervention, or documented 50% or more stenosis in a coronary artery as shown in earlier reports\u003csup\u003e14, 15\u003c/sup\u003e. The study was registered at the Chinese Clinical Trial Registry (www.chictr.org.cn/) database (ChiCTR2100049313). This study was approved by the Ethics Committee of West China Hospital, Sichuan University.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData collection and definitions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBaseline demographic and clinical data were extracted from participants\u0026apos; medical records, encompassing variables such as age, gender, height, weight, body mass index (BMI), heart rate, diastolic and systolic blood pressure, and medical history, including conditions like hypertension, diabetes, hyperlipidemia, smoking, alcohol consumption, chronic kidney disease (CKD), heart failure, hyperuricemia, and stroke. Additionally, coronary angiographic findings, electrocardiogram (ECG) results, and medication history (aspirin, clopidogrel, statins, \u0026beta;-blockers, ACE inhibitors, angiotensin receptor blockers, and calcium channel blockers) were documented. Laboratory parameters were obtained from venous blood samples collected after an overnight fast upon admission, including fasting blood glucose (FBG), hemoglobin A1c (HbA1c), total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), serum creatinine (SCr), C-reactive protein (CRP), alanine aminotransferase (ALT), aspartate aminotransferase (AST), estimated glomerular filtration rate (eGFR), and hemoglobin (Hb) levels.\u0026nbsp;The estimated glomerular filtration rate (eGFR) was determined utilizing the Modification of Diet in Renal Disease (MDRD) Study equation. Hypertension was characterized by a systolic blood pressure (SBP) of 140 mmHg or higher, and/or a diastolic blood pressure (DBP) of 90 mmHg or higher, or by a confirmed diagnosis of hypertension accompanied by the use of antihypertensive medications\u003csup\u003e16\u003c/sup\u003e. Hyperuricemia was defined as a uric acid (UA) concentration exceeding 420 \u0026micro;mol/L in males and 360 \u0026micro;mol/L in females\u003csup\u003e17\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDetermination of AIP and grouping\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Atherogenic Index of Plasma (AIP) is determined by calculating the base 10 logarithm of the ratio of triglycerides (TG) to high-density lipoprotein cholesterol (HDL-C), both expressed in molar concentrations (mmol/L), using the formula: log10(TG/HDL-C)\u003csup\u003e18\u003c/sup\u003e. Subsequently, patients were categorized into two groups based on the median AIP value: low AIP ([-0.954, 0.112]) and high AIP [(0.112, 1.309)], with the first quartile (Q1) comprising 1,099 patients (49.95%) and the second quartile (Q2) comprising 1,101 patients (50.05%). Furthermore, patients were stratified into quartiles according to their AIP values: Q1 ([-0.954, -0.074], 551 patients, 25.05%), Q2 ([-0.074, 0.112], 548 patients, 24.91%), Q3 ([0.112, 0.303], 549 patients, 24.95%), and Q4 ([0.303, 1.309], 552 patients, 25.09%).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFollow-up and clinical endpoints\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe principal endpoint of the study was all-cause mortality, assessed over a 10-year follow-up period commencing at the time of enrollment. Follow-up data were acquired through patient identification and contact information collected during hospitalization, communication with family members, and re-admission records from the hospital information system (HIS). Telephone follow-ups were conducted a minimum of three times, with comprehensive documentation of endpoint events. The follow-up period concluded after 10 years of observation, upon the occurrence of patient death, or at the termination of the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eContinuous variables were represented as mean \u0026plusmn; standard deviation, while categorical variables were presented as counts and percentages. To evaluate differences in continuous variables across groups, either a t-test or ANOVA was employed. For categorical variables, Pearson\u0026rsquo;s chi-squared test or Fisher\u0026rsquo;s exact test was used, depending on suitability. The study utilized multivariate Cox regression analyses, with AIP as the independent variable and mortality as the dependent variable, to ascertain whether AIP serves as an independent predictor of all-cause mortality. Restricted cubic spline (RCS) functions were employed to illustrate the relationship between positive AIP and overall survival. The optimal number of knots was determined based on the lowest Akaike Information Criterion, with three knots strategically placed on positive AIP. Change points were identified using piecewise linear regression modeling. Furthermore, sensitivity analyses were conducted across various subgroups, including gender, age categories, smoking status, alcohol consumption, and comorbidities such as stroke, diabetes mellitus, hypertension, chronic kidney disease, hyperlipidemia, hyperuricemia, and peripheral artery disease.