Abstract Background Aortic stenosis (AS) is a common cardiovascular condition marked by progressive narrowing of the aortic valve and is associated with substantial morbidity and mortality. Although its clinical impact is well recognized, the molecular mechanisms driving AS progression remain incompletely defined, particularly with regard to the contributions of non-coding RNAs and immune–metabolic interactions. Methods High-throughput RNA sequencing combined with integrative bioinformatics analyses was performed on human aortic valve tissues from patients with AS to identify differentially expressed RNAs. Expression of Nicotinamide Adenine Dinucleotide Kinase 2 (NADK2) was validated using western blotting and quantitative polymerase chain reaction in both human AS specimens and murine AS models. Functional enrichment analyses, immune cell infiltration profiling, and weighted gene co-expression network analysis were applied to characterize regulatory networks and identify hub genes. Results A total of 1,443 messenger RNAs (mRNAs), 3,147 long non-coding RNAs (lncRNAs), and 145 circular RNAs (circRNAs) were differentially expressed in AS valve tissues, with the majority showing increased expression. Principal component analysis demonstrated clear separation between AS and control samples. Functional enrichment analyses linked differentially expressed mRNAs primarily to sensory perception and calcium signaling pathways, lncRNAs to embryonic development–related processes, and circRNAs to protein regulatory functions. NADK2 was identified as a hub gene and was significantly upregulated at both the mRNA and protein levels in valve tissues from patients with AS and in the murine AS model. Immune infiltration analysis indicated increased proportions of CD4⁺ memory T cells and CD8⁺ T cells in AS, with strong positive correlations observed between these immune cell populations and NADK2 expression. Weighted gene co-expression network analysis further supported NADK2 as a key candidate regulator within the disease-associated magenta module. Conclusion This study delineates comprehensive RNA regulatory networks in patients with AS and identifies NADK2 as a key molecular contributor to disease pathogenesis. Its consistent upregulation across species, together with close associations with immune dysregulation and co-expression network modules, supports its potential relevance as a therapeutic target in AS.

To address the performance degradation of Gaussian Naive Bayes (GNB) classifier on imbalanced datasets caused by sparse minority class features and severe class overlap, this paper proposes a new feature construction algorithm based on dynamic dual-scale nearest neighbor statistical ratio (NNDSR). The core of NNDSR is a dynamic dual-scale nearest neighbor mechanism, which is designed to accurately extract the local aggregation characteristics of samples and the inter-class boundary information. On this basis, new features are generated through cross-class and dual-scale statistical ratio operations. These features possess both strong discriminability and Gaussian distribution adaptability, which can significantly amplify class differences and effectively approximate the core assumptions of GNB. By optimizing the information expression of minority classes and enhancing class separability with these features, the algorithm avoids the information distortion problem of traditional sampling techniques and solves the mismatch between general feature enhancement algorithms and GNB's core assumptions. Comparative experiments were conducted on 22 UCI datasets with varying scales, dimensions and imbalance ratios. Results show that NNDSR significantly outperforms the original data and 16 mainstream algorithms including sampling, feature enhancement and classifier-level optimization methods in core classification metrics such as AUC, G-mean and F-measure, with a notable improvement in the recognition accuracy of minority classes. Scalability tests further confirm its efficiency and stability on datasets with ten-thousand-level samples and within one hundred dimensions. This paper provides a robust new feature construction algorithm for GNB to handle imbalanced data, with strong practical application value.

Drinking hot or very hot beverages is a risk factor for GC in the UK where drinking hot tea and coffee is common.

Acute aortic dissection (AAD) is a life-threatening emergency without established effective monitoring biomarkers. This study aimed to explore biomarkers to optimize the diagnosis of AAD. AAD related genes were screened by spatial transcriptomics experiments, and their encoded proteins were validated in aortic tissues. We measured plasma levels of candidate proteins in 302 participants (173 AAD cases, 129 controls), finding higher PTMA, ADAMTS8, and CD36 in AAD. Case-control analysis revealed that elevated levels of those proteins along with D-dimer, increased systolic blood pressure (SBP), height and smoking history were risk factors for AAD. A multi-marker score comprising D-dimer, ADAMTS8, height, SBP, and age was developed for AAD diagnosis, achieving an AUC of 0.921 (95%CI 0.889-0.952), with 77.5% sensitivity and 96.5% specificity. We further validated the diagnostic performance of the multi-marker score in an independent validation set including healthy controls and patients with chest pain. Our findings indicate that PTMA, ADAMTS8, and CD36 are potential biomarkers associated with AAD. The multi-marker score effectively discriminates AAD from both healthy controls and non-AAD acute chest pain conditions, and may serve as a rapid, cost-effective auxiliary diagnostic tool.

