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The combined predictive power of the atherogenic index of plasma and serum glycated albumin for cardiovascular events in postmenopausal patients with acute coronary syndrome after percutaneous coronary intervention

Lipids in Health and Disease volume 23, Article number: 352 (2024) Cite this article

Abstract

Background

Glycated Albumin (GA) and atherogenic index of plasma (AIP) are two important biomarkers that respectively reflect lipid and glucose levels. Previous research has revealed their roles in cardiovascular diseases (CVD) and diabetes. However, their combined predictive ability in forecasting cardiovascular events (CVE) after percutaneous coronary intervention (PCI) among postmenopausal acute coronary syndrome (ACS) patients remains insufficiently studied.

Methods

Based on the levels of AIP (AIP-L and AIP-H) and GA (GA-L and GA-H), four groups were used to categorize the patients. The CVE assessed included cardiac death, nonfatal myocardial infarction (MI) and nonfatal stroke. To evaluate the relationship between AIP, GA, and CVE, multivariate Cox regression analyses were performed.

Results

98 patients (7.5%) experienced CVE during follow-up. AIP and GA were revealed as strong independent predictors of CVE through multivariate analysis (AIP: HR 3.324, 95%CI 1.732–6.365, P = 0.004; GA: HR 1.098, 95% CI 1.023–1.177, P = 0.009). In comparison to those in the initial group (AIP-L and GA-L), the fourth group (AIP-H and GA-H) of patients exhibited the greatest CVE risk (HR 2.929, 95% CI 1.206–5.117, P = 0.018). Derived from the model of baseline risk, the combination of AIP + GA significantly enhanced the AUC, meanwhile combining AIP and GA levels maximized prognostic accuracy in the baseline risk model.

Conclusions

This study found that the combined measurement of AIP and GA significantly enhanced the predictive capability for CVE following PCI in postmenopausal ACS patients. By integrating these two biomarkers, it became possible to more accurately identify high-risk individuals and provided clinicians with new predictive tools for postmenopausal ACS patients in risk assessment and management.

Background

With global population aging and lifestyle changes, the incidence of diabetes, ischemic heart disease, and stroke is on the rise, posing a significant threat to human health [1]. Despite progress over the past 30 years, atherosclerotic cardiovascular disease (ASCVD) mortality rates are currently rising, with middle-aged women experiencing the fastest relative increase [2]. In this context, menopause, as a significant stage of female aging, becomes particularly important. It refers to the permanent cessation of menstruation due to the loss of ovarian function and has considerable impacts on women’s social, reproductive, physiological, and psychological health [3]. Additionally, menopause brings significant metabolic and cardiovascular changes, resulting in a markedly increased risk of cardiovascular diseases (CVD) in menopausal women [4]. This trend may be partly attributed to the decline in estrogen levels in menopausal women, which weakens the protective cardiovascular effects [5]. Therefore, identifying biomarkers to predict and manage CVD risk in menopausal women has become particularly important. The atherogenic index of plasma (AIP), which reflects lipid levels and atherosclerosis risk, is gaining increasing attention. AIP has shown significant prognostic ability in traditional CVD patients and offers greater accuracy in predicting future cardiovascular events (CVE) [6]. Previous studies have suggested that in postmenopausal women, AIP may be a strong predictor of coronary artery disease (CAD) risk [7]. Furthermore, our preliminary research suggested that AIP may serve as an independent marker of CAD risk in menopausal Chinese Han women, potentially surpassing traditional lipid indicators [8], and our findings also indicated that AIP could predict CVE in patients with prediabetes complicated by unstable angina pectoris (UAP), highlighting its prognostic ability [9]. In addition, another important biomarker is glycated albumin (GA), essential to diabetes management. The clinical utility of GA measurement lies in its multifunctionality, serving both as an inflammatory mediator and as a marker for tracking high blood sugar and other diabetes complications [10]. In recent years, interest in GA has been steadily increasing, particularly in the field of diabetes monitoring, where it serves as a complementary biomarker to blood glucose and glycated hemoglobin A1c (HbA1c) [11]. Additionally, elevated serum GA levels can contribute to the formation of atherosclerotic plaques [12]. It was noteworthy that, in non-ST segment elevated myocardial infarction-acute coronary syndrome (NSTEMI-ACS) patients receiving percutaneous coronary intervention (PCI) treatment, GA was highly correlated with adverse outcomes, indicating in NSTEMI-ACS GA as a primary marker of adverse events [13]. In summary, AIP and GA are two important biomarkers in diabetes and CVD. While existing research has revealed their roles in these conditions, their combined predictive capability in forecasting CVE post-PCI in postmenopausal ACS patients remain insufficiently studied. Given the hormonal changes and increased susceptibility to lipid and glucose abnormalities in postmenopausal women, they face heightened risk of CVE following PCI. Building upon prior research, this study intended to investigate the combined predictive abilities of GA + AIP for postmenopausal ACS patients following PCI.

Methods

Study population

1305 postmenopausal patients were consecutively enrolled, who hospitalized at the Beijing Anzhen Hospital undergoing coronary angiography with the diagnosis with ACS and PCI treatment from January 2018 to December 2018. The criteria for exclusion were specified as: (1) heart failure, coronary artery bypass grafting (CABG) history, or cardiogenic shock; (2) incomplete clinical, laboratory or angiographic data; (3) PCI-related complications or failure; (4) in-hospital mortality or complications; (5) severe hepatic conditions and presence of other significant comorbidities; (6) extreme body mass index (BMI) and suspected familial hypertriglyceridemia; (7) estimated glomerular filtration rate (eGFR) below 30 defined as severe renal impairment. Depending on the median level of AIP, individuals were categorized into two groups (AIP-L group: <=0.0843, n = 653; AIP-H group: >0.0843, n = 652). Similarly, according to the median level of GA, two groups were formed from the patients (GA-L group: <=15.4%, n = 659; GA-H group: >15.4%, n = 646). Furthermore, 4 groups were stratified among the patients based on their AIP and GA levels: AIP-L + GA-L, AIP-H + GA-L, AIP-L + GA-H group, and AIP-H + GA-H. Strictly adhered to the principles of the Declaration of Helsinki, ethics committee approval was granted for this study from Beijing Anzhen Hospital. All patients gave in written or oral form informed consent (Fig. 1).

Fig. 1
figure 1

Flow chart of the study population enrollment. PCI, percutaneous coronary intervention; CABG, coronary artery bypass grafting; BMI, body mass index; TG, triglycerides; eGFR, estimated glomerular filtration rate; CVE, cardiovascular events; AIP, atherogenic index of plasma; GA, glycated albumin; AIP-L + GA-L, lower AIP level + lower GA level; AIP-H + GA-L, higher AIP level + lower GA level; AIP-L + GA-H, lower AIP level + higher GA level; AIP-H + GA-H, higher AIP level + higher GA level

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Definitions and data collection

Data on patient demographics, smoking habits, detailed medical histories, and other pertinent medical information from electronic health records were methodically retrieved, which was comprised of past medical histories such as previous myocardial infarction (MI), hypertension, T2DM, previous PCI, hyperlipemia, and previous stroke. ACS referred to unstable ST-segment elevation myocardial infarction (STEMI), UAP, and NSTEMI, diagnosed according to established guidelines [14]. Through elevated blood glucose levels or self-reported use of oral hypoglycemic drugs (OAD) or insulin, categorized as casual blood glucose levels > = 11.1 mmol/L, fasting blood glucose (FBG) levels > = 7.0 mmol/L, or two-hour postprandial levels > 11.1 mmol/L after a 75 g oral glucose tolerance test, T2DM was diagnosed [15]. Based on persistent blood pressure level > = 140/90 mmHg, or the continuous medication usage of antihypertension, hypertension was diagnosed [16].

