Over the past decade, global deaths from cardiovascular disease have risen by 12.5%. Cardiovascular diseases (CVD) now contribute to one-third of global fatalities [1]. This increase was largely due to population growth and aging, with the highest mortality rates occurring in South and East Asian countries due to their large and expanding populations [2]. Among patients who died from CVD, coronary artery disease (CAD), a severe narrowing of the coronary arteries caused by a variety of factors, accounted for the highest proportion of fatalities [2, 3]. Furthermore, epidemiological studies indicated that ischemic heart disease (IHF) was the primary cause of heart failure (HF) [4]. CAD, a major subtype of ischemic heart disease, triggers physiological changes that can negatively affect myocardial function, contributing to the development of HF [5]. When CAD was followed by secondary HF, also known as IHF [6], patients generally tended to have a worse prognosis [7]. Therefore, the use of simple and easily available laboratory predictors for the prognosis of IHF and the early identification of high-risk patients would be beneficial in reducing the burden of healthcare on society globally (especially in developing countries). Several previous studies have tested predictors of IHF, such as the systemic inflammation response index, positron emission tomographic metrics, and B-natriuretic peptide (BNP) [8, 9, 10]. Some studies further provided new insights and approaches for identifying high-risk patients and advancing precise medicine through artificial intelligence and other methods [11]. However, some new risk factors need to be investigated, which may lead to new guidelines for early clinical interventions in IHF patients with a poor prognosis.

The atherogenic index of plasma (AIP) is calculated as the logarithm of the triglyceride (TG) to high-density lipoprotein cholesterol (HDL-C) ratio, providing an indication of the balance between these two lipid components [12]. AIP is a reliable marker for assessing the extent of coronary atherosclerosis and has proven to be an effective prognostic tool for various CVDs [13]. Previous studies have also found a high AIP was associated with a poor prognosis in diabetes, renal insufficiency, acute ischemic stroke and other conditions [14, 15, 16]. For patients with IHF, however, the predictive validity of AIP has not been fully elucidated from current studies. TG and HDL-C, as components of AIP, have been found to be predictive of the prognosis of IHF in previous studies [17, 18]. Hypertriglyceridemia accelerates the development of CVD through mechanisms such as promoting atherosclerosis and increasing blood viscosity. Several TG-related indices, such as the triglyceride-glucose index, play a significant role in the occurrence of coronary artery disease, heart failure, and other cardiovascular events [19, 20]. Nevertheless, there were few studies comparing the predictive value of AIP, TG, and HDL-C specifically for IHF post-percutaneous coronary intervention (PCI). We explored how AIP relates to the prognosis of IHF, and further compared the predictive efficacy of AIP with its components (TG and HDL-C).

Our retrospective study revealed a significant link between AIP and MACE in IHF patients undergoing PCI. As AIP increased, so did the MACE and its components. Kaplan-Meier analysis showed that higher AIP had more frequent adverse events. Even after accounting for potential confounders, higher AIP was consistently and independently related to an augmented risk of these outcomes. RCS analysis confirmed a positive relationship between AIP and MACE. Subgroup analysis revealed a notable interaction effect in the age group. ROC illustrated AIP was the best predictive validity for MACE, followed by TG and HDL-C, respectively.

Dyslipidemia is integral to the development and progression of CVD. Impaired TG metabolism can result in the formation of triglyceride-rich lipoproteins (TRL), which contribute to atherogenesis through various mechanisms [22]. Previous studies have found a causal relationship between elevated TRL and inflammation [23]. This may be because TRLs and their remnants elevate plasma levels of cellular adhesion molecules, which in turn promote the binding of leukocytes and monocytes to atherosclerotic plaques, thereby initiating an inflammatory cascade [24, 25]. Moreover, the buildup of TRLs in plasma contributes to hyper-viscosity by inhibiting fibrinolysis and intensifies the coagulation cascade through increased platelet aggregation, thrombosis, and elevated expression of plasminogen activator inhibitor-1, creating a pro-coagulant environment [25, 26]. Conversely, HDL facilitates the removal of cholesterol out of plaque macrophage foam cells and transports to liver for metabolism and excretion into bile [27, 28]. HDL also exerts anti-inflammatory and antioxidant effects, regulates vascular endothelial homeostasis, and resists thrombosis by reducing platelet aggregation and adhesion reactions [28, 29, 30].

In view of the involvement of TG and HDL-C in atherosclerosis, numerous studies have established a link between AIP and the incidence of cardiovascular disease. A study by Mahdavi-Roshan M et al. [31] found that AIP could effectively predict the risk of CAD. Furthermore, higher AIP levels have been observed in HF patients compared to the general population, regardless of the underlying etiology [32]. In contrast, a study from the National Health and Nutrition Examination Survey (NHANES) database found the opposite result. A recent study identified an inverse relationship between AIP and the incidence of HF in a population of 5598 individuals [33]. Therefore, the specific role of AIP in the prognosis in IHF populations requires further exploration. Previous studies have highlighted the predictive value of TG and HDL-C in determining the prognosis of IHF [17, 18], but there was still an absence of available data on the predictive validity of AIP, TG, and HDL-C. We hypothesized that AIP may be a better prognostic predictor for IHF, since it integrates HDL-C and TG in this specific population.