There is ongoing discussion in the medical community on the effect of metformin in cardiac arrhythmias. Investigating the causal relationship between metformin and arrhythmias was the target of the research. We utilized the Mendelian randomization (MR)-based platform to collect genome-wide association data linking arrhythmias and metformin. We utilized SNPs connected to metformin as instrumental variables (IVs) to evaluate the causative link between metformin and arrhythmia through two-sample MR analysis. Four statistical techniques were employed: MR-Egger regression, weighted median estimator, weighted mode method, and inverse-variance weighted (IVW) method. The impact of individual SNPs on the outcomes of IVW analyses was investigated using the leave-one-out method, and the study's possible bias was examined using a funnel plot to guarantee the results' robustness. We identified 40 independent single nucleotide polymorphisms (SNPs) of genome-wide significance from GWAS data for metformin as IVs. The IVW method supported a causal relationship between metformin and reduced incidence of arrhythmia (OR = 0.0093; 95% CI = 0.002-0.055; P = 2.72E-07). MR-Egger regression indicated that there is no need to consider the effect of gene pleiotropy on the results of the study (intercept = 0.0014; P = .874). Although the MR-Egger method didn't pinpoint a causal link, it did reveal a similar directionality in the β values (OR = 0.0056; 95% CI = 9.384E-06 to 3.369; P = .1207). Fortunately, the weighted median and weighted mode techniques demonstrated a causal link between metformin and a decreased arrhythmia risk (OR = 0.004; 95% CI = 0.0003-0.052, P = .000023; OR = 0.0025; 95% CI = 7.418E-05 to 0.085, P = .001915). Neither Cochran Q test nor the funnel plot showed signs of directional pleiotropy, heterogeneity, or asymmetry. The sensitivity of the method was analyzed by leave-one-out method, and the results suggested that the method was stable. With the MR method, we've substantiated a potential cause-and-effect link between metformin usage and cardiac arrhythmia.

China has a large population, and the prevalence of dyslipidemia varies across regions. The prevalence of dyslipidemia is closely associated with the increasing burden of cardiovascular disease. Currently, there is a lack of high-quality data on early-onset dyslipidemia in Northwest China. This study aims to comprehensively assess the prevalence and risk factors of early-onset dyslipidemia among adults in this region, providing epidemiological evidence for disease prevention and health promotion. This is a cross-sectional study based on the universal health check-up program conducted in northwestern China from January to December 2019. Adults (aged 18 to 65 years old) living in both rural and urban areas were included. Participants received a questionnaire survey, physical examination, and laboratory tests, including liver and kidney function, complete blood count, fasting blood glucose, and lipid profile. This study included a total of 3,559,141 participants (43.2

