The global burden of cardiovascular diseases continues to rise, making their prevention, diagnosis and treatment increasingly critical. With advancements and breakthroughs in omics technologies such as high-throughput sequencing, multi-omics approaches can offer a closer reflection of the complex physiological and pathological changes in the body from a molecular perspective, providing new microscopic insights into cardiovascular diseases research. However, due to the vast volume and complexity of data, accurately describing, utilising, and translating these biomedical data demands substantial effort. Researchers and clinicians are actively developing artificial intelligence (AI) methods for data-driven knowledge discovery and causal inference using various omics data. These AI approaches, integrated with multi-omics research, have shown promising outcomes in cardiovascular studies. In this review, we outline the methods for integrating machine learning, one of the most successful applications of AI, with omics data and summarise representative AI models developed that leverage various omics data to facilitate the exploration of cardiovascular diseases from underlying mechanisms to clinical practice. Particular emphasis is placed on the effectiveness of using AI to extract potential molecular information to address current knowledge gaps. We discuss the challenges and opportunities of integrating omics with AI into routine diagnostic and therapeutic practices and anticipate the future development of novel AI models for wider application in the field of cardiovascular diseases.

Accurate prediction of 10-year major adverse cardiovascular events (MACE) is critical for effective disease prevention and management. Although the SCORE2 model introduced sex-specific algorithms, opportunities remain to further refine prediction.

To evaluate whether adding sex-specific proteomic profiles to the SCORE2 model enhances 10-year MACE risk prediction in the large UK Biobank (UKB) cohort.

Data from 47,382 UKB participants, aged 40 to 69 years without prior cardiovascular disease or diabetes, were utilized. Proteomic profiling of plasma samples was conducted using the Olink Explore 3072 platform, measuring 2,923 unique proteins, of which 2,085 could be used. Sex-specific Least Absolute Shrinkage and Selection Operator (LASSO) regression was used for biomarker selection. Model performance was assessed by changes in Harrell's C-index (a measure of discrimination), net reclassification index (NRI), and integrated discrimination index (IDI).

During 10-year follow-up, 2,163 participants experienced MACE. Overall, 18 proteins were selected by LASSO regression, with 5 of them identified in both sexes, 7 only in males, and 6 only in females. Incorporating these proteins significantly improved the C-index of the SCORE2 model from 0.713 to 0.778 (P < 0.001) in the total population. The improvement was greater in males (C-index increase from 0.684 to 0.771; Δ = +0.087) than in females (from 0.720 to 0.769; Δ = +0.049). The WAP four-disulfide core domain protein (WFDC2) and the growth/differentiation factor 15 (GDF15) were the proteins contributing the strongest C-index increase in both sexes, even more than the N-terminal prohormone of brain natriuretic peptide (NTproBNP).

The derived sex-specific 10-year MACE risk prediction models, combining 12 protein concentrations among men and 11 protein concentrations among women with the SCORE2 model, significantly improved the discriminative abilities of the SCORE2 model. This study shows the potential of sex-specific proteomic profiles for enhanced cardiovascular risk stratification and personalized prevention strategies.

This review goes beyond traditional approaches in cardio-oncology, highlighting often-neglected factors impacting patient care. Social determinants, environment, health care access, and gut microbiome significantly influence patient outcomes. Powerful tools like multi-omics and wearable technologies offer deeper insights into real-world experiences. The future lies in integrating these advancements with established practices to achieve precision cardio-oncology care. By crafting tailored therapies and continuously updating comprehensive management plans based on real-time data, we can unlock the full potential of personalized care for all patients.

The plasma proteome is the future for diagnostic markers for common diseases, like cardiometabolic disorders (CMDs) and coronary artery disease (CAD). The reliability of these markers requires identifying their source-organ(s). We profiled 974 plasma proteins in 532 CAD-patients of the STARNET study with arterial wall, major metabolic organ, and blood transcriptomic data. 144 plasma cis-pQTLs colocalized with tissue eQTLs including eight CMD/CAD GWAS genes. 262 plasma proteins correlated with their corresponding tissue "seed" genes whereof 101 in the liver. 851 plasma proteins were strongly associated with the activity of gene-regulatory networks (GRNs), particularly cross-tissue GRNs. The Adipose-Liver Plasma LeptIN-regulating GRN78 with the top key driver UCHL1 in fat stood out. Beyond genetics, explaining up to 20% of plasma protein variation, and a limited number of mostly hepatic seed genes, plasma proteins are integral to GRNs acting within and across blood cells, the arterial wall and major metabolic organs.

We investigated the association of the inflammatory biomarker YKL-40 with cardiovascular events (CVEs) and mortality in individuals with type 2 diabetes.

We followed 11,346 individuals recently diagnosed with type 2 diabetes for up to 14 years. Baseline YKL-40 levels (measured in 9,010 individuals) were grouped into percentiles (0-33 %, 34-66 %, 67-90 %, and 91-100 %) and analyzed continuously (per 1 SD log increment), with comparisons to CRP (measured in 9,644 individuals). Cox regression assessed associations with atrial fibrillation (AF), ischemic stroke (IS), venous thromboembolism (VTE), myocardial infarction (MI), heart failure (HF), peripheral artery disease (PAD), and all-cause, cardiovascular, and cancer mortality.

Adjusted HRs (95% CIs) for the highest (91-100%) versus the lowest (0-33%) YKL-40 percentile category were 1.31 (1.04-1.66) for AF, 1.43 (0.98-2.07) for IS, 1.07 (0.65-1.76) VTE, 0.88 (0.52-1.48) for MI, 1.66 (1.19-2.31) for HF, 1.66 (1.12-2.48) for PAD, and 2.18 (1.85-2.56) for all-cause, 1.64 (1.07-2.50) for cardiovascular, and 2.73 (2.05-3.63) for cancer mortality. Each 1 SD log increase in YKL-40 and CRP levels similarly increased CVE risks, with CRP being superior for MI and cardiovascular mortality.

YKL-40 is a prognostic biomarker for most CVEs, and even more so for all-cause mortality, primarily driven by cancer-related causes.

There is accumulating evidence that allergy is a risk factor for keratoconus. Nonetheless the association between allergic disease and keratoconus remains controversial. We performed a two-sample Mendelian randomization (MR) study to determine the putative causal association of 4 allergic diseases (allergic conjunctivitis, allergic asthma, allergic rhinitis and atopic dermatitis) with keratoconus.

