Atherosclerosis and Its Related Laboratory Biomarkers

Della Corte, Vittoriano; Todaro, Federica; Cataldi, Marco; Tuttolomondo, Antonino

doi:10.3390/ijms242115546

Cited by 6 publications

(3 citation statements)

References 111 publications

(126 reference statements)

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“…Currently, there are several serum markers used to identify early AS, such as lipoproteins, ceramides, C-reactive proteins, high-sensitivity C-reactive proteins, matrix metalloproteinases, and miRNAs, among which high-sensitivity C-reactive proteins are comprehensively validated in the prediction of atherosclerotic cardiovascular events 45 – 47 . In our study, we identified six Hub genes for AS, and the nomogram demonstrated satisfactory diagnostic efficacy.…”

Section: Discussionmentioning

confidence: 99%

Identification of key genes for atherosclerosis in different arterial beds

Wu,

Pan,

Zhou

et al. 2024

Sci Rep

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Atherosclerosis (AS) is the pathologic basis of various cardiovascular and cerebrovascular events, with a high degree of heterogeneity among different arterial beds. However, mechanistic differences between arterial beds remain unexplored. The aim of this study was to explore key genes and potential mechanistic differences between AS in different arterial beds through bioinformatics analysis. Carotid atherosclerosis (CAS), femoral atherosclerosis (FAS), infrapopliteal atherosclerosis (IPAS), abdominal aortic atherosclerosis (AAS), and AS-specific differentially expressed genes (DEGs) were screened from the GSE100927 and GSE57691 datasets. Immune infiltration analysis was used to identify AS immune cell infiltration differences. Unsupervised cluster analysis of AS samples from different regions based on macrophage polarization gene expression profiles. Weighted gene co-expression network analysis (WGCNA) was performed to identify the most relevant module genes with AS. Hub genes were then screened by LASSO regression, SVM-REF, and single-gene differential analysis, and a nomogram was constructed to predict the risk of AS development. The results showed that differential expression analysis identified 5, 4, 121, and 62 CAS, FAS, IPAS, AAS-specific DEGs, and 42 AS-common DEGs, respectively. Immune infiltration analysis demonstrated that the degree of macrophage and mast cell enrichment differed significantly in different regions of AS. The CAS, FAS, IPAS, and AAS could be distinguished into two different biologically functional and stable molecular clusters based on macrophage polarization gene expression profiles, especially for cardiomyopathy and glycolipid metabolic processes. Hub genes for 6 AS (ADAP2, CSF3R, FABP5, ITGAX, MYOC, and SPP1), 4 IPAS (CLECL1, DIO2, F2RL2, and GUCY1A2), and 3 AAS (RPL21, RPL26, and RPL10A) were obtained based on module gene, gender stratification, machine learning algorithms, and single-gene difference analysis, respectively, and these genes were effective in differentiating between different regions of AS. This study demonstrates that there are similarities and heterogeneities in the pathogenesis of AS between different arterial beds.

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Section: Discussionmentioning

confidence: 99%

Identification of key genes for atherosclerosis in different arterial beds

Wu,

Pan,

Zhou

et al. 2024

Sci Rep

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“…Accurate early risk stratification could facilitate the delivery of timely treatment for patients with CAD and may improve treatment outcomes in these cases. In recent years, researchers have identified serum biomarkers that are associated with an elevated risk of atherosclerotic events [2]. Many of these biomarkers, such as lipoprotein, C-reactive protein, and others, either alone or in combination, have been integrated into risk prediction models to assess whether their inclusion improves prediction accuracy [3].…”

Section: Introductionmentioning

confidence: 99%

Impact of lipoprotein(a) and fibrinogen on prognosis in patients with coronary artery disease: A retrospective cohort study