Abstract Background Non-cardiac chest pain (NCCP) is commonly regarded as a low-risk condition. However, long-term mortality, cause-specific death, and high-risk subgroup characteristics remain poorly defined. Methods In this multicentre registry-linked cohort study, we linked the Chest Pain Center Registry from 101 hospitals in Hunan, China, with the Mortality and Cause of Death Registry. Adults diagnosed with NCCP from Jan 1, 2017, to Dec 31, 2021, were included. We assessed 3-year all-cause, cardiovascular, and non-cardiovascular mortality using Cox, restricted cubic spline, and Fine-Gray models. Findings Among 160,245 patients, 4674 deaths occurred within 3 years (2.9%). Mortality increased sharply after 60.5 years. Age ≥ 60.5 years (adjusted hazard ratio [aHR] 7.49 [95% CI 6.89-8.14]), rural residence (time-varying aHR 1.46 [1.35-1.57] in year 1 and 1.66 [1.46-1.89] in years 1-3), and male sex (aHR 1.47 [1.38-1.57]) independently predicted death. Three-year mortality ranged from 0.3% in younger urban women to 8.4% in older rural men. Cardiovascular diseases accounted for 56.4% of deaths among older patients, whereas other non-cardiovascular causes (22.8%) and malignancy (20.8%) were the largest categories among younger decedents. Interpretation NCCP is not uniformly benign. Age, rural residence, and sex identify patients who could benefit from risk-stratified follow-up, with cardiovascular prevention prioritised for older rural men and broader non-cardiovascular assessment considered for younger patients. Research in context Evidence before this study We searched PubMed from database inception to June 4, 2026, without date restrictions or language filters, using combinations of “non-cardiac chest pain”, “noncardiac chest pain”, “non-specific chest pain”, “nonspecific chest pain”, and “undiagnosed chest pain” with “prognosis”, “mortality”, “long-term”, “cause-specific”, and “cardiovascular mortality”. We considered systematic reviews and cohort studies reporting prognosis, mortality, cardiovascular outcomes, recurrent health-care use, or cause-specific outcomes after NCCP, non-specific chest pain, or undiagnosed chest pain. Previous evidence, including cohort studies with several years of follow-up, suggests that patients with NCCP or related chest pain diagnoses are not a homogeneous low-risk group. However, definitions, clinical settings, and outcomes have varied substantially, and cause-specific mortality has not been consistently reported. In our search, we did not identify large multicentre registry-linked studies examining long-term cause-specific mortality after NCCP or how age, sex, and urban-rural context shape risk after chest pain centre discharge. Added value of this study This multicentre registry-linked cohort included 160,245 patients with NCCP from 101 chest pain centres in Hunan, China, linked to a mortality and cause-of-death registry. The study provides 3-year estimates of all-cause, cardiovascular, and non-cardiovascular mortality, characterises cause-of-death composition, and uses competing-risk methods to separate cardiovascular from non-cardiovascular death. It also shows that simple variables available at presentation, age, sex, and residence, identify a steep mortality gradient, with older rural men having the highest absolute risk. Implications of all the available evidence The available evidence and our findings support reframing NCCP as a potential entry point for risk-stratified follow-up rather than as reassurance alone. Age, sex, and residence could help chest pain services and primary care systems identify patients who need closer post-discharge surveillance. The urban-rural gradient also points to continuity of care and access to cardiovascular prevention as public health priorities after chest pain evaluation. Follow-up strategies should be aligned with competing risks: cardiovascular prevention may be most relevant for older patients, whereas broader assessment of non-cardiovascular conditions may be appropriate for younger patients. Future studies should test whether targeted follow-up pathways can improve long-term outcomes after NCCP.

BACKGROUND:Sleep health is multidimensional, yet existing research has largely examined isolated sleep characteristics, specific population groups, or only one direction of the sleep-depressive symptom relationship. As a result, the bidirectional associations between multidimensional sleep patterns and depressive symptoms remain insufficiently understood. METHODS:The study recruited 75,445 adults between July 2021 and December 2024 through the smart health management digital platform for primary cancer prevention (SmartHMDP-PCP) with 34,344 participants completed the follow-up survey. Sleep was assessed across eight dimensions-sleep midpoint, duration, pre-sleep activities, insomnia, sleep problems, daytime symptoms, medication use, and subjective quality-to construct a composite sleep score (healthy vs. unhealthy). Depressive symptoms were measured using the Self-Rating Depression Scale. Multivariate logistic regression estimated odds ratios (ORs) and 95% confidence intervals (CIs). RESULTS:Healthy sleep was associated with reduced odds of incident depressive symptoms (OR, 95%CI: 0.62, 0.58-0.67) and increased likelihood of symptom remission (1.55, 1.41-1.69). Maintaining healthy sleep conferred the strongest protection against incident depressive symptoms (0.38, 0.35-0.42) and the greatest likelihood of remission (2.72, 2.42-3.06). In bidirectional analyses, absence of baseline depressive symptoms (0.60, 0.56-0.66), particularly when sustained over time (0.41, 0.37-0.46), was also negatively associated with subsequent deteriorations in sleep patterns, with effect sizes numerically comparable to those observed in the reverse direction. CONCLUSIONS:Multidimensional sleep and depressive symptoms exhibit bidirectional associations of numerically similar magnitude. These findings underscore the importance of integrating sleep health promotion into mental health strategies and highlight the need for interventional and long-term longitudinal research to clarify causal pathways.