Fasting blood samples collected from veins were obtained for assessing high sensitivity C-reactive protein (hs-CRP), triglycerides (TG), eGFR, creatinine (Cr), total cholesterol (TC), GA, low-density lipoprotein cholesterol (LDL-C), FBG, high-density lipoprotein cholesterol (HDL-C), HbA1c, and uric acid using standardized laboratory techniques. With a two-dimensional modified Simpson’s method, cardiac function was additionally assessed using the left ventricular ejection fraction (LVEF) measurement, offering critical insights into heart function. A calculator available online at http://syntaxscore.com/ was employed to determine the initial Synergy between PCI with TAXUS (a drug-eluting stent utilizing paclitaxel) and Cardiac Surgery (SYNTAX) score. Two independent reviewers analyzed the preprocedural angiograms, unaware of the patients’ initial clinical details and outcomes. A third evaluator was consulted to achieve consensus in cases where discrepancies arose between the two reviewers. This methodological choice was deemed crucial to ensure a robust and unbiased assessment of the initial synergy, as it allowed for a more comprehensive evaluation by integrating diverse perspectives and expertise. All data were then recorded in the specialized digital database and underwent assessment of quality. AIP, introduced by Dobiásová and Frohlich in 2001, was calculated: AIP = log10 (TG/HDL-C) [17].

Endpoints and following up

At 1, 3, 6, and 12-months, evaluations of follow-up occurred after discharge, and then annually, either via clinic visits or phone calls. Skilled professionals recorded any outcomes in the follow-up period. In this investigation, the observational endpoint was the occurrence of CVE, which included cardiac death, nonfatal MI, and nonfatal stroke. Diagnoses of MI and stroke were made according to internationally recognized guidelines [14, 18]. All clinical endpoints were verified by reviewing medical records when necessary. During the period in each patient’s follow-up, the first adverse event occurred was designated as the CVE.

Statistical analysis

Continuous variables were reported either as median with interquartile range or mean ± standard deviation. Depending upon the distribution of data, for continuous variables, differences in baseline characteristics between groups were analyzed utilizing the Mann-Whitney U test or t-test and the Fisher’s exact test or chi-squared test expressed: counts with percentages for categorical variables. Based on the median of AIP and GA, the cumulative survival rates free of CVE were evaluated utilizing Kaplan-Meier analysis. This method was significant for estimating the survival function over time and allowed for the comparison of survival rates across different patient groups, effectively accounting for censored data, which enhanced the reliability of findings. Employing the log-rank test, differences between the lower and higher groups were evaluated. Both univariate and multivariate Cox regression analyses were used to assess the predictive values of AIP and GA for CVE. The univariate model evaluated the impact of each variable independently, while the multivariate model accounted for the simultaneous influence of multiple variables, allowing for a more comprehensive understanding of their independent effects on survival outcomes. The fully multivariate Cox regression model included variables such as age, BMI, current smoking, previous MI, previous stroke, previous PCI, T2DM, hypertension, hyperlipemia, LVEF, AIP, GA, TC, creatinine, LDL-C, hs-CRP, SYNTAX score, HbA1c and FBG. These variables were selected based on clinical expertise. TG and HDL-C were excluded as determinants of AIP. The 95% confidence interval (CI) and hazard ratio (HR) for CVE were calculated through treating AIP and GA as both a categorical and continuous variable. When treated as categorical, the lower median of AIP and GA served as the reference. As continuous variables, AIP and GA were normalized using the Z-score method to facilitate intuitive comparison of their predictive values, with HR examined per unit increase in normalized score. In addition, to ascertain the consistent predictive value of AIP and GA across different demographic characteristics and comorbidities, several subgroup analyses were conducted. Moreover, the continuous (linear or non-linear) relationship between AIP/GA and CVE risk was depicted using restricted cubic splines (RCS) based on the above adjusted Cox regression model. Through the application of receiver operating characteristic (ROC) analysis, the AIP and GA diagnostic effectiveness for predicting CVE was evaluated based on baseline risk model. Through using the Z-test, the area under the ROC curves (AUC) was calculated and then compared. Additionally, continuous net reclassification improvement (NRI), and integrated discrimination improvement (IDI) were assessed for determining the AIP and GA incremental value in risk stratification. Statistical analyses were conducted utilizing SPSS (version 23.0), MedCalc (version 20.0), and R software (version 4.3.1). Significance was determined at a two-tailed P-value < 0.05.

Results

Baseline information

The baseline analyses were presented in Tables 1, 2 and 3. 98 patients (7.50%) experienced CVE, which consisted of 16 (1.2%) cardiac death, 70 (5.4%) nonfatal MI, and 19 (1.5%) nonfatal strokes. Among the 1305 patients in this study, the average age was 64.92 ± 7.28 years.

Table 1 Baseline characteristics of study patients with and without CVE
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Table 2 Baseline clinical characteristics of patients stratified by the AIP and GA
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Table 3 Comparison of endpoint events stratified by the AIP and GA
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Compared to without CVE, patients experiencing CVE exhibited a markedly higher prevalence of T2DM., previous PCI, hypertension, left main (LM) disease, and DES/DCB use. Additionally, individuals with CVE showed significant higher SBP, TG, AIP, GA, hs-CRP, FBG, HbA1c, and SYNTAX score. Meanwhile, significant lower HDL-C, LVEF, and diameter of stents were indicated in patients with CVE, who also had lower prevalence of one-vessel disease and complete revascularization. Furthermore, the rate of insulin and OAD use at discharge was significantly lower among non-CVE group.

Compared with patients in AIP-L + GA-L group, patients in AIP-H + GA-H group had a significant higher prevalence with hyperlipemia, T2DM, previous PCI, two-vessel disease, chronic total occlusion (CTO) disease, and diffuse lesion. Besides, patients with higher AIP and higher GA showed significant higher age, BMI, TG, TC, LDL-C, Cr, AIP, GA, hs-CRP, FBG, uric acid, HbA1c. Simultaneously, significant lower HDL-C, LVEF, eGFR, and diameter of stents were observed in AIP-H + GA-H group, which also had lower prevalence of one-vessel disease and complete revascularization. What’s more, the rate of insulin and OAD use was lower in AIP-L + GA-L group.

Associations of AIP levels and CVE

In the univariate analysis, variables correlated with CVE included previous PCI, T2DM, hypertension, hyperlipemia, AIP, GA, hs-CRP, FBG, HbA1c, and SYNTAX score. As continuous variables for analysis, multivariate Cox regression indicated AIP and GA were independent predictors for CVE (AIP: HR 3.324, 95%CI 1.732–6.365, P = 0.004; GA: HR 1.098, 95% CI 1.023–1.177, P = 0.009) (Table 4).