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eStatistical analyses were performed utilizing R version 4.1.3 (R Foundation for Statistical Computing, Vienna, Austria), with statistical significance determined at a two-tailed p-value threshold of \u0026lt;0.05. Kaplan-Meier survival curves were constructed employing the \u0026apos;ggsurvplot\u0026apos; function from the \u0026apos;ggplot2\u0026apos; package in conjunction with the \u0026apos;survfit\u0026apos; function from the \u0026apos;survival\u0026apos; package. To investigate the non-linear association between AIP and all-cause mortality, the \u0026apos;rcm\u0026apos; package was employed, and visualizations were generated using the \u0026apos;ggplot2\u0026apos; package.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Considerations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eApproval for the study protocol was granted by the institutional review board of West China Hospital, following the Declaration of Helsinki (approval number 2012(243)). Written informed consent was secured from every participant before they were enrolled in the study. Patients visiting the hospital for follow-up will be reimbursed for their travel expenses.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eBaseline characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 2250 individuals who underwent CAG and met the diagnostic criteria for ACS were initially included in the study, but 50 were excluded based on exclusion criteria (eFigure 1). Ultimately, 2200 individuals were included in the final analysis of this study (eFigure 1).\u003c/p\u003e\n\u003cp\u003eThe mean age of the total population was 64.36 \u0026plusmn; 11.16 years, with significant differences observed across quartiles (Q1: 67.39 \u0026plusmn; 10.55 years; Q2: 65.54 \u0026plusmn; 10.77 years; Q3: 63.33 \u0026plusmn; 10.63 years; Q4: 61.20 \u0026plusmn; 11.68 years; P \u0026lt; 0.001) (Table 1). The average height was 164.42 \u0026plusmn; 6.10 cm, with lower values in Q1 (163.72 \u0026plusmn; 5.71 cm) and higher values in Q4 (165.08 \u0026plusmn; 5.53 cm; P \u0026lt; 0.01) (Table 1). The mean systolic blood pressure (SBP) was 129.88 \u0026plusmn; 22.60 mmHg, with no significant differences across quartiles (P = 0.23) (Table 1). However, diastolic blood pressure (DBP) showed a significant increase from Q1 (74.98 \u0026plusmn; 13.35 mmHg) to Q4 (77.80 \u0026plusmn; 13.22 mmHg; P \u0026lt; 0.01) (Table 1). The proportion of females decreased across quartiles (Q1: 25.41%; Q4: 19.20%), while the proportion of males increased correspondingly (Q1: 74.59%; Q4: 80.80%; P = 0.07) (Table 1). The distribution of diagnoses, including NSTEMI, STEMI, and UA, did not differ significantly across quartiles (P = 0.45) (Table 1).\u003c/p\u003e\n\u003cp\u003eSBP was similar between the two groups based on the AIP median (129.94 \u0026plusmn; 23.07 mmHg vs. 129.82 \u0026plusmn; 22.13 mmHg; P = 0.91). In contrast, DBP was significantly higher in the high-AIP group (77.19 \u0026plusmn; 13.48 mmHg vs. 75.96 \u0026plusmn; 13.09 mmHg; P = 0.03) (eTable 1). Urea and estimated glomerular filtration rate showed no significant differences between the groups. The sex distribution showed a slightly higher proportion of males in the high-AIP group (80.11% vs. 76.71%; P = 0.06) (eTable 1). Smoking prevalence was notably higher in the low-AIP group (43.40% vs. 36.60%; P \u0026lt; 0.01) (eTable 1). The distribution of acute coronary syndrome subtypes, including NSTEMI, STEMI, and UA, was similar between the groups (P = 0.99) (eTable 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssociation between clinical outcomes and AIP\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo evaluate overall survival, the log-rank test was utilized to compare Kaplan-Meier survival curves among the study groups, as shown in Figure 1. The analysis revealed that patients in the Q4 group had the lowest mortality risk compared to those in the other quartile groups (P = 0.014, Figure 1A). Additionally, the low AIP group demonstrated a significantly higher mortality risk than the high AIP group (P = 0.012, Figure 1B). In the crude model, higher AIP was significantly associated with a lower mortality risk (HR: 0.58, 95% CI: 0.42\u0026ndash;0.79, P\u0026lt; 0.001). The trend remained consistent in Model 1 (HR: 0.53, 95% CI: 0.35\u0026ndash;0.79, P = 0.002) and Model 2 (HR: 0.54, 95% CI: 0.36\u0026ndash;0.81, P = 0.003). A significant trend was observed across AIP quartiles (p for trend = 0.02 in Model 2).