Acute coronary syndrome (ACS) is a major global public health issue with a high mortality rate. Unstable angina (UA), a critical component of ACS, represents a severe phenotype of coronary artery disease (CAD) [1]. UA presents as chest pain caused by an imbalance between myocardial oxygen supply and demand. Moreover, UA is a transitional state between stable angina and acute myocardial infarction. Notably, the clinical prognosis of patients with UA has significantly improved due to the continuous advancement of interventional techniques such as percutaneous coronary intervention (PCI) [2]. However, some patients still face the challenge of residual coronary ischemia after PCI, which further increases the risk of major adverse cardiovascular events [3, 4]. The index of microcirculatory resistance (IMR) is widely recognized in clinical practice as the “gold standard” for assessing coronary microcirculatory dysfunction due to its accuracy and reproducibility [5]. However, the measurement method for IMR is complex, requiring pressure wires, vasodilators, and repeated saline injections, extending the measurement time and increasing procedural risks. In recent years, angiographic microcirculatory resistance (AMR) has gained increasing attention as a potential alternative to IMR. Indeed, studies have shown that an AMR $>$2.5 can define coronary microcirculatory dysfunction and is closely associated with adverse cardiovascular events [6, 7, 8, 9]. AMR has been proven to quantify residual coronary ischemia as a parameter based on the quantitative flow fraction. Studies have also noted that an AMR $>$2.5 is associated with the occurrence of major adverse cardiovascular events post-procedure [6, 7]. Therefore, identifying and evaluating the metabolic factors that influence AMR after PCI is of significant clinical value for improving patient prognosis and reducing the risk of cardiovascular events.

With changes in modern lifestyle, the prevalence of metabolic disorders continues to rise. Metabolic diseases, particularly glucose and lipid metabolism disorders, have become global health challenges [10, 11]. Abnormalities in glucose and lipid metabolism not only increase the risk of cardiovascular diseases, such as coronary artery disease and heart failure but also contribute to dysfunction in various metabolic organs, constituting a complex systemic disorder [12, 13, 14]. Clinical biomarkers such as fasting blood glucose (FBG), glycated hemoglobin (HbA1c), and lipid profiles play crucial roles in diagnosing and screening glucose and lipid metabolism disorders. Further, these biomarkers provide important information for personalized treatment and drug development [15]. Although previous studies have shown an association between HbA1c, the triglyceride glucose (TyG) index, and coronary microcirculatory disorders [16, 17], these studies mostly rely on single metabolic indicators and lack a comprehensive evaluation of the overall correlation between glucose and lipid metabolism abnormalities and coronary microcirculatory disorders.

Given these limitations, this study aimed to analyze the association between glucose and lipid metabolism markers and coronary microcirculatory dysfunction in patients with UA after PCI. The goal was to identify high-risk patients following PCI and develop long-term management strategies to reduce the likelihood of future adverse cardiovascular events.

UA, a disease that significantly affects patients’ quality of life, has long been a focus of cardiovascular treatment and management. Despite significant advancements in PCI technology improving patient outcomes, residual coronary ischemia remains a critical factor leading to adverse cardiovascular events [4, 21, 22]. Yongzhen Fan et al. [23] validated the effectiveness of AMR in diagnosing coronary microcirculatory dysfunction, highlighting an AMR $>$2.5 as a critical threshold for this disorder. Previous studies have demonstrated that AMR effectively assesses coronary artery physiological function and correlates closely with an increased risk of future adverse cardiovascular events [6, 7, 8, 9]. Therefore, identifying and evaluating metabolic susceptibility factors affecting post-PCI AMR and determining high-risk patients after PCI can help develop personalized long-term management strategies to reduce the likelihood of adverse cardiovascular events in the future.