Summary statistics were obtained from genome-wide association studies (GWAS) of allergic conjunctivitis (AC) (20,958 cases and 356,319 controls), allergic asthma (AA) (9631 cases and 210,122 controls), allergic rhinitis (AR) (11,009 cases and 359,149 controls), atopic dermatitis (AD) (13,473 cases and 336,589 controls), keratoconus (KC) (2116 cases and 24,626 controls) and 91 circulating inflammatory cytokines (n = 14,824). Two-sample univariable and multivariable MR analyses were performed. A two-step MR was then applied to determine whether systemic inflammatory cytokines mediated the effect of allergic disease on keratoconus.

The causal odds ratio (OR) estimate of genetically determined KC was 1.66 (95% CI: 1.32-2.08; P < 0.001) for AC, 1.29 (95% CI: 1.10-1.51, P = 0.0014) for AA, 1.39 (95% CI: 1.15-1.68; P < 0.001) for AR and 1.30 (95% CI: 1.17-1.45, P < 0.001) for AD. Multivariable MR indicated a suggestive association between AC and KC after conditioning on other allergic diseases (OR 1.61; 95% CI: 1.10-2.34; P adjusted = 0.054). Two-step MR revealed that the effect was not mediated by systemic inflammatory cytokines.

Our findings provide evidence of a potential causal relationship between AC and KC. The effect of AC on KC may be mediated via other systemic inflammatory cytokines not included in the present study, or by alternative mechanisms. These findings may offer insight for prevention and intervention strategies to lower the risk of KC in patients with AC.

The combined effect of systemic immune-inflammation index (SII) and aortic valve calcification (AVC) on the risk of major adverse cardiovascular events (MACE) in patients with coronary heart disease (CHD) remains unclear. This study aimed to investigate their combined association with MACE in CHD.

This retrospective cohort study included 846 CHD patients. SII was calculated as platelet count × neutrophil count / lymphocyte count, and AVC status was determined by echocardiography. Patients were divided into four groups based on median SII and AVC presence: Low SII + AVC (-), High SII + AVC (-), Low SII + AVC (+), and High SII + AVC (+). Cox regression, subgroup and sensitivity analyses assessed the association between SII + AVC and MACE.

Multivariate Cox regression revealed that, compared to the Low SII + AVC (-), MACE risk increased 6.542-fold in the High SII + AVC (+) group and 1.605-fold in the High SII + AVC (-) group (P < 0.05). Subgroup analysis indicated that, compared to the Low SII + AVC (-), MACE risk was significantly elevated in the High SII + AVC (-) group for patients over 60, both genders, with hypertension, hyperlipidemia, or without diabetes (P < 0.05). In the Low SII + AVC (+) group, MACE risk was elevated only in males (P < 0.05). The High SII + AVC (+) group had increased MACE risk in all subgroups except those with diabetes (P < 0.05). After excluding patients with estimated glomerular filtration rate < 60 mL/min/1.73m², the high SII + AVC (+) group remained significantly associated with increased MACE risk (P = 0.001), as did the High SII + AVC (-) group (P = 0.031).

The combination of SII and AVC is significantly associated with MACE risk in patients with CHD.

Protein biomarkers that reflect different pathophysiological pathways have been associated with the risk of adverse cardiovascular events. However, it is uncertain whether these associations are sustained with increasing years after the biomarkers are measured.

In this cohort study, 7745 patients with coronary heart disease who participated in the LIPID (Long-Term Intervention With Pravastatin in Ischemic Disease) trial, BNP (B-type natriuretic peptide), troponin I, cystatin-C, C-reactive protein, d-dimer and midregional proadrenomedullin were measured at baseline and after 1 year. Discrimination of plasma biomarker concentrations for cardiovascular death were evaluated in landmark analyses from 1 year for the next 5 years of the randomized trial, and for 10 additional years after trial completion. All 6 biomarkers were associated with risk of cardiovascular death (n=1903) both during and after the clinical trial (each P<0.001). C-statistics for BNP were 0.706 and 0.704; cystatin-C, 0.686 and 0.693; troponin I, 0.686 and 0.689; C-reactive protein, 0.655 and 0.684; d-dimer, 0.670 and 0.679, and midregional adrenomedullin, 0.686 and 0.688, respectively. In multivariable models, adding all 6 biomarkers to models with clinical risk factors increased the C-statistic for cardiovascular death from 0.709 to 0.775 during the clinical trial, and from 0.713 to 0.751 during 10-year follow-up after the randomized trial (P<0.001 for both).

In patients with chronic coronary heart disease, biomarkers that reflect different pathophysiological pathways are associated with the risk of cardiovascular death for at least the next 15 years.

Improved identification of individuals at high risk of developing cardiovascular disease would enable targeted interventions and potentially lead to reductions in mortality and morbidity. Our aim was to determine whether use of large-scale proteomics improves prediction of cardiovascular events beyond traditional risk factors (TRFs).

Using proximity extension assays, 2919 plasma proteins were measured in 38 380 participants of the UK Biobank. Both data- and literature-based feature selection and trained models using extreme gradient boosting machine learning were used to predict risk of major cardiovascular events (MACEs: fatal and non-fatal myocardial infarction, stroke, and coronary artery revascularization) during a 10-year follow-up. Area under the curve (AUC) and net reclassification index (NRI) were used to evaluate the additive value of selected protein panels to MACE prediction by Systematic COronary Risk Evaluation 2 (SCORE2) or the 10 TRFs used in SCORE2. SCORE2 and SCORE2 refitted to UK Biobank data predicted MACE with AUCs of 0.740 and 0.749, respectively. Data-driven selection identified 114 proteins of greatest relevance for prediction. Prediction of MACE was not improved by using these proteins alone (AUC of 0.758) but was significantly improved by combining these proteins with SCORE2 or the 10 TRFs (AUC = 0.771, P < 001, NRI = 0.140, and AUC = 0.767, P = 0.03, NRI 0.053, respectively). Literature-based protein selection (113 proteins from five previous studies) also improved risk prediction beyond TRFs while a random selection of 114 proteins did not.

Large-scale plasma proteomics with data-driven and literature-based protein selection modestly improves prediction of future MACE beyond TRFs.

To examine the association of Life's Essential 8 (LE8) with the risk of recurrent cardiovascular events among patients with CHD.

This prospective cohort study included 11,997 patients with CHD from the UK Biobank. The LE8 score was generated using five lifestyle factors (diet, body mass index, physical activity, smoking, and sleep) and three biological factors (blood lipids, blood glucose, and blood pressure). LE8 score ranged from 0 to 100 and was categorized into quartiles. Cox proportional hazards regression models were applied to estimate the hazard ratio (HR) and 95% CI (confidence interval).