\u003c/p\u003e\n\u003cp\u003eThe association between the AIP and mortality risk was evaluated using three statistical models: the unadjusted model, Model 1 (adjusted for age, sex, body mass index, smoking status, alcohol consumption, systolic blood pressure, diastolic blood pressure, and estimated glomerular filtration rate), and Model 2 (further adjusted for diabetes mellitus, hypertension, hyperlipidemia, and medications including aspirin, clopidogrel, statins, \u0026beta;-blockers, angiotensin-converting enzyme inhibitors, and angiotensin II receptor blockers)(Table 2). The analysis was stratified by sex (male and female) and age groups (18\u0026ndash;44 years, 45\u0026ndash;64 years, \u0026gt;65 years) (Table 2). In terms of overall mortality risk, the crude model indicated that a higher AIP was significantly associated with a reduced mortality risk (HR: 0.58, 95% CI: 0.42\u0026ndash;0.79, P \u0026lt; 0.001) (Table 2). This inverse relationship persisted in Model 1 (HR: 0.53, 95% CI: 0.35\u0026ndash;0.79, P = 0.002) and Model 2 (HR: 0.54, 95% CI: 0.36\u0026ndash;0.81, P = 0.003) (Table 2). A significant trend was detected across AIP quartiles (P for trend = 0.02 in Model 2) (Table 2).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn the sex-stratified analysis, no significant association was found between AIP and mortality risk among female participants across all models. In Model 2, the hazard ratio for the highest versus lowest quartile was 0.49 (95% CI: 0.23\u0026ndash;1.03, P = 0.06), and the trend analysis was not significant (P for trend = 0.22) (Table 2). An inverse relationship of statistical significance was identified between AIP and mortality risk among male. In Model 2, the HR for the highest compared to the lowest quartile was 0.64 (95% confidence interval [CI]: 0.43\u0026ndash;0.95, P = 0.03), with a significant trend observed across quartiles (P for trend = 0.03)\u0026nbsp;(Table 2). A significant association was observed in the \u0026gt;65 years age group. In Model 2, the hazard ratio (HR) for the highest versus lowest quartile was 0.55 (95% CI: 0.37\u0026ndash;0.84, P = 0.005), and the trend analysis further corroborated the inverse relationship (P for trend = 0.01) (Table 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssociation Between AIP and Mortality Risk Across Different Subgroups\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn male patients, elevated AIP levels were associated with a decreased risk of mortality (HR: 0.566 [0.393\u0026ndash;0.816]) (Figure 2A). Similarly, higher AIP levels correlated with a reduced mortality risk in patients without a history of stroke (HR: 0.558 [0.406\u0026ndash;0.766]) or dyslipidemia (HR: 0.652 [0.467\u0026ndash;0.911]) (Figure 2A). The prognostic significance of AIP for outcomes was consistent across conditions such as diabetes, hypertension, and chronic kidney disease (CKD) (Figure 2A). Male patients with elevated AIP levels exhibited a lower mortality risk compared to those with lower AIP levels (HR: 0.793 [0.645\u0026ndash;0.974]) (Figure 2B). Non-smokers with high AIP levels demonstrated a reduced mortality risk relative to those with low AIP levels (HR: 0.790 [0.660\u0026ndash;0.946]) (Figure 2B). Among diabetic patients, those with elevated AIP levels showed a decreased mortality risk compared to those with lower levels (HR: 0.764 [0.611\u0026ndash;0.957]) (Figure 2B). Patients without peripheral vascular disease or hypertension and with high AIP levels experienced a lower mortality risk than those with low AIP levels (Figure 2B).