Table 4 Univariate and multivariate Cox regression analysis for CVE
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Based on the median level of AIP (AIP-L: AIP < = 0.0843, n = 653; AIP-H: AIP > 0.0843, n = 652), individuals were partitioned into two groups. Depicted in Table 5, CVE rates in AIP-L and AIP-H were 5.4% and 9.7% with significant difference. The Kaplan-Meier survival analysis revealed a rise in CVE occurrences within the higher AIP group (Fig. 2A) (Log-rank P = 0.003). Regarding individual adverse events, nonfatal MI increased with higher AIP (Fig. 3D) (Log-rank P = 0.027), while cardiac death (Fig. 3A) and stroke (Fig. 3G) showed no differences in the two AIP groups. After adjusting for BMI, age, current smoking, previous MI, previous stroke, FBG, previous PCI, T2DM, hypertension, hyperlipemia, LDL-C, LVEF, AIP, GA, TC, creatinine, hs-CRP, HbA1c and SYNTAX score, multivariate Cox regression revealed group of AIP-H exhibited a higher CVE risk (HR 1.835, 95%CI 1.214–2.775, P = 0.004)(Table 5). It was revealed AIP was correlated with the CVE risk positively by RCS curves (P for overall = 0.018; Fig. 4A).

Table 5 Associations of AIP and GA categories with CVE
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Fig. 2
figure 2

The event-free survival rate in AIP , GA, and combined groups for CVE. (A) Kaplan-Meier curves of AIP for CVE; (B) Kaplan-Meier curves of GA for CVE; (C) Kaplan-Meier curves of AIP + GA for CVE. AIP, atherogenic index of plasma; GA, glycated albumin; AIP-L + GA-L, lower AIP level + lower GA level; AIP-H + GA-L, higher AIP level + lower GA level; AIP-L + GA-H, lower AIP level + higher GA level; AIP-H + GA-H, higher AIP level + higher GA level

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Fig. 3
figure 3

The event-free survival rate in AIP, GA, and combined groups for the individual adverse events. (A) Kaplan-Meier curves of AIP for cardiac death; (B) Kaplan-Meier curves of GA for cardiac death; (C) Kaplan-Meier curves of AIP + GA for cardiac death; (D) Kaplan-Meier curves of AIP for nonfatal MI; (E) Kaplan-Meier curves of GA for nonfatal MI; (F) Kaplan-Meier curves of AIP + GA for nonfatal MI; (G) Kaplan-Meier curves of AIP for nonfatal stroke; (H) Kaplan-Meier curves of GA for nonfatal stroke; (I) Kaplan-Meier curves of AIP + GA for nonfatal stroke. MI, myocardial infarction; AIP, atherogenic index of plasma; GA, glycated albumin; AIP-L + GA-L, lower AIP level + lower GA level; AIP-H + GA-L, higher AIP level + lower GA level; AIP-L + GA-H, lower AIP level + higher GA level; AIP-H + GA-H, higher AIP level + higher GA level

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Fig. 4
figure 4

Restricted cubic spline curves for the association of AIP and GA with the risk of CVE in the adjusted model. (A) RCS of AIP for the risk of CVE; (B) RCS of GA for the risk of CVE. AIP, atherogenic index of plasma; GA, glycated albumin; CVE, cardiovascular events; RCS, restricted cubic spline. Adjusted model included: age, BMI, current smoking, previous MI, previous stroke, previous PCI, T2DM, hypertension, hyperlipemia, LVEF, TC, creatinine, LDL-C, hs-CRP, SYNTAX score, HbA1c and FBG

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Associations of GA levels and CVE

Similarly, based on the median level of GA (GA-L: GA < = 15.4%, n = 659; GA-H: GA > 15.4%, n = 646), individuals were partitioned into two groups. Depicted in Table 5, the CVE rates in GA-L and GA-H were 4.7% and 10.3% with significant difference. Kaplan–Meier analysis revealed CVE increased with higher GA (Fig. 2B) (Log-rank P < 0.001). Regarding individual adverse events, cardiac death (Fig. 3B), nonfatal MI (Fig. 3E), and stroke (Fig. 3H) increased with higher GA levels (cardiac death: Log-rank P = 0.011; nonfatal MI: Log-rank P = 0.010; stroke: Log-rank P = 0.010). The adjusted HR for higher GA was 2.828 (95% CI 1.491–3.494, P < 0.001). RCS curves indicated GA was associated with CVE risk positively (P for overall < 0.001; Fig. 4B).

Inter‑relationship of AIP, GA levels and CVE

To evaluate the interaction between AIP, GA, and CVE, Four groups were stratified among the patients based on levels of AIP and GA: AIP-L + GA-L group (n = 324), AIP-H + GA-L group, (n = 335), AIP-L + GA-H group (n = 329), and AIP-H + GA -H group, (n = 317). The CVE rates in these groups were 2.8%, 6.6%, 7.9%, and 12.9%, with significant difference (Table 3). When compared with the AIP-L + GA-L group, the AIP-H + GA -L, AIP-L + GA-H, and AIP-H + GA -H groups had 2.004-, 2.375-, and 4.915-fold higher CVE risks. After factors adjusted, the AIP-H + GA -L, AIP-L + GA-H, and AIP-H + GA-H groups had 2.318-, 2.719-, and 2.929-fold higher CVE risks [HR (95% CI): 2.318 (0.940–3.834), P = 0.068; 2.719 (1.145–4.452), P = 0.023; 2.929 (1.206–5.117), P = 0.018] (Table 5). As illustrated in Fig. 2C, it was shown CVE was highest in G4 among the 4 groups by Kaplan–Meier survival analysis. As for individual adverse events, cardiac death (Fig. 3C), nonfatal MI (Fig. 3F), and stroke (Fig. 3I) was highest in the G4 group among the four groups (cardiac death: Log-rank P = 0.016; nonfatal MI: Log-rank P = 0.008; stroke: Log-rank P = 0.017).

The predictive significance of AIP and GA for CVE in subgroup analysis

After adjusting for multiple factors including covariates performed in adjusted Cox regression model aside from what utilized in stratification, both AIP and GA were significant predictors of CVE across various subgroups. The association between AIP and GA with CVE, stratified by age, BMI, hypertension, T2DM, current smoking, LDL-C, LVEF, hyperlipemia, HbA1c, and type of ACS, was illustrated in Fig. 5.

Fig. 5
figure 5

Forest plot illustrating the association of the AIP and GA with the risk of CVE stratified by different subgroups. AIP, atherogenic index of plasma; GA, glycated albumin; CVE, cardiovascular events; BMI, body mass index; T2DM, type 2 diabetes mellitus; LDL-C, low-density lipoprotein cholesterol; LVEF, left ventricular ejection fraction; UAP, unstable angina pectoris; STEMI, ST-segment elevation myocardial infarction; NSTEMI, non-ST segment elevated myocardial infarction. Adjusted model included: previous MI, previous stroke, previous PCI, TC, creatinine, hs-CRP, SYNTAX score, and FBG

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Incremental effect of the AIP, GA and AIP + GA for predicting CVE

The inclusion of both AIP and GA significantly enhanced the AUC derived from the model of baseline risk, including age, BMI, current smoking, previous MI, previous stroke, hs-CRP, FBG, previous PCI, T2DM, hypertension, LDL-C, hyperlipemia, LVEF, TC, creatinine, HbA1c and SYNTAX score (Table 6; Fig. 6C) (AUC: baseline risk model 0.689 vs. baseline risk model + AIP + GA, 0.738, P for comparison = 0.009). Notwithstanding, the independent inclusion of AIP and GA did not substantially improve the AUC of the initial risk model (Table 6; Fig. 6A, B). Moreover, the combined use of AIP and GA markedly enhanced reclassification and discrimination capabilities exceeding those of the model of baseline risk, demonstrating a category-free NRI of 0.359 and an IDI of 0.048, surpassing the individual contributions of AIP or GA alone (Table 7).