During a median follow up of 12.5 years, we documented 3366 recurrent cardiovascular events, 1068 myocardial infarction, 1829 heart failure events, 703 strokes, and 934 cardiovascular deaths. The multivariable-adjusted HR (95% CI) for the highest versus the lowest quartile of LE8 score was 0.57 (0.50, 0.65) for recurrent cardiovascular events, 0.66 (0.52, 0.83) for myocardial infarction, 0.54 (0.45, 0.67) for heart failure, 0.50 (0.36, 0.68) for stroke, and 0.46 (0.37, 0.56) for cardiovascular death. Furthermore, the population attributable fraction of the lowest to the highest quartile of LE8 score were ranged from 16.2% to 32.5% for the various cardiovascular outcomes. In addition, biomarkers including renal function and inflammation collectively explained 47.6%-87.7% of the associations between the lifestyle factors and recurrent cardiovascular events.

Better cardiovascular health as measured by LE8 was associated with significantly lower risk of recurrent cardiovascular events among patients with CHD. Clinicians should prioritize educating patients with CHD on the importance of optimal cardiovascular health for secondary prevention. In addition, our findings indicated significant mediation effect of biomarkers involving of glycemic control, renal function, liver function, lipid profile, and systemic inflammation on the associations between overall lifestyle factors and recurrent cardiovascular events.

Chitinase‑3 like‑protein‑1 (CHI3L1), a glycoprotein belonging to the glycoside hydrolase family 18, binds to chitin; however, this protein lacks chitinase activity. Although CHI3L1 is not an enzyme capable of degrading chitin, it plays significant roles in abnormal glucose and lipid metabolism, indicating its involvement in metabolic disorders. In addition, CHI3L1 is considered a key player in inflammatory diseases, with clinical data suggesting its potential as a predictor of cardiovascular disease. CHI3L1 regulates the inflammatory response of various cell types, including macrophages, vascular smooth muscle cells and fibroblasts. In addition, CHI3L1 participates in vascular remodeling and fibrosis, contributing to the pathogenesis of cardiovascular disease. At present, research is focused on elucidating the role of CHI3L1 in cardiovascular disease. The present systematic review was conducted to comprehensively evaluate the effects of CHI3L1 on cardiovascular cells, and determine the potential implications in the occurrence and progression of cardiovascular disease. The present study may further the understanding of the involvement of CHI3L1 in cardiovascular pathology, demonstrating its potential as a therapeutic target or biomarker in the management of cardiovascular disease.

Protein biomarkers may contribute to the identification of vulnerable subgroups for premature mortality. This study aimed to investigate the association of plasma proteins with all-cause and cause-specific mortality among individuals with and without baseline type 2 diabetes (T2D) and evaluate their impact on the prediction of all-cause mortality in two prospective Cooperative Health Research in the Region of Augsburg (KORA) studies.

The discovery cohort comprised 1545 participants (median follow-up 15.6 years; 244 with T2D: 116 total, 62 cardiovascular, 31 cancer-related and 23 other-cause deaths; 1301 without T2D: 321 total, 114 cardiovascular, 120 cancer-related and 87 other-cause deaths). The validation cohort comprised 1031 participants (median follow-up 6.9 years; 203 with T2D: 76 total, 45 cardiovascular, 19 cancer-related and 12 other-cause deaths; 828 without T2D: 169 total, 74 cardiovascular, 39 cancer-related and 56 other-cause deaths). We used Cox regression to examine associations of 233 plasma proteins with all-cause and cause-specific mortality and Lasso regression to construct prediction models for all-cause mortality stratifying by baseline T2D. C-index, category-free net reclassification index (cfNRI), and integrated discrimination improvement (IDI) were conducted to evaluate the predictive performance of built prediction models.

Thirty-five and 62 proteins, with 29 overlapping, were positively associated with all-cause mortality in the group with and without T2D, respectively. Out of these, in the group with T2D, 35, eight, and 26 were positively associated with cardiovascular, cancer-related, and other-cause mortality, while in the group without T2D, 55, 41, and 47 were positively associated with respective cause-specific outcomes in the pooled analysis of both cohorts. Regulation of insulin-like growth factor (IGF) transport and uptake by IGF-binding proteins emerged as a unique pathway enriched for all-cause and cardiovascular mortality in individuals with T2D. The combined model containing the selected proteins (five and 12 proteins, with four overlapping, in the group with and without T2D, respectively) and clinical risk factors improved the prediction of all-cause mortality by C-index, cfNRI, and IDI.

This study uncovered shared and unique mortality-related proteins in persons with and without T2D and emphasized the role of proteins in improving the prediction of mortality in different T2D subgroups.

The Naples prognostic score (NPS) is a novel multidimensional inflammatory and nutritional assessment system in cancer patients. However, its significance in patients with chronic kidney disease (CKD) after percutaneous coronary intervention (PCI) remains unclear. The study has a single-center, retrospective design and included 631 patients with CKD who underwent index PCI between 2019 and 2022. All participants were divided into 2 groups according to the NPS (Low-risk group: n = 209; High-risk group: n = 422) and followed up until November 2022. The primary endpoint was Major Adverse Cardiac Events (MACE). NPS predicted MACE events better than other scores, besides, high-risk NPS with severe renal dysfunction (RD) group (MODEL 2) had superior MACE diagnostic efficiency than NPS high-risk group lonely. (NPS: AUC: 0.605, P < .001; MODEL 2: AUC: 0.624, P < .001, respectively). Kaplan-Meier survival analysis of two groups showed that high-risk group had higher incidence of MACE (P < .001). Meanwhile, high-risk group had higher MACE events [adjusted Hazard Ratio (aHR) 2.013, 95% CI 1.294, 3.132; P = .002]. NPS is an independent prognostic factor for CKD patients undergoing index PCI before operation whose predictive value for survival prognosis is better than other nutritional and inflammatory indicators. Compared with low NPS, patients with high NPS have a relatively poor prognosis.

Cardiovascular disease (CVD) continues to be the primary cause of mortality worldwide, underscoring the importance of identifying additional cardiovascular risk factors. The consensus is that lipid levels alone do not fully reflect the status of atherosclerosis, thus necessitating extensive research on cardiovascular biomarkers. This review encompasses a wide spectrum of methodologies for identifying novel risk factors or biomarkers for CVD. Inflammation, oxidative stress, plaque instability, cardiac remodeling, and fibrosis play pivotal roles in CVD pathogenesis. We introduce and discuss several promising biomarkers-namely, osteocalcin, angiogenin, lipoprotein-associated phospholipase A2, growth differentiation factor 15, galectin-3, growth stimulation expressed gene 2, and microRNAs, all of which have potential implications in the assessment and management of cardiovascular risk.