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn males, the highest quartile of AIP (Q4) was associated with a significant reduction in mortality risk (HR = 0.670, P = 0.009), whereas no significant associations were observed in the second (Q2) or third quartiles (Q3). Among patients without a history of stroke, a higher PAI in Q4 corresponded to a decreased mortality risk (HR = 0.655, P = 0.002) (eTable 2). Smokers in Q4 also experienced a significant reduction in mortality risk (HR = 0.660, P = 0.016), with no significant associations noted in Q2 or Q3 (eTable 2). Diabetic patients exhibited a significant decrease in mortality risk in Q4 (HR = 0.567, P = 0.001). Similarly, hypertensive patients demonstrated a significant reduction in mortality risk in Q4 (HR = 0.631, P = 0.039) (eTable 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRestricted cubic spline curve between the AIP and mortality risk\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAnalyses employing AIP as the independent variable across various models demonstrated a significant non-linear relationship (Figure 3). In Model 3, the overall P-value was 0.021, while the non-linearity P-value (NL-P value) was 0.529. The two-piecewise regression model identified an inflection point at 0.112. For AIP values less than 0.11, the association was not statistically significant (HR = 0.704, 95% CI: 0.332\u0026ndash;1.492, P = 0.359)\u0026nbsp;(Figure 3). Conversely, for AIP values equal to or greater than 0.11, a significant reduction in risk was observed (HR = 0.39, 95% CI: 0.178\u0026ndash;0.854, P = 0.019)\u0026nbsp;(Figure 3).\u003c/p\u003e\n\u003cp\u003eA significant association between increased AIP and reduced outcome risk was observed, with heterogeneity noted across genders and models. In the female subgroup, Model 3 had an overall P-value of 0.143 and an NL-P value of 0.677.The two-piecewise regression model identified an inflection point at 0.08(eFigure 2).For AIP \u0026lt; 0.08, the association was borderline significant (HR = 0.187, 95% CI: 0.034\u0026ndash;1.027, P = 0.054) (eFigure 2).For AIP \u0026ge; 0.08, a significant reduction in risk was evident (HR = 0.147, 95% CI: 0.029\u0026ndash;0.739, P = 0.020) (eFigure 2).In the male subgroup, Model 3 showed an overall P-value of 0.078 and an NL-P value of 0.639(eFigure 2). The standard model revealed a significant association between AIP and outcomes (HR = 0.642, 95% CI: 0.436\u0026ndash;0.945, P = 0.025) (eFigure 2). In the two-piecewise model, AIP \u0026ge; 0.12 showed a reduction in risk, but this finding did not achieve statistical significance (HR = 0.434, 95% CI: 0.177\u0026ndash;1.066, P = 0.069) (eFigure 2).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eAIP was associated with mortality risk in patients with ACS, where elevated AIP levels correlated with a decreased risk of mortality. Patients in the highest quartile exhibited a lower mortality risk compared to those in the lowest quartile. A significant inverse relationship was identified between elevated AIP and mortality risk. In sex-stratified analyses, no significant association was observed between AIP and mortality risk in females. Conversely, a significant inverse relationship was evident in males. Among individuals over the age of 65, a significant association was noted, with the hazard ratio comparing the highest to the lowest quartile. Analyses employing AIP as the independent variable across various models demonstrated a significant non-linear relationship. In the female subgroup, a two-piecewise regression model identified an inflection point at 0.08; for AIP values below 0.08, the association was borderline significant. In the male subgroup, a significant association between AIP and mortality risk was observed.\u003c/p\u003e\u003cp\u003eThe findings of this study align with previous research that has highlighted the significant role of the AIP in predicting mortality risk. AIP, a marker derived from the ratio of triglycerides to high-density lipoprotein cholesterol, has been consistently associated with cardiovascular outcomes. For instance, a study investigating the association of AIP with cardiovascular disease mortality and all-cause mortality in the general US adult population found a J-shaped relationship, indicating that both very low and high levels of AIP could be linked to increased mortality risk\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. This suggests that maintaining an optimal AIP level is crucial for reducing mortality risk. Furthermore, the reduction in AIP levels may be particularly beneficial in patients with ACS. A study focusing on ACS patients undergoing percutaneous coronary intervention (PCI) demonstrated that a higher AIP was significantly associated with an increased risk of adverse cardiovascular events, including mortality\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. This indicates that interventions aimed at lowering AIP could potentially reduce specific mortality risks in ACS patients. These findings are further supported by evidence from a community-based cohort study, which demonstrated that higher cumulative AIP levels were associated with an increased risk of major adverse cardiac events, stroke, and myocardial infarction\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. This reinforces the notion that AIP is a valuable biomarker for assessing cardiovascular risk and mortality.