Table 6 C-statistics for discrimination ability of various models
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Table 7 Category-free NRI and IDI for the incremental predictive values of various models
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Fig. 6
figure 6

C-statistics evaluating incremental effect of AIP, GA or AIP + GA beyond baseline risk model. AIP, atherogenic index of plasma; GA, glycated albumin; CVE, cardiovascular events. Baseline risk model included: age, BMI, current smoking, previous MI, previous stroke, previous PCI, T2DM, hypertension, hyperlipemia, LVEF, TC, creatinine, LDL-C, hs-CRP, SYNTAX score, HbA1c and FBG

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Discussion

Main findings

This study pioneered the investigation into the combined predictive efficacy of the AIP and serum GA for CVE in postmenopausal patients with ACS after PCI. Our findings revealed that both GA and AIP, when combined, serve as significant independent predictors of CVE in this specific patient population. The main findings included that: (1) multivariate Cox regression demonstrated both AIP and GA levels independently predicted CVE; (2) cumulative CVE was highest in the AIP-H + GA-H group among the four groups, and after controlling for potential variables, compared to AIP-L + GA-L group, the AIP-H + GA-H groups had 2.929-fold higher risks of CVE; (3) after adjusting for multiple factors, both AIP and GA were significant predictors of CVE across various subgroups; (4) the combination of AIP and GA significantly enhanced the AUC derived from the baseline risk model, meanwhile combining AIP and GA levels maximized prognostic accuracy in the baseline risk model. The results underscored the critical importance of considering both lipid and glucose abnormalities, especially in the context of hormonal changes that might heighten cardiovascular risk.

The roles of AIP and GA in atherosclerosis

Atherosclerosis is a systemic and inflammatory disease, where inflammation at the site of atherosclerotic plaques plays a crucial pathophysiological role in acute plaque rupture. Dyslipidemia is one of the most important components of this event chain and exerts a significant influence on the development of coronary atherosclerosis [19]. AIP served as a surrogate for small, dense LDL particles. An elevation in AIP levels indicated a higher likelihood of oxidized particles forming foam cells, leading to an increase in oxidized apolipoprotein B and LDL-C. AIP values maintained at a high level indicated sustained high TG levels and/or relatively low HDL-C levels. Following an increase in TG levels, they competed with glucose for entry into cells, reducing the quantity and activity of insulin receptors on adipocytes, thus preventing insulin from binding to its receptors [20]. Additionally, high TG levels led to an increase in free fatty acids and the formation of toxic lipids, which altered insulin signaling and caused excessive secretion of glucagon [21]. However, low levels of HDL-C decreased cholesterol efflux, leading to the accumulation of cholesterol in pancreatic beta cells, which in turn caused beta cell dysfunction, impaired insulin secretion, elevated blood glucose, and beta cell apoptosis [22, 23]. These potential mechanisms provided a pathophysiological explanation for the association between AIP and atherosclerosis. Moreover, elevated AIP values were directly associated with endothelial dysfunction through promotion of lipid peroxidation, resulting in excessive expression of activation of oxygen free radicals and adhesion molecules. These factors collectively contribute to heightened atherogenicity [24]. In addition to AIP, GA was also a crucial biomarker. It reflected short-term (2 to 4 weeks) glycemic control and may provide supplementary information to HbA1c in identifying individuals at risk for diabetes or its complications [25]. Advanced glycation end products (AGE) on albumin played a potential role in atherosclerosis by impairing endoplasmic reticulum function associated with macrophage cholesterol efflux. This process promoted diabetic atherosclerosis through glycation levels in albumin within the body [26]. Additionally, Machado-Lima et al. has found that receptor for AGE (RAGE)-mediated AGE-albumin has detrimental effects on cholesterol efflux in macrophages [27], and Minanni et al. pointed out reducing AGE in albumin can improve cholesterol efflux [28]. Furthermore, Gomes et al. have demonstrated AGE directly contributed to albumin’s involvement in atherosclerosis development in dyslipidemic mice. and showed that this effect was independent of the presence of diabetes and partially involves inducing lipid peroxidation and inflammation to modulate the renin-angiotensin system [29]. In the glycation form, albumin not only exhibited changes in its physiological functions but also acquired pathological phenotypes. High levels of GA could lead to irreversible damage in various organs and tissues, making it a major target for diabetic complications [30, 31]. Additionally, GA could activate and aggregate platelets, upregulating the expression of adhesion molecules involved in atherosclerotic plaque formation and promoting oxidative processes [32, 33]. As a primary mechanism through which GA exerted its damaging effects, the activation of RAGE subsequently participated in the activation of the production of pro-inflammatory cytokines and growth factors, cell apoptosis, oxidative stress, and pro-thrombotic activity, all of which were pathologically associated with elevated levels of AGE and GA [34, 35]. Hence, assessing the possible impact of AIP and GA as predictive biomarkers may hold substantial clinical relevance for risk assessment in ACS patients. Given their association with atherosclerotic processes, these biomarkers could aid in identifying patients at elevated CVE risk, thereby guiding more personalized and effective therapeutic interventions.

The association of AIP and GA with CVE

Based on CHARLS database, a study investigating the connection between AIP and cardiovascular metabolic diseases in mid-aged and elderly populations indicated that dynamic monitoring of AIP is critically important for preventing and managing cardiovascular metabolic diseases, including diabetes, CAD, and stroke [36]. Furthermore, both heightened baseline AIP levels and prolonged AIP levels were associated with an increased risk of MI [37], and the risk of Moreover, a distinct positive association existed between AIP and the in-stent restenosis (ISR) risk in ACS patients [38]. In studies concerning AIP and stroke, both longitudinally updated mean AIP and baseline levels were correlated with stroke and ischemic stroke risks [39]. Regarding studies on AIP in women and menopausal women, AIP was markedly linked to carotid artery plaques in CAD patients, with the stronger correlation observed in women compared to men [40]. In a study involving 340 healthy women, AIP was identified as a potential biomarker for early diagnosis of CVE [41]. AIP was linked to all-cause mortality risk independently among elderly women with arterial hypertension, regardless of age, smoking habits, statin therapy, or comorbidities [42]. Meanwhile, there was limited data on the association between GA and CVD risk, and previous studies have been constrained by cross-sectional designs or small prospective studies with limited CVE numbers [43]. Recently, there was increasing research interest in GA. One study found that serum GA was a new predictive marker for forecasting prolonged outcomes in T2DM complicated by stable CAD [44]. Additionally, elevated GA levels were notably linked to the CVD occurrence and its species, even in populations with normal HbA1c levels or without diabetes [45]. Importantly, in low-risk ACS patients undergoing PCI, elevated serum GA levels were correlated with adverse mid-term outcomes particularly among those combined concomitant diabetes [46]. In this study, our findings revealed that elevated AIP and GA, whether assessed continuously or categorically, were correlated with a higher CVE risk. Importantly, this correlation remained significant even after controlling for conventional risk factors. Regarding the imbalance in patient distribution between those without CVE and those with CVE, this imbalance was significant, as it may affect the generalizability of the findings and the statistical power of the analyses. While the data were analyzed by stratifying AIP and GA levels and conducting both univariate and multivariate Cox regression analyses, the presence or absence of CVE was only included in the baseline table. This distribution did not have a substantial impact on the final grouping results.