Background Inflammatory infiltration constitutes a fundamental pathophysiological mechanism in myocardial ischemia-reperfusion (IR) injury, characterized by its role in initiating tissue damage, amplifying pathological inflammatory cascades, and exacerbating structural and functional impairment of the myocardium. Integrated transcriptomic and metabolomic analysis identified the metabolite itaconic acid as a potential key regulator in IR injury. Therefore, we assessed the effect of its derivative, 4-octyl itaconate (4-OI), on inflammatory infiltration in a mouse model of IR injury. Methods Following establishment of a myocardial IR model, tissue samples from the infarct border zone were harvested for transcriptomics and wide-target metabolomic sequencing. Bioinformatics methods were used to analyze the transcriptomics and metabolomics results separately and to perform a joint analysis. Molecular docking and molecular dynamics modeling were employed to explore proteins bound to itaconic acid. Following intragastric administration of 4-OI to myocardial IR mice, echocardiography was performed. Plasma levels of interleukin-4 (IL4), interleukin10 (IL10), cardiac troponin T (cTnT), and creatine kinase-myocardial band (CKMB) were measured by enzyme-linked immunosorbent assay (ELISA). Myocardial inflammatory infiltration was evaluated by hematoxylin and eosin Staining (HE staining), while inflammatory marker expression associated with macrophages was evaluated by immunohistochemical staining for inducible nitric oxide synthase (iNOS) and immune responsive gene 1 (IRG1). Macrophage heterogeneity was further assessed by immunofluorescence co-localization staining for F4/80, CCR2, and CD206. Additionally, mRNA expression levels of Interleukin-1 beta (Il1b), Tumor Necrosis Factor-alpha (Tnfa), Il4, and Il10 in myocardial tissue were quantified by quantitative reverse transcription polymerase chain reaction (qRT-PCR). Results Integrated transcriptomic and metabolomic analysis identified itaconic acid as a potential metabolite modulating myocardial IR injury. Preliminary molecular docking analysis suggested possible in silico interactions between itaconic acid and Pla2g2d, Lcn2, and Gpr55 proteins, which require further experimental validation. A derivative of itaconic acid, 4-OI, significantly ameliorated myocardial IR injury in mice, reduced inflammatory infiltration, decreased iNOS-associated staining, and modestly increased Arg1-associated staining in the infarct border zone. This treatment was also accompanied by differences in macrophage marker-defined populations, reflected by fewer F4/80+CCR2+ and more F4/80+CD206+ cells in the injured myocardium.

The Oxidative Balance Score (OBS) is a composite measure of systemic oxidative stress. This study aims to evaluate the impact of OBS on all-cause mortality in patients with cardiovascular disease–cancer comorbidity and to use machine learning to identify related factors. We analyzed data from the 2007–2018 US National Health and Nutrition Examination Survey (NHANES). Cox regression, Kaplan-Meier analysis, restricted cubic splines (RCS), and subgroup analysis were used to explore the association between OBS and CVD-cancer comorbidity. Five machine learning models were constructed and compared to identify the optimal CVD-cancer comorbidity risk prediction model, and feature importance was assessed. Among the study participants, compared to participants in the lowest tertile of the OBS score, those in the highest tertile exhibited a lower risk of all-cause mortality (HR = 0.78, 95

Background:Early detection of esophageal squamous cell carcinoma (ESCC) may improve survival, but universal screening is not feasible. This study aimed to develop and validate a model for identifying individuals at high absolute risk of ESCC in a high-risk Chinese population. Methods:The model was developed by using data from a case-control study in Yanting County, Sichuan, China between 2011 and 2013, including 942 ESCC cases and 942 age- and sex-matched control participants. Conditional logistic regression and the Gail algorithm were used to construct the model. Model performance was assessed by using the area under the receiver-operating characteristic curve (AUC) with 10-fold cross-validation. External validation was performed in two independent populations, i.e. another Chinese case-control study and the UK Biobank cohort. Results:The model included six risk factors: education level, marital status, tobacco smoking, alcohol consumption, body mass index (overweight status), and family history of cancer. The model incorporated age- and sex-specific incidence rates in the population to estimate the 5-year absolute risk. The AUC was 0.72 (95% confidence interval [CI] 0.69-0.74) in the derivation dataset and 0.68 (95% CI, 0.67-0.69) after cross-validation. In external validation, the AUC was 0.65 (95% CI, 0.61-0.69) in the independent Chinese case-control study and 0.66 (95% CI, 0.56-0.75) in UK Biobank. The estimated 5-year absolute risk of ESCC in the population in Yanting ranged from 0.0005% to 18.2%. In the group at the highest predicted risk, six individuals would need to undergo screening to detect one ESCC case within 5 years. Conclusion:The developed model demonstrated acceptable performance and has the potential to identify high-risk individuals for targeted prevention and early detection in the studied high-risk population.