\u003c/p\u003e\u003cp\u003eAIP, reflecting the triglycerides-to-HDL-C ratio in plasma, is recognized as an effective marker for predicting atherosclerosis and CVD\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Research indicates a strong association between AIP and cardiovascular risk factors, including obesity, blood glucose, and lipid profiles\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e.An elevated at AIP reflects increased TG levels. Previous research has already identified an association between elevated TG levels and a reduced risk of mortality\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. This phenomenon, known as the \"triglyceride paradox,\" has been partially supported by prior studies\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Our current findings, which demonstrate that increased AIP is associated with a decreased risk of mortality in ACS patients, further corroborate the existence of the triglyceride paradox. The mechanisms underlying the triglyceride paradox remain unclear, but several hypotheses have been proposed. First, low TG levels in ACS patients have been linked to recurrent ischemic events and higher mortality rates\u003csup\u003e\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Second, the \"obesity paradox,\" particularly observed in chronic wasting diseases such as coronary heart disease, suggests that elevated BMI is associated with a reduced risk of mortality. Given that TG levels are significantly correlated with BMI, this may also explain why ACS patients with high AIP in our study exhibited a significantly lower risk of mortality \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e.Thus, increased AIP may signify improvements in these risk factors, potentially reducing mortality risk. AIP may indicate changes in inflammatory activity that influence cardiovascular risk\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e.For example, inflammatory cytokines like IL-27 are linked to impaired cardiac function and adverse long-term outcomes in ACS patients\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e.Hyperuricemia is an independent risk factor for elevated AIP, potentially contributing to CVD progression via apolipoprotein inhibition\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. These findings may partially explain the association observed between AIP and mortality risk in ACS patients in this study. Low AIP levels may indicate normal lipid metabolism, resulting in minimal impact on mortality risk. Conversely, when AIP surpasses a specific threshold (e.g., 0.11), it may indicate lipid metabolism abnormalities, substantially elevating the risk of CVD and mortality\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Moreover, interactions between AIP and other metabolic indicators may modulate its association with mortality risk. For instance, AIP is strongly correlated with insulin resistance and obesity, both of which independently elevate CVD and mortality risks\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e.Thus, the nonlinear association between AIP and mortality risk may represent underlying complex metabolic interactions.\u003c/p\u003e\u003cp\u003eOur study revealed an inverse association between the at AIP and all-cause mortality in patients with ACS, contrasting with previous reports. Changing AIP levels could be a strategic approach to mitigating specific mortality risks, particularly in patients with ACS, thereby improving clinical outcomes\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. In the general Chinese population, elevated AIP levels are associated with an increased risk of stroke and ischemic stroke, but not significantly linked to intracerebral hemorrhage\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. These differences may stem from variations in study populations, particularly between ACS patients and the general population. Differences in disease progression stages may also affect research outcomes, as ACS patients are generally in the acute phase, while the general population may be in chronic or asymptomatic stages. This may explain the relationship between AIP and mortality risk in ACS patients. This discrepancy may be attributable to several unique