These findings highlighted that AIP and GA was a robust predictor of CVE independently among postmenopausal patients with ACS undergoing PCI. Moreover, it was indicated a significant correlation between AIP, GA, and CVE by RCS curves. Due to their potential relationship, this study stratified the cohort into 4 groups in compliance with median levels of AIP/GA. Findings demonstrated that patients with elevated levels of both biomarkers had notably increased CVE risks compared to those with lower levels of AIP and GA. What’s more, regarding C-statistics and IDI, attaching both AIP/GA to the built model for CVE provided significant incremental value compared to adding either biomarker alone (AIP or GA). This discovery highlighted the benefit of concurrently assessing both biomarkers for precise CVE prediction. As far as we knew, this study represented the initial investigation into the joint prognostic role of AIP and GA in postmenopausal ACS patients undergoing PCI, revealing their combined impact. It was worth noting that factors such as lifestyle behaviors including diet, physical activity, and medication adherence could significantly impact the relationships observed between AIP, GA, and CVE. Although this study did not include sensitivity analyses to specifically address these unmeasured confounders, acknowledging the potential influence of these unmeasured confounders was essential for a comprehensive interpretation of the findings. Future research should consider these factors to enhance the robustness of the conclusions and provide clearer insights into the role of AIP and GA in cardiovascular risk assessment.

The correlation of AIP and GA with T2DM

In the Chinese population aged 45 and older, AIP exhibited a positive connection with the prediabetes and T2DM risk [47]. Additionally, a quantitative study exploring the exact connection between AIP and the prediabetes risk among a large sample population found a linear positive correlation [48]. Meanwhile, GA as a marker reflecting glycemic control, provided crucial information on cardiovascular risk in diabetic patients. Currently, there was no international consensus or recommendation on the clinical application of GA, but mounting evidence supported its use in clinical practice. Therefore, GA was increasingly considered a novel short-term biomarker for diabetes [49]. Previous research has consistently demonstrated a robust correlation between glycated albumin and microvascular conditions, similar in magnitude to that of HbA1c [50]. Due to hormonal changes, postmenopausal women were more prone to lipid abnormalities and glucose fluctuations, further increasing their risk of CVE after PCI. Considering AIP and GA together, where AIP revealed lipid abnormalities and GA provided information on glycemic control, their combined application can more accurately predict CVD risk. This integrated approach can aid in developing more effective treatment and management strategies to improve patient outcomes.

In clinical settings, the findings regarding the combined predictive value of AIP and GA can be implemented by incorporating these biomarkers into routine cardiovascular risk assessment protocols. This stratification allowed healthcare professionals to assess cardiovascular risk more effectively and tailor prevention strategies accordingly. For instance, patients in the AIP-H and GA-H group were identified as high-risk and could have benefited from more intensive monitoring and early interventions, such as lifestyle modifications or targeted pharmacotherapy. Conversely, those in the AIP-L and GA-L group might have required less aggressive management. The integration of AIP and GA measurements into routine practice could have enhanced risk assessment and improved patient outcomes through personalized treatment approaches.

Strengths and limitations

This study’s strength lied in being the first to specifically assess the combined predictive capabilities of AIP and GA for CVE. AIP focused on lipid metabolism, while GA assessed glycemic control. Their combined application can more accurately reflect the overall cardiovascular risk. Therefore, integrating AIP and GA in clinical practice helped in better predicting and managing CVE risks, thereby improving outcomes. This study also had several limitations. Firstly, GA and AIP were assessed using baseline data, which precluded the evaluation of their longitudinal relationships with CVE risk over time. This limitation highlighted the importance of incorporating dynamic data in future studies, as changes in AIP and GA levels over time could have provided deeper insights into their prognostic value and improved risk stratification for CVE. Secondly, potential influences from long-term use of OAD, insulin, antihypertensive, and lipid-lowering medications on lipid and glucose levels could not be omitted. Thirdly, despite adjusting for numerous confounding factors, residual confounding effects, for example dietary habit, cannot be completely ruled out, so causal relationships cannot be established. Fourthly, being a single-center study conducted in a Chinese population, the results of this study may not be generalizable to wider populations, and there might be hospital admission bias. Meanwhile, this study was conducted on Chinese postmenopausal women, which limited the generalizability of the findings to other populations. Future research should aim to include more diverse populations to validate the results and assess their applicability across different demographics. Moreover, further forward-looking, multicenter randomized controlled, large-sample trials could enhance the reliability of our conclusions. Subsequent research should consider these factors to improve the precision and credibility of the findings.

Conclusions

This study found that the combined measurement of AIP and GA can significantly improve the ability to predict CVE after PCI in postmenopausal patients with ACS. The combination of AIP and GA provided a more comprehensive prognosis assessment.

Data availability

No datasets were generated or analysed during the current study.

Abbreviations

ASCVD:

Atherosclerotic cardiovascular diseases

CVD:

Cardiovascular diseases

AIP:

Atherogenic index of plasma

CVE:

Cardiovascular events

CAD:

Coronary artery disease

UAP:

Unstable angina pectoris

GA:

Glycated albumin

HbA1c:

Glycosylated hemoglobin A1c

T2DM:

Type 2 diabetes mellitus

NSTEMI:

Non-ST segment elevated myocardial infarction

ACS:

Acute coronary syndrome

PCI:

Percutaneous coronary intervention

CABG:

Coronary artery bypass grafting

BMI:

Body mass index

eGFR:

Estimated glomerular filtration rate

MI:

Myocardial infarction

STEMI:

ST-segment elevation myocardial infarction

OAD:

Oral hypoglycemic drugs

TC:

Total cholesterol

TG:

Triglycerides

LDL-C:

Low-density lipoprotein cholesterol

HDL-C:

High-density lipoprotein cholesterol

Cr:

Creatinine

hs-CRP:

High sensitivity C-reactive protein

FBG:

Fasting blood glucose

LVEF:

Left ventricular ejection fraction

SYNTAX:

Synergy between PCI with TAXUS and Cardiac Surgery

SBP:

Systolic blood pressure

DBP:

Diastolic blood pressure

CI:

Confidence interval

HR:

Hazard ratio

RCS:

Restricted cubic spline

ROC:

Receiver operating characteristic

AUC:

Area under the curve

NRI:

Net reclassification improvement

IDI:

Integrated discrimination improvement

LM:

Left main

LAD:

Left anterior descending artery

CTO:

Chronic total occlusion

AGE:

Advanced glycation end product

RAGE:

Receptor for advanced glycation end product

ISR:

In-stent restenosis

References

  1. GBD 2019 Diseases and Injuries Collaborators. Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the global burden of Disease Study 2019. Lancet. 2020;396(10258):1204–22. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/S0140-6736(20)30925-9.