Persistent inflammation is associated with adverse health outcomes, but its impact on mortality has not been investigated previously among hip fracture patients. This article aims to investigate the influence of changes in levels of cytokines in the 2 months after a hip fracture repair on 5-year mortality.

This is a prospective cohort study from the Baltimore Hip Studies (BHS) with 191 community-dwelling older men and women (≥65 years) who had recently undergone surgical repair of an acute hip fracture, with recruitment from May 2006 to June 2011. Plasma interleukin-6 (IL-6), soluble tumor necrosis factor alpha receptor1 (sTNFα-R1), and interleukin-1 receptor agonist (IL-1RA) were obtained within 22 days of admission and at 2 months. All-cause mortality over 5 years was determined. Logistic regression analysis tested the associations between the cytokines' trajectories and mortality over 5 years, adjusted for covariates (age, sex, education, body mass index, lower extremity physical activities of daily living, and Charlson comorbidity index).

High levels of IL-6 and sTNFα-R1 at baseline with small or no decline at 2 months were associated with higher odds of 5-year mortality compared with those with lower levels at baseline and greater decline at 2 months after adjustment for age, and other potential confounders (OR = 4.71, p = 0.01 for IL-6; OR = 15.03, p = 0.002 for sTNFα-R1). Similar results that failed to reach significance were found for IL-1RA (OR = 2.40, p = 0.18). Those with higher levels of cytokines at baseline with greater decline did not have significantly greater mortality than the reference group, those with lower levels at baseline and greater decline.

Persistent elevation of plasma IL-6 and sTNFα-R1 levels within the first 2 months after hospital admission in patients with hip fracture is associated with higher 5-year mortality. These patients may benefit from enhanced care and earlier intensive interventions to reduce the risk of death.

Performing non-invasive carotid imaging is challenging, owing inter-operator variability and organizational barriers, but plasma proteomics can offer an alternative. We sought plasma proteins that associate with the presence of carotid plaques, their number and predict the incidence of clinically overt atherosclerotic cardiovascular events (ASCVD) above currently recognized risk factors in "apparently healthy" subjects.

We studied the plasma levels of 368 proteins in 664 subjects from the PLIC study, who underwent an ultrasound imaging screening of the carotids to check for the presence of plaques. We clustered, by artificial intelligence (A.I.), the proteins that associate with the presence, the number of plaques and that predict incident ASCVDs over 22 years (198 events were registered).

299/664 subjects had at least 1 carotid plaque (1+) (77 with only one plaque, 101 with 2 plaques, 121 with ≥3 plaques (3+)). The remaining 365 subjects with no plaques acted as controls. 106 proteins were associated with 1+ plaques, but 97 proteins significantly predicted 3+ plaques only (AUC = 0.683 (0.601-0.785), p < 0.001), when considered alone. A.I. underscored 87 proteins that improved the performance of the classical risk factors both in detecting 3+ plaques (AUC = 0.918 (0.887-0.943) versus risk factors alone, AUC = 0.760 (0.716-0.801), p < 0.001) and in predicting the incident ASCVD (AUC = 0.739 (0.704-0.773) vs risk factors alone AUC = 0.559 (0.521-0.598), p < 0.001). The chemotaxis/migration of leukocytes and interleukins/cytokines signaling were biological pathways mostly represented by these proteins.

Plasma proteomics marks the number of carotid plaques and improve the prediction of incidence ASCVDs in apparently healthy subjects.

The use of 3 biomarkers - cystatin-C (Cys-C), retinol-binding protein (RBP), and ischemia-modified albumin (IMA) - for the clinical classification and outcome of coronary heart disease (CHD) has not been adequately evaluated. We explored the serum levels of these 3 markers and evaluated their diagnostic and prognostic values in patients with CHD. This retrospective case-control study, conducted between June 2017 and June 2018, included 201 patients with CHD hospitalized at the Henan Provincial People's Hospital and 127 healthy individuals from Henan Provincial People's Hospital as controls. Cys-C, RBP, IMA levels, and other laboratory parameters in the 2 groups were determined, and patient outcomes were analyzed. Cys-C, RBP, and IMA levels were higher in the case group than in the control group (P < .05). Logistic regression analysis confirmed that these 3 biomarkers were independent risk factors for CHD. Each indicator has clinical significance in the diagnosis and prognosis of CHD, with RBP being the most significant. The AUC value for CHD detection using a combination of the 3 indicators was 0.783, and the sensitivity and specificity values were 78% and 74.6%, respectively. Simultaneous detection of Cys-C, RBP, and IMA could be an optimal method for early diagnosis and prognosis of CHD.

Adrenomedullin (ADM) is a vasodilatory peptide that plays a crucial role in maintaining cardiovascular health through its various biological functions. ADM was discovered in the acidic extract of human pheochromocytoma tissue and has been recognized for its significant effects on the vascular system. The main functions of ADM include vasodilation, controlling blood pressure and maintaining vascular integrity, although its role on cardiovascular health is broader. Research has shown that elevated levels of adrenomedullin have been observed in a large number of severe diseases, with high risk of death. In this work, we examined the role of ADM as a predictive molecule of the risk of mortality and adverse clinical outcome through a narrative review of the scientific literature. The results were divided based on the pathologies and anatomical districts examined. This review demonstrates how ADM shows, in many diseases and different systems, a close correlation with the risk of mortality. These results prove the value of ADM as a prognostic marker in various clinical contexts and diseases, with utility in the stratification of the risk of clinical worsening and/or death and in the evaluation of therapeutic efficacy. The results open new perspectives with respect to the concrete possibility that ADM enters clinical practice as an effective diagnostic and prognostic marker of death as well as a molecular target for therapies aimed at patient survival.

The increasing incidence of cardiovascular disease (CVD) is a significant global health concern, affecting millions of individuals each year. Accurate diagnosis of acute CVD poses a formidable challenge, as misdiagnosis can significantly decrease patient survival rates. Traditional biomarkers have played a vital role in the diagnosis and prognosis of CVDs, but they can be influenced by various factors, such as age, sex, and renal function. Soluble ST2 (sST2) is a novel biomarker that is closely associated with different CVDs. Its low reference change value makes it suitable for continuous measurement, unaffected by age, kidney function, and other confounding factors, facilitating risk stratification of CVDs. Furthermore, the combination of sST2 with other biomarkers can enhance diagnostic accuracy and prognostic value. This review aims to provide a comprehensive overview of sST2, focusing on its diagnostic and prognostic value as a myocardial marker for different types of CVDs and discussing the current limitations of sST2.