characteristics of our study population. First, the high prevalence (90%) of dual antiplatelet therapy (aspirin and clopidogrel) in our cohort may have mitigated the prothrombotic effects associated with elevated AIP, as previous studies have established a link between increased AIP and heightened platelet reactivity, which predisposes to ischemic complications including stent thrombosis and in-stent restenosis \u003csup\u003e\u003cspan additionalcitationids=\"CR40 CR41 CR42\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. Second, the widespread use of statins (\u0026gt;\u0026thinsp;90% of patients), known to modulate AIP components by reducing triglycerides and elevating high-density lipoprotein cholesterol \u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e, may have modified the atherogenic potential of AIP. Third, we observed a graded increase in beta-blocker utilization across AIP quartiles, reaching nearly 70% in the highest AIP group. Given the established mortality benefit of beta-blockers in ACS patients, this treatment pattern may have contributed to the observed inverse association\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. While these findings suggest a potential protective association between elevated AIP and mortality risk in ACS patients, the underlying mechanisms require further elucidation through large-scale prospective studies and randomized controlled trials.\u003c/p\u003e\u003cp\u003eOur findings underscore the need to integrate AIP into risk stratification models for ACS patients, particularly for male and elderly populations. Current guidelines primarily focus on traditional lipid parameters, such as LDL-C and HDL-C, while overlooking composite indices like AIP. Incorporating AIP into routine clinical assessments may enhance the precision of mortality risk prediction and inform individualized therapeutic strategies. Additionally, sex-stratified approaches in lipid management should be emphasized, with targeted interventions for males at higher AIP thresholds. Future public health initiatives should also prioritize educational campaigns to raise awareness about the prognostic value of composite lipid indices in cardiovascular care.\u003c/p\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eLimitation\u003c/h2\u003e\u003cp\u003eThis single-center study may introduce patient selection bias. While data on discharge medications were included in the long-term mortality risk analysis, information on subsequent medication adjustments during follow-up was unavailable. Additionally, post-discharge bleeding and ischemic complications, which could markedly influence patient prognosis, were not recorded. Coronary vascular information was not included in our multivariate analysis, which might have slightly affected the results. Nonetheless, the study population comprised patients with unstable angina, acute STEMI, and acute NSTEMI. The inclusion of these ACS subtypes, which differ in the severity of vascular lesions, partially mitigates potential biases.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn patients with ACS, AIP was associated with mortality risk, wherein higher AIP levels corresponded to a decreased likelihood of death. Specifically, patients in the highest quartile of AIP exhibited a significantly reduced mortality risk compared to those in the lowest quartile. A pronounced inverse correlation was observed between elevated AIP levels and mortality risk. Stratified analyses by sex revealed a significant inverse relationship between AIP and mortality risk in males, whereas this association was not significant in females. Among individuals aged over 65, a robust association was identified, with the hazard ratio comparing the highest to the lowest quartiles. Analyses utilizing AIP as the independent variable across various models demonstrated a significant non-linear relationship.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWJW and YP participated in study conception and design. WJW, MJ, YYY, \u0026nbsp;CXF, and HXR performed the acquisition of data. WJW, PY, ZX, SMY, MJ and YJ participated in analysis and interpretation of data. WJW, ZX, MJ and YP drafted the manuscript and YP, WJW, and MJ helped in critical review of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the Declaration of Helsinki and was approved by the Institutional Review Board of West China Hospital, Sichuan University (approval number: 2012(243)). Written informed consent was obtained from all participants prior to their inclusion in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial Registry\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was registered at the Chinese Clinical Trial Registry (www.chictr.org.cn/) database (ChiCTR2100049313).