    Article  Google Scholar 

  2. van Roeters JE, Tokgözoğlu LS, Badimon L, Dumanski SM, Gulati M, Hess CN, et al. Women, lipids, and atherosclerotic cardiovascular disease: a call to action from the European atherosclerosis society. Eur Heart J. 2023;44(39):4157–73. https://doiorg.publicaciones.saludcastillayleon.es/10.1093/eurheartj/ehad472.

    Article  CAS  Google Scholar 

  3. Nansseu JR, Moor VJ, Nouaga ME, Zing-Awona B, Tchanana G, Ketcha A. Atherogenic index of plasma and risk of cardiovascular disease among Cameroonian postmenopausal women. Lipids Health Dis. 2016;15:49. https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12944-016-0222-7.

    Article  CAS  PubMed  Google Scholar 

  4. Manson JE, Woodruff TK. Reproductive Health as a marker of subsequent Cardiovascular Disease: the role of Estrogen. JAMA Cardiol. 2016;1(7):776–7. https://doiorg.publicaciones.saludcastillayleon.es/10.1001/jamacardio.2016.2662.

    Article  PubMed  Google Scholar 

  5. North American Menopause Society. The 2012 hormone therapy position statement of: the North American Menopause Society. Menopause. 2012;19(3):257–71. https://doiorg.publicaciones.saludcastillayleon.es/10.1097/gme.0b013e31824b970a.

    Article  Google Scholar 

  6. Abdu FA, Alifu J, Mohammed AQ, Liu L, Zhang W, Yin G, et al. The correlation of atherogenic index of plasma with non-obstructive CAD and unfavorable prognosis among patients diagnosed with MINOCA. Eur J Intern Med Published Online March. 2024;26. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.ejim.2024.03.024.

  7. Wu TT, Gao Y, Zheng YY, Ma YT, Xie X. Atherogenic index of plasma (AIP): a novel predictive indicator for the coronary artery disease in postmenopausal women. Lipids Health Dis. 2018;17(1):197. https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12944-018-0828-z.

    Article  CAS  PubMed  Google Scholar 

  8. Guo Q, Zhou S, Feng X, Yang J, Qiao J, Zhao Y, et al. The sensibility of the new blood lipid indicator–atherogenic index of plasma (AIP) in menopausal women with coronary artery disease. Lipids Health Dis. 2020;19(1):27. https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12944-020-01208-8.

    Article  CAS  PubMed  Google Scholar 

  9. Liu Y, Feng X, Yang J, Zhai G, Zhang B, Guo Q, et al. The relation between atherogenic index of plasma and cardiovascular outcomes in prediabetic individuals with unstable angina pectoris. BMC Endocr Disord. 2023;23(1):187. https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12902-023-01443-x.

    Article  CAS  PubMed  Google Scholar 

  10. Roohk HV, Zaidi AR, Patel D. Glycated albumin (GA) and inflammation: role of GA as a potential marker of inflammation. Inflamm Res. 2018;67(1):21–30. https://doiorg.publicaciones.saludcastillayleon.es/10.1007/s00011-017-1089-4.

    Article  CAS  PubMed  Google Scholar 

  11. Dozio E, Di Gaetano N, Findeisen P, Corsi Romanelli MM. Glycated albumin: from biochemistry and laboratory medicine to clinical practice. Endocrine. 2017;55(3):682–90. https://doiorg.publicaciones.saludcastillayleon.es/10.1007/s12020-016-1091-6.

    Article  CAS  PubMed  Google Scholar 

  12. Moon JH, Chae MK, Kim KJ, Kim HM, Cha BS, Lee HC, et al. Decreased endothelial progenitor cells and increased serum glycated albumin are independently correlated with plaque-forming carotid artery atherosclerosis in type 2 diabetes patients without documented ischemic disease. Circ J. 2012;76(9):2273–9. https://doiorg.publicaciones.saludcastillayleon.es/10.1253/circj.cj-11-1499.

    Article  CAS  PubMed  Google Scholar 

  13. Liu C, Zhao Q, Ma X, Cheng Y, Sun Y, Zhang D, et al. Prognostic implication of serum glycated albumin for patients with non-ST-segment elevation acute coronary syndrome undergoing percutaneous coronary intervention. Cardiovasc Diabetol. 2022;21(1):11. https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12933-022-01446-3.

    Article  CAS  PubMed  Google Scholar 

  14. Byrne RA, Rossello X, Coughlan JJ, Barbato E, Berry C, Chieffo A, et al. 2023 ESC guidelines for the management of acute coronary syndromes. Eur Heart J. 2023;44(38):3720–826. https://doiorg.publicaciones.saludcastillayleon.es/10.1093/eurheartj/ehad191.

    Article  CAS  PubMed  Google Scholar 

  15. ElSayed NA, Aleppo G, Aroda VR, Bannuru RR, Brown FM, Bruemmer D, et al. 2. Classification and diagnosis of diabetes: standards of Care in Diabetes-2023. Diabetes Care. 2023;46(Suppl 1):S19–40. https://doiorg.publicaciones.saludcastillayleon.es/10.2337/dc23-S002.

    Article  CAS  PubMed  Google Scholar 

  16. Mancia G, Kreutz R, Brunström M, Burnier M, Grassi G, Januszewicz A, et al. 2023 ESH guidelines for the management of arterial hypertension the Task Force for the management of arterial hypertension of the European Society of Hypertension: endorsed by the International Society of Hypertension (ISH) and the European Renal Association (ERA). J Hypertens. 2023;41(12):1874–2071. https://doiorg.publicaciones.saludcastillayleon.es/10.1097/HJH.0000000000003480.

    Article  CAS  PubMed  Google Scholar 

  17. Dobiásová M, Frohlich J. The plasma parameter log (TG/HDL-C) as an atherogenic index: correlation with lipoprotein particle size and esterification rate in apob-lipoprotein-depleted plasma (FER(HDL)). Clin Biochem. 2001;34(7):583–8. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/s0009-9120(01)00263-6.

    Article  PubMed  Google Scholar 

  18. Mead GE, Sposato LA, Sampaio Silva G, Yperzeele L, Wu S, Kutlubaev M, et al. A systematic review and synthesis of global stroke guidelines on behalf of the World Stroke Organization. Int J Stroke. 2023;18(5):499–531. https://doiorg.publicaciones.saludcastillayleon.es/10.1177/17474930231156753.

    Article  PubMed  Google Scholar 

  19. Aydınyılmaz F, Özbeyaz NB, Guliyev İ, Algül E, Şahan HF, Kalkan K. Effect of Atherogenic Index of plasma on pre-percutaneous coronary intervention Thrombolysis in Myocardial Infarction Flow in patients with ST Elevation myocardial infarction. Angiol Published Online July. 2023;3. https://doiorg.publicaciones.saludcastillayleon.es/10.1177/00033197231185204.

  20. Jiang Z, Zhu X, Zhao D, Jiang H, Wang X, Su F. Associations between non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio and hyperuricemia: a cross-sectional study. Lipids Health Dis. 2024;23(1):280. https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12944-024-02269-9.