Drugs specifically targeting YKL-40, an over-expressed gene (CHI3L1) in various diseases remain developed. The current study is to create a humanized anti-YKL-40 neutralizing antibody and characterize its potentially therapeutic signature. We utilized in silico CDR-grafting bioinformatics to replace the complementarity determining regions (CDRs) of human IgG1 with mouse CDRs of our previously established anti-YKL-40 antibody (mAY). In fifteen candidates (VL1-3/VH1-5) of heavy and light chain variable region combination, one antibody L3H4 named Rosazumab demonstrated strong binding affinity with YKL-40 (KD = 4.645 × 10-8 M) and high homology with human IgG (80 %). In addition, we established different overlapping amino acid peptides of YKL-40 and found that Rosazumab specifically bound to residues K337, K342, and R344, the KR-rich functional domain of YKL-40. Rosazumab inhibited migration and tube formation of YKL-40-expressing tumor cells and induced tumor cell apoptosis. Mechanistically, Rosazumab induced interaction of N-cadherin with β-catenin and activation of downstream MST1/RASSF1/Histone H2B axis, leading to chromosomal DNA breakage and cell apoptosis. Treatment of xenografted tumor mice with Rosazumab twice a week for 4 weeks inhibited tumor growth and angiogenesis, but induced tumor apoptosis. Rosazumab injected in mice distributed to blood, tumor, and other multiple organs, but did not impact in function or structure of liver and kidney, indicating non-detectable toxicity in vivo. Collectively, the study is the first one to demonstrate that a humanized YKL-40 neutralizing antibody offers a valuable means to block tumor development.

Colon cancer is one of the malignant tumors with high morbidity, lethality, and prevalence across global human health. Molecular biomarkers play key roles in its prognosis. In particular, immune-related lncRNAs (IRL) have attracted enormous interest in diagnosis and treatment, but less is known about their potential functions. We aimed to investigate dysfunctional IRL and construct a risk model for improving the outcomes of patients. Nineteen immune cell types were collected for identifying house-keeping lncRNAs (HKLncRNA). GSE39582 and TCGA-COAD were treated as the discovery and validation datasets, respectively. Four machine learning algorithms (LASSO, Random Forest, Boruta, and Xgboost) and a Gaussian mixture model were utilized to mine the optimal combination of lncRNAs. Univariate and multivariate Cox regression was utilized to construct the risk score model. We distinguished the functional difference in an immune perspective between low- and high-risk cohorts calculated by this scoring system. Finally, we provided a nomogram. By leveraging the microarray, sequencing, and clinical data for immune cells and colon cancer patients, we identified the 221 HKLncRNAs with a low cell type-specificity index. Eighty-seven lncRNAs were up-regulated in the immune compared to cancer cells. Twelve lncRNAs were beneficial in improving performance. A risk score model with three lncRNAs (CYB561D2, LINC00638, and DANCR) was proposed with robust ROC performance on an independent dataset. According to immune-related analysis, the risk score is strongly associated with the tumor immune microenvironment. Our results emphasized IRL has the potential to be a powerful and effective therapy for enhancing the prognostic of colon cancer.

Calcific aortic valve stenosis (CAVS) is characterized by increasing inflammation and progressive calcification in the aortic valve leaflets and is a major cause of death in the aging population. This study aimed to identify the inflammatory proteins involved in CAVS and provide potential therapeutic targets. We investigated the observational and causal associations of 92 inflammatory proteins, which were measured using affinity-based proteomic assays. Firstly, the case-control cohort identified differential proteins associated with the occurrence and progression of CAVS. Subsequently, we delved into exploring the causal impacts of these associated proteins through Mendelian randomization. This involved utilizing genetic instruments derived from cis-protein quantitative loci identified in genome-wide association studies, encompassing a cohort of over 400,000 individuals. Finally, we investigated the gene transcription and protein expression levels of inflammatory proteins by single-cell and immunohistochemistry analysis. Multivariate logistic regression and spearman's correlation analysis showed that five proteins showed a significant positive correlation with disease severity. Mendelian randomization showed that elevated levels of two proteins, namely, matrix metallopeptidase-1 (MMP1) and sirtuin 2 (SIRT2), were associated with an increased risk of CAVS. Immunohistochemistry and single-cell transcriptomes showed that expression levels of MMP1 and SIRT2 at the tissue and cell levels were significantly higher in calcified valves than in non-calcified control valves. These findings indicate that MMP1 and SIRT2 are causally related to CAVS and open up the possibility for identifying novel therapeutic targets.

Age-related disorders are closely linked to the accumulation of senescent cells. The senescence-associated secretory phenotype (SASP) sustains and progresses chronic inflammation, which is involved in cellular and tissue dysfunction. SASP-related growth and differentiation factor-15 (GDF-15) is an immunoregulatory cytokine that is coupled to aging and thus may have a regulatory role in the development and maintenance of atherosclerosis, a major cause of cardiovascular disease (CVD). Although the effects of GDF-15 are tissue-specific and dependent on microenvironmental changes such as inflammation, available data suggest that GDF-15 has a significant role in CVD. Thus, GDF-15 is a promising biomarker and potential therapeutic target for atherosclerotic CVD.

To investigate the correlation between retinal vessel density (VD) parameters with serum B-type natriuretic peptide (BNP) in patients with coronary heart disease (CHD) using novel optical coherence tomography angiography (OCTA) denoising images based on artificial intelligence (AI).

OCTA images of the optic nerve and macular area were obtained using a Canon-HS100 OCT device in 176 patients with CHD. Baseline information and blood test results were recorded.

Retinal VD parameters of the macular and optic nerves on OCTA were significantly decreased in patients with CHD after denoising. Retinal VD of the superficial capillary plexus (SCP), deep capillary plexus (DCP) and radial peripapillary capillary (RPC) was strongly correlated with serum BNP levels in patients with CHD. Significant differences were noted in retinal thickness and retinal VD (SCP, DCP and RPC) between the increased BNP and normal BNP groups in patients with CHD.

Deep learning denoising can remove background noise and smooth rough vessel surfaces. SCP,DCP and RPC may be potential clinical markers of cardiac function in patients with CHD. Denoising shows great potential for improving the sensitivity of OCTA images as a biomarker for CHD progression.