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIf the manuscript is accepted, we approve it for publication in Lipids in Health and Disease.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eKajikawa M, Noma K, Nakashima A, Maruhashi T, Iwamoto Y, Matsumoto T, Iwamoto A, Oda N, Hidaka T, Kihara Y, Aibara Y, Chayama K, Sasaki S, Kato M, Dote K, Goto C, Liao JK and Higashi Y. 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Elevated atherogenic index of plasma associated with stroke risk in general Chinese. \u003cem\u003eEndocrine\u003c/em\u003e. 2024.\u003c/li\u003e\n\u003cli\u003eWon KB, Kim HJ, Cho JH, Lee SY, Her AY, Kim BK, Joo HJ, Park Y, Chang K, Song YB, Ahn SG, Suh JW, Cho JR, Kim HS, Kim MH, Lim DS, Kim SW, Jeong YH and Shin ES. Different association of atherogenic index of plasma with the risk of high platelet reactivity according to the presentation of acute myocardial infarction. \u003cem\u003eSci Rep\u003c/em\u003e. 2024;14:10894.\u003c/li\u003e\n\u003cli\u003eHochholzer W, Trenk D, Fromm MF, Valina CM, Stratz C, Bestehorn H-P, B\u0026uuml;ttner HJ and Neumann F-J. 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Diagnosis, Treatment, and Long-Term Management of Kawasaki Disease: A Scientific Statement for Health Professionals From the American Heart Association. \u003cem\u003eCirculation\u003c/em\u003e. 2017;135:e927-e999. \u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 and 2 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Atherogenic Index of Plasm, Mortality, Acute Coronary Syndrome, Cohort Study","lastPublishedDoi":"10.21203/rs.3.rs-7645153/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7645153/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eThe atherogenic index of plasma (AIP) is a potential marker for cardiovascular risk, but its association with mortality in acute coronary syndrome (ACS) patients remains unclear. This study aimed to evaluate the relationship between AIP and mortality risk in ACS patients, with stratified analyses by sex and age.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003e This cohort study enrolled 2,200 patients with ACS, stratified according to AIP quartiles. The association between AIP levels and all-cause mortality was evaluated using Cox proportional hazards regression models during the 10-year follow-up period. Additionally, sex- and age-stratified analyses were performed, and potential non-linear relationships were examined using two-piecewise linear regression models.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eHigher AIP levels were associated with reduced mortality risk (HR: 0.58, 95% CI: 0.42\u0026ndash;0.79, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), consistent in adjusted models (Model 1: HR: 0.53, 95% CI: 0.35\u0026ndash;0.79, P\u0026thinsp;=\u0026thinsp;0.002; Model 2: HR: 0.54, 95% CI: 0.36\u0026ndash;0.81, P\u0026thinsp;=\u0026thinsp;0.003). In sex-stratified analyses, no significant association was found in females, but males in the highest AIP quartile had a lower mortality risk (HR: 0.64, 95% CI: 0.43\u0026ndash;0.95, P\u0026thinsp;=\u0026thinsp;0.03). Patients aged\u0026thinsp;\u0026gt;\u0026thinsp;65 years also showed a significant association (HR: 0.55, 95% CI: 0.37\u0026ndash;0.84, P\u0026thinsp;=\u0026thinsp;0.005). Non-linear analyses revealed a threshold effect in females, with an inflection point at 0.08.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eElevated AIP levels were associated with reduced mortality risk in ACS patients, particularly in males and older individuals. These findings suggest AIP may aid in risk stratification and personalized management of ACS, warranting further validation.\u003c/p\u003e","manuscriptTitle":"Association Between the Atherogenic Index of Plasm and 10-year Mortality in Patients with Acute Coronary Syndrome: Prospective Cohort Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-20 08:37:06","doi":"10.21203/rs.3.rs-7645153/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"114c2fc5-073d-4ac2-a4a7-5bea0fcb5763","owner":[],"postedDate":"October 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-03T18:46:08+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-20 08:37:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7645153","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7645153","identity":"rs-7645153","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}