    Article  CAS  PubMed  Google Scholar 

  21. Jung UJ, Choi MS. Obesity and its metabolic complications: the role of adipokines and the relationship between obesity, inflammation, insulin resistance, dyslipidemia and nonalcoholic fatty liver disease. Int J Mol Sci. 2014;15(4):6184–223. https://doiorg.publicaciones.saludcastillayleon.es/10.3390/ijms15046184.

    Article  CAS  PubMed  Google Scholar 

  22. Chen B, Zeng J, Fan M, You Q, Wang C, Wang K, et al. A longitudinal study on the impact of the TyG Index and TG/HDL-C ratio on the risk of type 2 diabetes in Chinese patients with prediabetes. Lipids Health Dis. 2024;23(1):262. https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12944-024-02239-1.

    Article  CAS  PubMed  Google Scholar 

  23. Graham A. Modulation of the Cellular microRNA Landscape: contribution to the Protective effects of high-density lipoproteins (HDL). Biology (Basel). 2023;12(9):1232. https://doiorg.publicaciones.saludcastillayleon.es/10.3390/biology12091232.

    Article  CAS  PubMed  Google Scholar 

  24. Kurklu HA, Tan TS, Ozyuncu N, Baskovski E, Ozdol C. Atherogenic index of plasma predicts obstructive coronary artery disease in patients with stable angina Pectoris. Diagnostics (Basel). 2023;13(20):3249. https://doiorg.publicaciones.saludcastillayleon.es/10.3390/diagnostics13203249.

    Article  CAS  PubMed  Google Scholar 

  25. Selvin E, Rawlings AM, Grams M, Klein R, Sharrett AR, Steffes M, et al. Fructosamine and glycated albumin for risk stratification and prediction of incident diabetes and microvascular complications: a prospective cohort analysis of the atherosclerosis risk in communities (ARIC) study. Lancet Diabetes Endocrinol. 2014;2(4):279–88. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/S2213-8587(13)70199-2.

    Article  PubMed  Google Scholar 

  26. Traldi P, Castilho G, Sartori CH, Machado-Lima A, Nakandakare ER, Corrêa-Giannella ML, et al. Glycated human serum albumin isolated from poorly controlled diabetic patients impairs cholesterol efflux from macrophages: an investigation by mass spectrometry. Eur J Mass Spectrom (Chichester). 2015;21(3):233–44. https://doiorg.publicaciones.saludcastillayleon.es/10.1255/ejms.1322.

    Article  CAS  PubMed  Google Scholar 

  27. Machado-Lima A, López-Díez R, Iborra RT, Pinto RS, Daffu G, Shen X, et al. RAGE mediates cholesterol efflux impairment in Macrophages caused by Human Advanced Glycated Albumin. Int J Mol Sci. 2020;21(19):7265. https://doiorg.publicaciones.saludcastillayleon.es/10.3390/ijms21197265.

    Article  CAS  PubMed  Google Scholar 

  28. Minanni CA, Machado-Lima A, Iborra RT, Okuda LS, de Souza Pinto R, Santana MFM, et al. Persistent effect of Advanced Glycated Albumin driving inflammation and disturbances in cholesterol efflux in macrophages. Nutrients. 2021;13(10):3633. https://doiorg.publicaciones.saludcastillayleon.es/10.3390/nu13103633.

    Article  CAS  PubMed  Google Scholar 

  29. Gomes DJ, Velosa AP, Okuda LS, Fusco FB, da Silva KS, Pinto PR, et al. Glycated albumin induces lipid infiltration in mice aorta independently of DM and RAS local modulation by inducing lipid peroxidation and inflammation. J Diabetes Complications. 2016;30(8):1614–21. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.jdiacomp.2016.07.001.

    Article  PubMed  Google Scholar 

  30. Noels H, Jankowski V, Schunk SJ, Vanholder R, Kalim S, Jankowski J. Post-translational modifications in kidney diseases and associated cardiovascular risk. Nat Rev Nephrol. 2024;20(8):495–512. https://doiorg.publicaciones.saludcastillayleon.es/10.1038/s41581-024-00837-x.

    Article  CAS  PubMed  Google Scholar 

  31. Copur S, Siriopol D, Afsar B, Comert MC, Uzunkopru G, Sag AA, et al. Serum glycated albumin predicts all-cause mortality in dialysis patients with diabetes mellitus: meta-analysis and systematic review of a predictive biomarker. Acta Diabetol. 2021;58(1):81–91. https://doiorg.publicaciones.saludcastillayleon.es/10.1007/s00592-020-01581-x.

    Article  CAS  PubMed  Google Scholar 

  32. Belinskaia DA, Voronina PA, Shmurak VI, Jenkins RO, Goncharov NV. Serum albumin in Health and Disease: esterase, antioxidant, Transporting and Signaling properties. Int J Mol Sci. 2021;22(19):10318. https://doiorg.publicaciones.saludcastillayleon.es/10.3390/ijms221910318.

    Article  CAS  PubMed  Google Scholar 

  33. Blache D, Bourdon E, Salloignon P, Lucchi G, Ducoroy P, Petit JM, et al. Glycated albumin with loss of fatty acid binding capacity contributes to enhanced arachidonate oxygenation and platelet hyperactivity: relevance in patients with type 2 diabetes. Diabetes. 2015;64(3):960–72. https://doiorg.publicaciones.saludcastillayleon.es/10.2337/db14-0879.

    Article  CAS  PubMed  Google Scholar 

  34. Jujic A, Engström G, Nilsson PM, Johansson M. Accumulation of advanced glycation end products in skin and increased vascular ageing in the general population: the Malmö offspring study. J Hypertens. 2024;42(3):530–7. https://doiorg.publicaciones.saludcastillayleon.es/10.1097/HJH.0000000000003627.

    Article  CAS  PubMed  Google Scholar 

  35. Lu H, Xu S, Liang X, Dai Y, Huang Z, Ren Y, et al. Advanced Glycated End products Alter Neutrophil Effect on Regulation of CD4 + T cell differentiation through induction of myeloperoxidase and neutrophil elastase activities. Inflammation. 2019;42(2):559–71. https://doiorg.publicaciones.saludcastillayleon.es/10.1007/s10753-018-0913-5.

    Article  CAS  PubMed  Google Scholar 

  36. Huang X, Wen S, Huang Y, Huang Z. Gender differences in the association between changes in the atherogenic index of plasma and cardiometabolic diseases: a cohort study. Lipids Health Dis. 2024;23(1):135. https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12944-024-02117-w.

    Article  PubMed  Google Scholar 

  37. Zhang Y, Wu S, Tian X, Xu Q, Xia X, Zhang X, et al. Elevated atherogenic index of plasma increased the risk of myocardial infarction in a general population. Ann Epidemiol. 2024;90:1–8. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.annepidem.2023.11.002.

    Article  PubMed  Google Scholar 

  38. Zhu Y, Chen M, Liu K, Gao A, Kong X, Liu Y, et al. Atherogenic Index of Plasma and the risk of In-Stent restenosis in patients with Acute Coronary Syndrome beyond the traditional risk factors. J Atheroscler Thromb. 2022;29(8):1226–35. https://doiorg.publicaciones.saludcastillayleon.es/10.5551/jat.63136.