Chitinase 3-like 1 (also known as CHI3L1 or YKL-40) is a mammalian chitinase that has no enzymatic activity, but has the ability to bind to chitin, the polymer of N-acetylglucosamine (GlcNAc). Chitin is a component of fungi, crustaceans, arthropods including insects and mites, and parasites, but it is completely absent from mammals, including humans and mice. In general, chitin-containing organisms produce mammalian chitinases, such as CHI3L1, to protect the body from exogenous pathogens as well as hostile environments, and it was thought that it had a similar effect in mammals. However, recent studies have revealed that CHI3L1 plays a pathophysiological role by inducing anti-apoptotic activity in epithelial cells and macrophages. Under chronic inflammatory conditions such as inflammatory bowel disease and chronic obstructive pulmonary disease, many groups already confirmed that the expression of CHI3L1 is significantly induced on the apical side of epithelial cells, and activates many downstream pathways involved in inflammation and carcinogenesis. In this review article, we summarize the expression of CHI3L1 under chronic inflammatory conditions in various disorders and discuss the potential roles of CHI3L1 in those disorders on various cell types.

Obstructive sleep apnea (OSA) affects almost a billion people worldwide and is associated with a myriad of adverse health outcomes. Among the most prevalent and morbid are cardiovascular diseases (CVDs). Nonetheless, randomized controlled trials (RCTs) of OSA treatment have failed to show improvements in CVD outcomes. A major limitation in our field is the lack of precision in defining OSA and specifically subgroups with the potential to benefit from therapy. Further, this has called into question the validity of using the time-honored apnea-hypopnea index as the ultimate defining criteria for OSA. Recent applications of advanced statistical methods and machine learning have brought to light a variety of OSA endotypes and phenotypes. These methods also provide an opportunity to understand the interaction between OSA and comorbid diseases for better CVD risk stratification. Lastly, machine learning and specifically heterogeneous treatment effects modeling can help uncover subgroups with differential outcomes after treatment initiation. In an era of data sharing and big data, these techniques will be at the forefront of OSA research. Advanced data science methods, such as machine-learning analyses and artificial intelligence, will improve our ability to determine the unique influence of OSA on CVD outcomes and ultimately allow us to better determine precision medicine approaches in OSA patients for CVD risk reduction. In this narrative review, we will highlight how team science via machine learning and artificial intelligence applied to existing clinical data, polysomnography, proteomics, and imaging can do just that.

Coronary heart disease (CHD) is a major global health concern, especially among individuals with type 2 diabetes (T2D). Given the crucial role of proteins in various biological processes, this study aimed to elucidate the aetiological role and predictive performance of protein biomarkers on incident CHD in individuals with and without T2D.

The discovery cohort included 1492 participants from the Cooperative Health Research in the Region of Augsburg (KORA) S4 study with 147 incident CHD cases (45 vs. 102 cases in the group with T2D and without T2D, respectively) during 15.6 years of follow-up. The validation cohort included 888 participants from the KORA-Age1 study with 70 incident CHD cases (19 vs. 51 cases in the group with T2D and without T2D, respectively) during 6.9 years of follow-up. We measured 233 plasma proteins related to cardiovascular disease and inflammation using proximity extension assay technology. Associations of proteins with incident CHD were assessed using Cox regression and Mendelian randomization (MR) analysis. Predictive models were developed using priority-Lasso and were evaluated on top of Framingham risk score variables using the C-index, category-free net reclassification index (cfNRI), and relative integrated discrimination improvement (IDI).

We identified two proteins associated with incident CHD in individuals with and 29 in those without baseline T2D, respectively. Six of these proteins are novel candidates for incident CHD. MR suggested a potential causal role for hepatocyte growth factor in CHD development. The developed four-protein-enriched model for individuals with baseline T2D (ΔC-index: 0.017; cfNRI: 0.253; IDI: 0.051) and the 12-protein-enriched model for individuals without baseline T2D (ΔC-index: 0.054; cfNRI: 0.462; IDI: 0.024) consistently improved CHD prediction in the discovery cohort, while in the validation cohort, significant improvements were only observed for selected performance measures (with T2D: cfNRI: 0.633; without T2D: ΔC-index: 0.038; cfNRI: 0.465).

This study identified novel protein biomarkers associated with incident CHD in individuals with and without T2D and reaffirmed previously reported protein candidates. These findings enhance our understanding of CHD pathophysiology and provide potential targets for prevention and treatment.

Cardiovascular disease (CVD) is among the leading causes of death worldwide. The discovery of new omics biomarkers could help to improve risk stratification algorithms and expand our understanding of molecular pathways contributing to the disease. Here, ASSIGN-a cardiovascular risk prediction tool recommended for use in Scotland-was examined in tandem with epigenetic and proteomic features in risk prediction models in ≥12 657 participants from the Generation Scotland cohort.

Previously generated DNA methylation-derived epigenetic scores (EpiScores) for 109 protein levels were considered, in addition to both measured levels and an EpiScore for cTnI (cardiac troponin I). The associations between individual protein EpiScores and the CVD risk were examined using Cox regression (ncases≥1274; ncontrols≥11 383) and visualized in a tailored R application. Splitting the cohort into independent training (n=6880) and test (n=3659) subsets, a composite CVD EpiScore was then developed.

Sixty-five protein EpiScores were associated with incident CVD independently of ASSIGN and the measured concentration of cTnI (P<0.05), over a follow-up of up to 16 years of electronic health record linkage. The most significant EpiScores were for proteins involved in metabolic, immune response, and tissue development/regeneration pathways. A composite CVD EpiScore (based on 45 protein EpiScores) was a significant predictor of CVD risk independent of ASSIGN and the concentration of cTnI (hazard ratio, 1.32; P=3.7×10-3; 0.3% increase in C-statistic).

EpiScores for circulating protein levels are associated with CVD risk independent of traditional risk factors and may increase our understanding of the etiology of the disease.

Heart failure (HF) is a complex clinical syndrome with persistently high mortality. High-throughput proteomic technologies offer new opportunities to improve HF risk stratification, but their contribution remains to be clearly defined. We aimed to systematically review prognostic studies using high-throughput proteomics to identify protein signatures associated with HF mortality.

We searched four databases and two clinical trial registries for articles published from 2012 to 2023. HF proteomics studies measuring high numbers of proteins using aptamer or antibody-based affinity platforms on human plasma or serum with outcomes of all-cause or cardiovascular death were included. Two reviewers independently screened articles, extracted data, and assessed the risk of bias. A third reviewer resolved conflicts. We assessed the risk of bias using the Risk Of Bias In Non-randomized Studies-of Exposure tool.