The root cause of glycemic and lipid metabolism disorders stems from the imbalance between pro-oxidant and antioxidant levels, leading to endothelial dysfunction promoting cardiovascular disease development [10]. Biological markers, as indicators for disease diagnosis and staging, allow the detection of changes in patient biomarkers and can develop personalized treatments [15]. Moreover, FBG, HbA1c, and lipid profiles are biological markers of glycemic and lipid metabolism and are significantly associated with the risks of cardiovascular events [13, 14, 22, 24]. HbA1c is the gold standard for long-term blood glucose control, reflecting the glycemic status over the past 2–3 months. Prolonged hyperglycemia results in advanced glycation end products (AGEs) that inhibit endothelial nitric oxide synthase (eNOS) and induce vascular and myocardial collagen cross-linking, thereby impairing vascular dilation [25]. The study have confirmed a significant correlation between HbA1c levels and residual coronary ischemia post-PCI [17]. Furthermore, research indicates that optimal control of LDL-C in patients undergoing PCI can significantly improve coronary microvascular function after one year due to a reduction in LDL-C, which improves endothelial cell function [26, 27]. Most studies analyze the correlation between a single metabolic abnormality and cardiovascular events, meaning the close relationship between glycemic and lipid metabolism abnormalities is often overlooked in single-angle analyses [28, 29]. Therefore, analyzing the independent risk factors for residual coronary ischemia post-PCI in patients with UA from a comprehensive glycemic and lipid metabolism perspective and conducting subgroup analysis can provide more precise clinical guidance. This comprehensive analytical approach helps to improve understanding of the overall impact of glycemic and lipid metabolism abnormalities on cardiovascular events, enabling the development of more effective personalized treatment strategies.

This study thoroughly explored the relationship between glucose and lipid metabolism disorders and microcirculatory dysfunction in patients with UA after PCI and evaluated the predictive value of relevant metabolic indicators. The results revealed that HbA1c, TG, LDL-C, and Lp(a) levels can be independent risk factors for post-PCI microcirculatory dysfunction. Subgroup analyses based on sex further demonstrated that these risk factors, identified in the overall sample, remained significant across both male and female groups. This indicates the robustness of these indicators in predicting post-PCI AMR $>$2.5, confirming their general applicability regardless of sex, with no significant interaction effects. To further evaluate the predictive value of these risk factors, the total sample was randomly divided into a training set and a validation set at a 7:3 ratio. A nomogram including HbA1c, TG, LDL-C, and Lp(a) was constructed based on the training set. The predictive performance of the nomogram was assessed using ROC curves, presenting an AUC of 0.824 in the training set, indicating high accuracy. The model was then tested in the validation set, with an AUC of 0.746, again demonstrating high accuracy. Therefore, calibration curves in both the training and validation sets confirmed the consistency of the model. Furthermore, these indicators are both accessible and affordable, allowing for risk assessment in patients with UA post-PCI and the development of individualized treatment plans and management strategies to reduce the likelihood of future cardiovascular adverse events.

The findings of this study have several important implications for clinical practice. First, they emphasize the importance of monitoring and controlling metabolic risk factors in managing patients with UA post-PCI. Second, the predictive model based on the nomogram helps in the early identification of high-risk patients and facilitates timely interventions.

HbA1c, TG, LDL-C, and Lp(a) are independent risk factors for microcirculatory dysfunction after PCI in patients with UA. A predictive model incorporating these factors was established, demonstrating good predictive value and clinical utility, which can provide clinicians with valuable information to develop treatment strategies to minimize future major adverse cardiovascular events.

UA, unstable angina pectoris; PCI, percutaneous coronary intervention; QFR, quantitative flow ratio; IMR, index of microcirculatory resistance; AMR, angiographic microvascular resistance; CMVD, coronary microvascular disease; DM, diabetes mellitus; TG, triglyceride; HDLC, high-density lipoprotein cholesterol; LDLC, low-density lipoprotein cholesterol; VLDL, very low-density lipoprotein; FBG, fasting blood glucose; HbA1c, glycated hemoglobin.

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

KML, CMQ and JW—Conception and design; KML and CMQ—administrative support; KML and SL—provision of study materials; JW, SL and ZL—collection and assembly of data; KML and CMQ—data analysis and interpretation. All the authors have written and approved the final manuscript. All authors read and approved the final manuscript. All authors have participated sufficiently in the work and agreed to be accountable for all aspects of the work.

This study adhered to the principles outlined in the Helsinki Declaration and was approved by the Ethics Committee of the Second Affiliated Hospital of Xuzhou Medical University (2020120205). Written informed consent was obtained from all participants.

We would like to express our gratitude to all those who helped us in the writing of this manuscript.

This research received no external funding.

The authors declare no conflict of interest.