Background Malnutrition, frailty, and sarcopenia are common and interrelated in patients with cancer, yet their associations with nutrition impact symptom clusters remain unclear. This study examined the overlap of these conditions, and their relationships with symptom clusters in adults with cancer.Methods Malnutrition was assessed using the Patient-Generated Subjective Global Assessment and Global Leadership Initiative on Malnutrition criteria, frailty using the FRAIL scale, and sarcopenia using the 2019 Asian Working Group for Sarcopenia consensus. Latent class analysis identified nutrition impact symptom clusters. Logistic regression models evaluated associations between symptom clusters and the three conditions.Results This cross-sectional latent class analysis included 28,377 hospitalized adults with cancer (median age 58 years; 55.3% male). Gastrointestinal (41.0%) and lung (23.1%) cancers were the most common diagnoses. The prevalence of malnutrition, frailty and sarcopenia was 56.4%, 37.7%, and 16.9%, respectively. Weight loss and low muscle mass represent key shared features of their coexistence. Five nutrition impact symptom clusters were identified and ranked by severity. Patients with severe multi-symptom cluster had 3.87-fold higher odds of malnutrition and 6.03-fold higher odds of frailty. Patients with the gastrointestinal-dominant symptom cluster were most likely to experience malnutrition, whereas those with the sensory-alteration symptom cluster were most likely to exhibit frailty.Conclusion Maintaining stable body weight, particularly muscle mass, is crucial for cancer patients to reduce the risk of comorbidity among these conditions. Identifying nutrition impact symptom clusters and providing targeted interventions may help reduce the burden of these conditions and improve the efficiency of nutritional care.

Stanford type A aortic dissection (AAD) is a life-threatening cardiovascular disease characterized by tearing in the aortic wall. Using spatial transcriptomics and multiplex immunofluorescence, we comprehensively analyzed ascending aortas from eight AAD patients across different severities and segments. We demonstrate that SPP1-driven inflammatory signaling intensifies with AAD severity, identifying a nine-gene, layer-anchored severity scale: MYL6/CALD1/MYH9 (mild); CCL2/CP/COL4A1 (moderate); and TMSB4X/ATP5F1E/PKM (severe). Importantly, the collagen-remodeling triad COL1A1/COL3A1/MMP2 is concurrently up-regulated in the brachiocephalic, left subclavian, and left common carotid arteries, often before the ascending aorta meets surgical diameter thresholds. These molecular signatures provide a critical foundation for non-invasive biomarker discovery, risk stratification, and precision pharmacotherapy targeting the SPP1-inflammatory axis, ultimately offering new insights into AAD mechanisms and therapeutic targets.

To investigate the protective effect and mechanism of enhanced expression of endogenous macrophage migration inhibitory factor (MIF) on cardiac ischemia–reperfusion (I/R) injury. A recombinant double-stranded adeno-associated virus serotype 9 with MIF or green fluorescent protein (GFP) genes (dsAAV9-MIF/GFP) was transduced into mice and neonatal rat ventricular myocytes (NRVMs). The models of cardiac 60 min ischemia and 24 h reperfusion and 12 h hypoxia/12 h reoxygenation (H/R) were established in mice and NRVMs, respectively. Infarct size, cardiac remodeling, and related signaling pathways were assessed. The dsAAV9 vector demonstrated strong transduction efficacy and cardiac affinity. Cardiac overexpression of MIF led to a 35.3