Out of 5131 unique articles identified, nine articles were included in the review. The nine studies were observational; three used the aptamer platform, and six used the antibody platform. We found considerable heterogeneity across studies in measurement panels, HF definitions, ejection fraction categorization, follow-up duration, and outcome definitions, and a lack of risk estimates for most protein associations. Hence, we proceeded with a systematic review rather than a meta-analysis. In two comparable aptamer studies in patients with HF with reduced ejection fraction, 21 proteins were identified in common for the association with all-cause death. Among these, one protein, WAP four-disulfide core domain protein 2 was also reported in an antibody study on HFrEF and for the association with CV death. We proposed standardized reporting criteria to facilitate the interpretation of future studies.

In this systematic review of nine studies evaluating the association of proteomics with mortality in HF, we identified a limited number of proteins common across several studies. Heterogeneity across studies compromised drawing broad inferences, underscoring the importance of standardized approaches to reporting.

Sleep-related breathing disorders (SRBD) are related to cardiovascular outcomes in patients with chronic coronary syndrome (CCS). Whether SRBD-related symptoms are associated with prognostic biomarkers in patients with CCS is not established.

Associations between frequency (never/rarely, sometimes, often, always) of self-reported SRBD-related symptoms (excessive daytime sleepiness [EDS]; morning tiredness [MT]; loud snoring; multiple awakenings/night; gasping, choking, or apnea when asleep) and levels of biomarkers related to cardiovascular prognosis (high-sensitivity C-reactive protein [hs-CRP], interleukin 6 [IL-6], high-sensitivity cardiac troponin T [hs-cTnT], N-terminal pro B-type natriuretic peptide [NT-proBNP], cystatin C, growth differentiation factor 15 [GDF-15] and lipoprotein-associated phospholipase A2 activity) were assessed at baseline in 15,640 patients with CCS on optimal secondary preventive therapy in the STABILITY trial. Cross-sectional associations were assessed by adjusted linear regression models testing for trends with the never/rarely category serving as reference.

EDS was associated (geometric mean ratio, 95% confidence interval) with increased levels of IL-6 (often 1.07 [1.03-1.10], always 1.15 [1.10-1.21]), GDF-15 (often 1.03 [1.01-1.06], always 1.07 [1.03-1.11]), NT-proBNP (always 1.22 [1.12-1.33]), and hs-cTnT (always 1.07 [1.01-1.12]). MT was associated with increased levels of IL-6 (often 1.05 [1.01-1.09], always 1.09 [1.04-1.15]), and GDF-15 (always 1.06 [1.03-1.10]). All symptoms were to some degree associated with higher levels of hs-CRP and loud snoring was also associated with decreased levels of NT-proBNP and hs-cTnT.

In patients with CCS, stepwise increased frequency of SRBD-related symptoms, such as EDS and MT, were associated with gradually higher levels of IL-6 and GDF-15, each reflecting distinct pathophysiological pathways.

To define endotypes of carotid subclinical atherosclerosis.

We integrated demographic, clinical, and molecular data (n = 124) with ultrasonographic carotid measurements from study participants in the IMPROVE cohort (n = 3340). We applied a neural network algorithm and hierarchical clustering to identify carotid atherosclerosis endotypes. A measure of carotid subclinical atherosclerosis, the c-IMTmean-max, was used to extract atherosclerosis-related features and SHapley Additive exPlanations (SHAP) to reveal endotypes. The association of endotypes with carotid ultrasonographic measurements at baseline, after 30 months, and with the 3-year atherosclerotic cardiovascular disease (ASCVD) risk was estimated by linear (β, SE) and Cox [hazard ratio (HR), 95% confidence interval (CI)] regression models. Crude estimates were adjusted by common cardiovascular risk factors, and baseline ultrasonographic measures. Improvement in ASCVD risk prediction was evaluated by C-statistic and by net reclassification improvement with reference to SCORE2, c-IMTmean-max, and presence of carotid plaques. An ensemble stacking model was used to predict endotypes in an independent validation cohort, the PIVUS (n = 1061). We identified four endotypes able to differentiate carotid atherosclerosis risk profiles from mild (endotype 1) to severe (endotype 4). SHAP identified endotype-shared variables (age, biological sex, and systolic blood pressure) and endotype-specific biomarkers. In the IMPROVE, as compared to endotype 1, endotype 4 associated with the thickest c-IMT at baseline (β, SE) 0.36 (0.014), the highest number of plaques 1.65 (0.075), the fastest c-IMT progression 0.06 (0.013), and the highest ASCVD risk (HR, 95% CI) (1.95, 1.18-3.23). Baseline and progression measures of carotid subclinical atherosclerosis and ASCVD risk were associated with the predicted endotypes in the PIVUS. Endotypes consistently improved measures of ASCVD risk discrimination and reclassification in both study populations.

We report four replicable subclinical carotid atherosclerosis-endotypes associated with progression of atherosclerosis and ASCVD risk in two independent populations. Our approach based on endotypes can be applied for precision medicine in ASCVD prevention.

Acute coronary syndrome (ACS) is a common acute myocardial ischemia syndrome and is one of the death-related causes of cardiovascular diseases. Identifying biomarkers to indicate disease severity and predict the occurrence of major adverse cardiovascular events (MACE) would benefit the clinical prognosis of ACS. This study estimated the expression and significance of lncRNA TPRG1-AS1 in the onset and development of ACS, aiming to explore a novel biomarker for the diagnosis and prognosis of ACS.

A total of 109 ACS patients and 66 patients who received coronary angiography and excluded ACS were enrolled in this study. TPRG1-AS1 in the serum of study subjects was analyzed by PCR. The significance of TPRG1-AS1 in screening ACS was evaluated by ROC analysis. The association of TPRG1-AS1 with the disease severity of ACS was assessed by Pearson correlation analysis with patients' clinicopathological features. The potential of TPRG1-AS1 in predicting the occurrence of MACE was assessed by logistic regression analysis.

Significant upregulation of TPRG1-AS1 was observed in ACS patients, which served as a risk factor for ACS and distinguish between ACS patients and the normal group. TPRG1-AS1 was positively correlated with Gensini score, cys-C, cTnI, and NT-proBNP levels of ACS patients, which indicate severe development of ACS. Additionally, increasing serum TPRG1-AS1 was associated with the high incidence of MACE during patients' hospitalization and was identified as a risk factor for MACE in ACS patients.

Upregulated TPRG1-AS1 in ACS served as a diagnostic biomarker and predicted the severe development of patients.

Treatment of cardiovascular diseases (CVD) is a substantial burden to healthcare systems worldwide. New tools are needed to improve precision of treatment by optimizing the balance between efficacy, safety, and cost. We developed a high-throughput multi-marker decision support instrument which simultaneously quantifies proteins associated with CVD.