Our study showed that higher AIP was followed by a higher MACE risk. This study also used RCS curves to characterize the relationship between AIP and prognosis in IHF, which was rarely used in other studies of AIP. The RCS curves indicated that the risk of MACE tended to increase as AIP increased, and the slopes of the curves were steepest when AIP was approximately 0.4. Therefore, an AIP value of 0.4 might be a critical threshold that needs to be emphasized, as MACE may increase substantially beyond this point. In the subgroup analyses, no subgroups were identified that would influence the effect of AIP on MACE, except for the age subgroup, which indicated that AIP was a stronger predictor of MACE at ages younger than 65 years. This may be due to the fact that fewer comorbidities and milder conditions in the younger age group reduced the influence of confounding factors on outcomes, thus amplifying the predictive effect of AIP [34]. Therefore, for people younger than 65 years, we should pay more attention to AIP to identify high-risk patients in the clinic, which may result in a higher clinical benefit. Using ROC curves and Delong analysis, we validated the predictive efficacy of AIP in IHF patients, and found that it was superior to TG and HDL-C, two traditional cardiovascular predictors. Therefore, AIP may be useful as a more accurate indicator to be included in prediction models to identify high-risk patients in subsequent studies. In addition, we would also recommend the widespread clinical use of this metric, especially when TG and HDL-C had opposite predictive outcomes in an individual patient.

In previous studies, IHF the population has been characterized by more comorbidities, and a poorer prognosis. After PCI, the prognosis of this population has greater variability. Our study verified a significant positive relationship of AIP and MACE, as confirmed through various analytical models after accounting for potential confounders. We further evaluated the predictive validity (the traditional indices TG and HDL-C versus AIP), and showed that AIP had better predictive validity than TG and HDL-C. Therefore, without adding any additional economic cost in clinical practice, a simple mathematical algorithm can be used to obtain a better prognostic predictor for patients with IHF undergoing PCI. Since South and East Asian countries have the highest proportion of cardiovascular deaths, the use of AIP may be able to improve the cardiovascular mortality rate in economically disadvantaged areas without increasing the economic burden, and even further reduce some of the unnecessary healthcare costs.

(1) The study was retrospective, and patients’ prior treatment may have affected the index measurements. (2) It was a single-center study from the Beijing Anzhen Hospital, one of the largest cardiovascular centers in China, which enrolled patients with predominantly cardiovascular diseases, which may have biased the results and affected the generalizability of the conclusions. (3) We only included fasting laboratory results obtained on the morning following admission and did not conduct dynamic measurements of TG and HDL-C throughout patients’ hospital stay. (4) The study population consisted almost exclusively of Chinese individuals, and it remains unknown whether the findings will vary according to ethnicity. (5) There were limitations in the follow-up methodology of this study that made it difficult to perform dynamic tracking of AIP during the follow-up period.

Prospective studies will be needed to exclude some treatment-induced bias affecting TG and HDL-C. Additionally, we will conduct studies in multiple centers in multiple countries, and a uniform assay will be developed to exclude measurement bias caused by multiple centers while obtaining universal conclusions. In addition, trends in metrics during the patient’s hospitalization will be included to improve the accuracy of the study. Additional research is needed to determine the applicability of the conclusions of this study to the general public. In addition, further improvements will be made in follow-up methods to dynamically assess changes in indicators and outcomes.

In IHF patients undergoing PCI, higher AIP were linked to an increased risk of MACE, establishing AIP as a moderately predictive prognostic indicator.Additionally, AIP demonstrated superior predictive validity compared to conventional TG and HDL-C.

The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.

YXX, BYZ made equal contributions to this work. YXX, BYZ, and YJZ contributed to the study’s design, data collection, analysis, and manuscript preparation. MSM and YK offered guidance in study design and intellectual input. TNS and XH assisted with data collection and statistical analysis. All authors contributed to the final review and approval of the manuscript, ensuring its accuracy and completeness before submission. All the authors above have reviewed this study critically for important Intellectual content. All authors have participated sufficiently in the work and agreed to be accountable for all aspects of the work.

Written or verbal informed consent was obtained from all participants. Where feasible, written informed consent was collected from a small subset of patients. However, due to the retrospective nature of this study, obtaining written consent from all patients was not possible. For the majority, verbal informed consent was secured. The study was carried out in accordance with the guidelines of the Declaration of Helsinki and approved by the Ethics Committee of Beijing Anzhen Hospital, Capital Medical University (Approval No. 2022235X). The verbal informed consent was approved by the committee. During follow-up, patients were verbally informed that their clinical data could be used for research and potential publication, and their consent was explicitly obtained. They were assured that all data would be anonymized, ensuring that no personally identifiable information would be disclosed.

This study received funding from the National Key Research and Development Program of China (2017YFC0908800), the Pilot Projects for Public Welfare Development of Beijing Municipal Medical Institute “Precision Medicine and Interventional Diagnosis and Treatment Platform for Coronary Heart Disease” (2019-3), Capital’s Funds for Health Improvement and Research (CFH 2020-2-2063), and the Beijing Municipal Natural Science Foundation (7202041).

The authors declare no conflict of interest. Prof. Yujie Zhou is serving as one of the Editor-in-Chief. We declare that Prof. Yujie Zhou had no involvement in the peer review of this article and has no access to information regarding its peer review. Full responsibility for the editorial process for this article was delegated to Prof. Stefano De Servi.