Background Diabetes mellitus (DM) and coronary artery disease (CAD) are closely interrelated clinical conditions. However, the combination analysis based on DM related CAD diagnostic model remains a gap. The primary objective of this study was to identify diagnostic models and diagnostic markers for CAD based on the association of diabetic phenotypes and attempt to explore them further in a mouse model. Methods We used data integration as well as multiple datasets for both coronary artery disease and diabetes to exclude bias as well as to improve reliability. We employed the least absolute shrinkage and selection operator (LASSO) regression algorithms to construct the CAD diagnostic model. Furthermore, we established mouse CAD model (low-density lipoprotein receptor deficient mice with high fat diet) to explore the crosstalk between the screened biomarkers and severe CAD progress. Results The intersecting genes from differential analysis and weighted correlation network analysis (WGCNA) results yielded 32 diabetes-related biomarkers. We then identified two diabetes-related phenotypes through the consensus clustering in CAD patients. Microenvironmental analysis revealed that phenotype 1 exhibited higher expression of most cytokines, inflammatory factors, interleukins, and related receptors. Immune cell composition in phenotype 1 showed increased infiltration compared to phenotype 2. The LASSO regression identified 16 diabetes-related genes and we further constructed a diagnostic model based on these genes, which the area under the curve (AUC) reached 0.8. Additionally, single cell immune analysis exhibited the location of these genes. KCNQ1, ATP6V1B1, MTDH, and ITPK1 were predominantly located in macrophages, indicating their potential in regulating macrophage during myocardial injury. Furthermore, We elucidated that KCNQ1 and ITPK1 exhibited high expression level in mouse CAD model in tissue level. exhibited similar expression trends with macrophage biomarkers (CD31 and CD68). The result of qPCR also indicated the elevated level of KCNQ1 and ITPK1, which exhibited crosstalk with CD31 and CD68 in mouse CAD model. Conclusion This study delves into the microenvironmental characteristics of diabetes-related phenotypes in CAD, constructing an optimal diagnostic model and validated the significance of diagnostic markers in mouse CAD model, which may offer insights that could be beneficial for clinical management in the near future.

Head and neck squamous cell carcinoma (HNSCC) patients undergoing free flap reconstruction face a high risk of surgical site infection (SSI). Logistic regression (LR) models for SSI prediction are limited by linear assumptions, while machine learning (ML) approaches like random forest (RF) may offer superior performance by handling complex clinical data. This study aimed to identify SSI risk factors and compare the predictive performance of LR and RF models. This retrospective study included 442 HNSCC patients. Two predictive models were constructed based on LR and RF methods, respectively. The predictive performance of two models was assessed based on area under the receiver operator characteristic curves, calibration curve, and decision curve analysis (DCA). SHapley Additive exPlanations (SHAP) was applied in the RF model to analyze the impact of features on prediction results. The RF model outperformed LR, achieving higher accuracy, sensitivity, specificity, and AUC. Calibration curves indicated superior alignment of RF predictions with observed outcomes. DCA revealed higher net benefits for RF across a wide probability threshold range. SHAP analysis identified PNI, operation time, and NLR as top predictors. Novel systemic markers (PNI, NLR) and clinical factors are critical for risk stratification.

AIM:Small dense low-density lipoprotein cholesterol (sdLDL-C) is recognized as an atherogenic risk factor. This study investigated the prognostic significance of sdLDL-C levels in patients with premature acute coronary syndrome (PACS) and multivessel disease (MVD). METHODS:This retrospective study enrolled 847 hospitalized patients diagnosed with PACS and MVD between May 2022 and November 2023. Patients were stratified based on clinical outcomes and tertiles of sdLDL-C levels. Multivariate Cox proportional hazard models were applied to determine whether or not sdLDL-C was a prognostic risk factor for major adverse cardiovascular events (MACEs). Cumulative event curves were estimated using the Kaplan-Meier method. The predictive efficacy of sdLDL-C for MACEs was assessed through a time-dependent receiver operating characteristic (ROC) analysis. In addition, a restricted cubic spline (RCS) analysis was conducted to explore the relationship between sdLDL-C levels and the risk of MACEs. RESULTS:During a median follow-up of 12 months (interquartile range: 9-15 months), 124 MACEs (14.64%) were observed. The sdLDL-C levels in the MACEs group were significantly higher compared to the non-MACEs group (P＜0.001). A multivariate Cox hazards regression analysis revealed that the risk of MACEs in the highest sdLDL-C tertile group was 2.38 times greater than in the lowest tertile group (hazard ratio [HR]: 2.38, 95% confidence interval [CI]: 1.42-4.00; P = 0.001). Furthermore, each 1-mg/dL increase in sdLDL-C levels corresponded to a 12.2% increase in the risk of MACEs (HR: 1.12, 95% CI: 1.08-1.16; P＜0.001). A Kaplan-Meier survival analysis identified significant differences in event-free survival among sdLDL-C tertiles (log-rank test, P＜0.001). The time-dependent ROC analysis demonstrated a progressive increase in the area under the curve during the follow-up period, particularly within the first 12 months. The RCS analysis revealed a nonlinear dose-response relationship between higher sdLDL-C levels and increased cumulative risk of MACEs (Pnonlinear = 0.001). CONCLUSION:sdLDL-C is a predominant predictor of a poor prognosis in patients with PACS and MVD, underscoring its clinical relevance for risk stratification and the early identification of high-risk individuals who may benefit from targeted intervention.