Candidate proteins independently associated with different clinical outcomes were selected from clinical studies by the screening of 368 circulating biomarkers. We then custom-designed a quantitative PEA-panel with 21 proteins (CVD-21) by including recombinant antigens as calibrator samples for normalization and absolute quantification of the proteins. The utility of the CVD-21 tool was evaluated in plasma samples from a case-control cohort of 4224 patients with chronic coronary syndrome (CCS) using multivariable Cox regression analyses and machine learning techniques. The assays in the CVD-21 tool gave good precision and high sensitivity with lower level of determination (LOD) between 0.03-0.7 pg/ml for five of the biomarkers. The dynamic range for the assays was sufficient to accurately quantify the biomarkers in the validation study except for troponin I, which in the modeling was replaced by high-sensitive cardiac troponin T (hs-TnT). We created seven different multimarker models, including a reference model with NT-proBNP, hs-TnT, GDF-15, IL-6, and cystatin C and one model with only clinical variables, for the comparison of the discriminative value of the CVD-21 tool. All models with biomarkers including hs-TnT provided similar discrimination for all outcomes, e.g. c-index between 0.68-0.86 and outperformed models using only clinical variables. Most important prognostic biomarkers were MMP-12, U-PAR, REN, VEGF-D, FGF-23, TFF3, ADM, and SCF.

The CVD-21 tool is the very first instrument which with PEA simultaneously quantifies 21 proteins with associations to different CVD. Novel pathophysiologic and prognostic information beyond that of established biomarkers were identified by a number of proteins.

Integrated analyses of plasma proteomic and genetic markers in prospective studies can clarify the causal relevance of proteins and discover novel targets for ischemic heart disease (IHD) and other diseases.

The purpose of this study was to examine associations of proteomics and genetics data with IHD in population studies to discover novel preventive treatments.

We conducted a nested case-cohort study in the China Kadoorie Biobank (CKB) involving 1,971 incident IHD cases and 2,001 subcohort participants who were genotyped and free of prior cardiovascular disease. We measured 1,463 proteins in the stored baseline samples using the OLINK EXPLORE panel. Cox regression yielded adjusted HRs for IHD associated with individual proteins after accounting for multiple testing. Moreover, cis-protein quantitative loci (pQTLs) identified for proteins in genome-wide association studies of CKB and of UK Biobank were used as instrumental variables in separate 2-sample Mendelian randomization (MR) studies involving global CARDIOGRAM+C4D consortium (210,842 IHD cases and 1,378,170 controls).

Overall 361 proteins were significantly associated at false discovery rate <0.05 with risk of IHD (349 positively, 12 inversely) in CKB, including N-terminal prohormone of brain natriuretic peptide and proprotein convertase subtilisin/kexin type 9. Of these 361 proteins, 212 had cis-pQTLs in CKB, and MR analyses of 198 variants in CARDIOGRAM+C4D identified 13 proteins that showed potentially causal associations with IHD. Independent MR analyses of 307 cis-pQTLs identified in Europeans replicated associations for 4 proteins (FURIN, proteinase-activated receptor-1, Asialoglycoprotein receptor-1, and matrix metalloproteinase-3). Further downstream analyses showed that FURIN, which is highly expressed in endothelial cells, is a potential novel target and matrix metalloproteinase-3 a potential repurposing target for IHD.

Integrated analyses of proteomic and genetic data in Chinese and European adults provided causal support for FURIN and multiple other proteins as potential novel drug targets for treatment of IHD.

Anemia, inflammatory status, and malnutrition are all important factors in the prognosis of cardiovascular disease (CVD), and their interactions are also noteworthy. A recent scoring system, the hemoglobin albumin lymphocyte and platelet (HALP) score, combining multi-dimensional metrics, has been used in the prognoses of many diseases except coronary heart disease (CHD). Herein, this study aims to explore the association between HALP score and all-cause mortality in patients with CHD.

Demographic and clinical data of adult patients with CHD were extracted from the National Health and Nutrition Examination Surveys (NHANES) database from 2003 to 2018 in this retrospective cohort study. Weighted univariate and multivariate COX proportional hazard models were used for covariates screening and exploration of the association between HALP score and all-cause mortality. The evaluation indexes were hazard ratios (ORs) and 95% confidence intervals (CIs). Kaplan-Meier (KM) curve and the receiver operator characteristic (ROC) curve were used to assess the predictive performance of HALP on CHD prognosis. In addition, subgroup analyses of age and congestive heart failure (CHF) were also performed.

Among the eligible patients, 657 died of all-cause mortality. After adjusting for the covariates including age, education level, PIR, marital status, smoking, physical activity, total energy intake, CHF, stroke, hypertension, DM, CKD, cancer or malignancy, monocyte, drug for CVD, treatment for anemia, anticoagulants drug, and adrenal cortical steroids, we found that HALP score was negatively associated with the risk of all-cause mortality [HR = 0.83, 95% CI: (0.74-0.93)]. Compared with patients with high HALP scores, those who had lower HALP scores seemed to have a higher risk of all-cause mortality (all P < 0.05). HALP score has a potential predictive value on CHD prognosis with an area under the curve (AUC) of 0.61. Furthermore, in patients aged <65 years, with or without CHF, a lower HALP score was also associated with a higher risk of all-cause mortality (all P < 0.05).

HALP score has a potential predictive value on CHD prognosis; however, the causal association between HALP score and mortality in patients with CHD needs further exploration.

Cardiovascular disease (CVD) is a leading cause of morbidity and mortality worldwide, and senescent cells have emerged as key contributors to its pathogenesis. Senescent cells exhibit cell cycle arrest and secrete a range of proinflammatory factors, termed the senescence-associated secretory phenotype (SASP), which promotes tissue dysfunction and exacerbates CVD progression. Omics technologies, specifically transcriptomics and proteomics, offer powerful tools to uncover and define the molecular signatures of senescent cells in cardiovascular tissue. By analyzing the comprehensive molecular profiles of senescent cells, omics approaches can identify specific genetic alterations, gene expression patterns, protein abundances, and metabolite levels associated with senescence in CVD. These omics-based discoveries provide insights into the mechanisms underlying senescence-induced cardiovascular damage, facilitating the development of novel diagnostic biomarkers and therapeutic targets. Furthermore, integration of multiple omics data sets enables a systems-level understanding of senescence in CVD, paving the way for precision medicine approaches to prevent or treat cardiovascular aging and its associated complications.