    Article  CAS  PubMed  Google Scholar 

  39. Zhang Y, Chen S, Tian X, Xu Q, Xia X, Zhang X, et al. Elevated atherogenic index of plasma associated with stroke risk in general Chinese. Endocr Published Online January. 2024;10. https://doiorg.publicaciones.saludcastillayleon.es/10.1007/s12020-023-03677-0.

  40. He Y, Li Z, Yu L, Liu Y, Li L, Yang R, et al. Association between the atherogenic index of plasma and carotid artery plaques in patients with coronary heart disease in different glucose metabolism states: an RCSCD-TCM study in Tianjin, China. Endocrine. 2023;81(2):252–61. https://doiorg.publicaciones.saludcastillayleon.es/10.1007/s12020-023-03389-5.

    Article  CAS  PubMed  Google Scholar 

  41. Fernández-Macías JC, Ochoa-Martínez AC, Varela-Silva JA, Pérez-Maldonado IN. Atherogenic index of plasma: Novel Predictive Biomarker for Cardiovascular illnesses. Arch Med Res. 2019;50(5):285–94. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.arcmed.2019.08.009.

    Article  CAS  PubMed  Google Scholar 

  42. Bendzala M, Sabaka P, Caprnda M, Komornikova A, Bisahova M, Baneszova R, et al. Atherogenic index of plasma is positively associated with the risk of all-cause death in elderly women: a 10-year follow-up. Wien Klin Wochenschr. 2017;129(21–22):793–8. https://doiorg.publicaciones.saludcastillayleon.es/10.1007/s00508-017-1264-1.

    Article  PubMed  Google Scholar 

  43. Selvin E, Rawlings AM, Lutsey PL, Maruthur N, Pankow JS, Steffes M, et al. Fructosamine and Glycated Albumin and the risk of Cardiovascular outcomes and Death. Circulation. 2015;132(4):269–77. https://doiorg.publicaciones.saludcastillayleon.es/10.1161/CIRCULATIONAHA.115.015415.

    Article  CAS  PubMed  Google Scholar 

  44. Yang ZK, Shen Y, Shen WF, Pu LJ, Meng H, Zhang RY, et al. Elevated glycated albumin and reduced endogenous secretory receptor for advanced glycation endproducts levels in serum predict major adverse cardio-cerebral events in patients with type 2 diabetes and stable coronary artery disease. Int J Cardiol. 2015;197:241–7. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.ijcard.2015.06.003.

    Article  PubMed  Google Scholar 

  45. Mihara A, Ohara T, Hata J, Honda T, Chen S, Sakata S, et al. Association between serum glycated albumin and risk of cardiovascular disease in a Japanese community: the Hisayama Study. Atherosclerosis. 2020;311:52–9. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.atherosclerosis.2020.08.016.

    Article  CAS  PubMed  Google Scholar 

  46. Zhang J, Du Y, Hu C, Liu Y, Liu J, Gao A, et al. Elevated glycated albumin in serum is Associated with adverse cardiac outcomes in patients with Acute Coronary Syndrome who underwent revascularization therapy. J Atheroscler Thromb. 2022;29(4):482–91. https://doiorg.publicaciones.saludcastillayleon.es/10.5551/jat.61358.

    Article  PubMed  Google Scholar 

  47. Jiang L, Li L, Xu Z, Tang Y, Zhai Y, Fu X, et al. Non-linear associations of atherogenic index of plasma with prediabetes and type 2 diabetes mellitus among Chinese adults aged 45 years and above: a cross-sectional study from CHARLS. Front Endocrinol (Lausanne). 2024;15:1360874. https://doiorg.publicaciones.saludcastillayleon.es/10.3389/fendo.2024.1360874.

    Article  PubMed  Google Scholar 

  48. Zheng X, Zhang X, Han Y, Hu H, Cao C. Nonlinear relationship between atherogenic index of plasma and the risk of prediabetes: a retrospective study based on Chinese adults. Cardiovasc Diabetol. 2023;22(1):205. https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12933-023-01934-0.

    Article  CAS  PubMed  Google Scholar 

  49. Zendjabil M. Glycated albumin. Clin Chim Acta. 2020;502:240–4. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.cca.2019.11.007.

    Article  CAS  PubMed  Google Scholar 

  50. Nathan DM, McGee P, Steffes MW, Lachin JM, DCCT/EDIC Research Group. Relationship of glycated albumin to blood glucose and HbA1c values and to retinopathy, nephropathy, and cardiovascular outcomes in the DCCT/EDIC study. Diabetes. 2014;63(1):282–90. https://doiorg.publicaciones.saludcastillayleon.es/10.2337/db13-0782.

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

We thank all our colleagues at the department of Cardiology, Beijing Anzhen Hospital, Capital Medical University. Meanwhile, XXF is currently a medical doctoral candidate co-trained by Beijing Anzhen Hospital, Capital Medical University, and the University of California, Los Angeles (UCLA), in the United States, so we want to thank Prof. Aldons J. Lusis from UCLA for providing the learning opportunity for XXF.

Funding

XXF was supported by the grant from China Scholarship Council (CSC) (Grant No. 202308110233). QYG was supported by the grant from Beijing Hospitals Authority Youth Programme (Grant No. QML20210601) and National Natural Science Foundation of China (Grant No. 82300368). YJZ was supported by National Key Research and Development Program of China (Grant No. 2022YFC3602500), Beijing Municipal Administration of Hospitals’ Mission plan (Grant No. SML20180601), Capital’s Funds for Health Improvement and Research (Grant No. CFH 2020-2-2063), and Beijing Municipal Natural Science Foundation (Grant No. 7202041).

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  1. Yujie Zhou and Qianyun Guo contributed equally to this work and are joint last authors.

Authors and Affiliations

  1. Beijing Key Laboratory of Precision Medicine of Coronary Atherosclerotic Disease, Clinical Center for Coronary Heart Disease, Department of Cardiology, Beijing Anzhen Hospital, Beijing Institute of Heart Lung and Blood Vessel Disease, Capital Medical University, Beijing, China

    Xunxun Feng, Yang Liu, Jiaqi Yang, Shiwei Yang, Zhiming Zhou, Yujie Zhou & Qianyun Guo

  2. Department of Medicine, Division of Cardiology, University of California, Los Angeles, CA, USA

    Xunxun Feng

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Contributions

XXF, QYG and YJZ participated in the study design. XXF, QYG, YL, JQY, SWY, and ZMZ participated in data collection. XXF, JQY and YL performed the statistical analysis. XXF drafted the article. All the authors read and approved the final manuscript.

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Correspondence to Yujie Zhou or Qianyun Guo.

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The study protocol strictly adhered to the Declaration of Helsinki. All laboratory tests in this study were reviewed by the Ethics Review Board of the Beijing Anzhen Hospital. All patients signed an informed consent form.

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Feng, X., Liu, Y., Yang, J. et al. The combined predictive power of the atherogenic index of plasma and serum glycated albumin for cardiovascular events in postmenopausal patients with acute coronary syndrome after percutaneous coronary intervention. Lipids Health Dis 23, 352 (2024). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12944-024-02335-2

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Keywords

Lipids in Health and Disease

ISSN: 1476-511X