Liang,

Liu

et al. 2024

THC

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BACKGROUND: Despite the considerable progress made in preventative methods, medication, and interventional therapies, it remains evident that cardiovascular events (CVEs) continue to be the primary cause of both death and morbidity among individuals diagnosed with coronary artery disease (CAD). OBJECTIVE: To compare the connection between lipoprotein a (Lp[a]), fibrinogen (Fib), and both parameters combined with all-cause mortality to detect their value as prognostic biomarkers. METHODS: This is a retrospective study. Patients diagnosed with CAD between January 2007 and December 2020 at the Guangdong Provincial People’s Hospital (China) were involved in the study. 43,367 patients met the eligibility criteria. The Lp(a) and Fib levels were distributed into three tertile groups (low, medium, and high). All of the patients included in the study were followed up for all-cause mortality. Kaplan–Meier and Cox regression were performed to determine the relationship between Lp(a), Fib, and all-cause mortality. A concordance statistics model was developed to detect the impact of Fib and Lp(a) in terms of anticipating poor outcomes in patients with CAD. RESULTS: Throughout a median follow-up of 67.0 months, 6,883 (15.9%) patients died. Participants with high Lp(a) (above 27.60 mg/dL) levels had a significantly higher risk for all-cause mortality than individuals with low Lp(a) levels (below 11.13 mg/dL; adjusted hazard ratio [aHR] 1.219, 95% confidence interval [CI]: 1.141–1.304, p< 0.001). Similarly, patients with high Fib levels (above 4.32 g/L) had a significantly greater risk of developing all-cause mortality compared with those with reduced Fib levels (below 3.41 g/L; aHR 1.415, 95% CI: 1.323–1.514, p< 0.001). Patients with raised Lp(a) and Fib levels had the maximum risk for all-cause mortality (aHR 1.702; 95% CI: 1.558–1.859, p< 0.001). When considered together, Lp(a) and Fib caused a significant elevation of the concordance statistic by 0.009 (p< 0.05), suggesting a higher value for predicting mortality when combining the two indicators. CONCLUSION: High Lp(a) and Fib levels could be used as predictive biomarkers for all-cause mortality in individuals with CAD. The prediction accuracy for all-cause mortality improved after combining the two parameters.

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“…Physical activity can decrease hs-CRP levels and CAD risk [5]. The atherosclerotic process is connected with hs-CRP, as this biomarker stimulates the uptake of LDL cholesterol into macrophages [6]. Hs-CRP is also implicated in cardiovascular outcomes [7,8].…”

Section: Introductionmentioning

confidence: 99%

The Relationship between Job Strain and Ischemic Heart Disease Mediated by Endothelial Dysfunction Markers and Imaging

Moisii,

Jari,

Ursu

et al. 2024

Medicina

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Background and Objectives: Job strain is a psychological, physical, and behavioral stress that occurs at the workplace. Job strain is associated with more than double the normal risk of coronary artery disease (CAD). The main aim of this study was to determine the association between job strain and the following parameters: high-sensitivity C-reactive protein (hs-CRP), the albumin urine excretion rate (AUER), and secondary-level testing. Materials and Methods: This study was a descriptive cross-sectional study conducted on patients who underwent cardiological assessment between October 2023 and February 2024 at the Promedicanon Cardiology Center. This study comprised 210 participants, with two groups: 105 chronic coronary syndromes (CCS) patients and 105 no-CCS patients. The baseline characteristics collected were age, gender, education, rural/urban environment, traditional CAD risk factors, hs-CRP, and AUER. The secondary-level testing included an electrocardiogram (ECG), echocardiography, and enhanced contrast computed tomography (ECCT). Psychological questionnaires comprised the tertiary-level testing, including the PHQ-9 depression questionnaire, and the satisfaction with work scale (SWWS) for job strain (Likert score). Results: The baseline characteristics were all significantly different between the groups (p < 0.05) except for total cholesterol. The hs-CRP level had a mean value of 0.4837 ± 0.19082 in the CCS group; for the no-CCS group, the hs-CRP mean value was 0.2289 ± 0.11009; p-value < 0.001. The AUER had a mean value of 42.770 ± 12.8658 for the CCS group and 26.432 ± 9.7338 for the no-CCS group; p-value < 0.001. For the associations between secondary-level testing and job strain: p < 0.001 for ST depression, negative T-waves, and q-waves; p = 0.415 for atrial fibrillation (AF); p = 0.018 for wall motion studies; p = 0.005 for ECCT. The association between job strain and AF had no statistical significance. The contractility of left ventricle walls and coronary calcification score were associated with job strain, with statistical significance. The p-value was 0.013 for the relationship between depression and the ECCT; for the association between depression and CCS status, the p-value was 0.021. Depression is usually diagnosed in job strain. The association between depression, and coronary calcification, as well as depression and CCS status had statistical significance. Conclusions: Job strain increased the hs-CRP level and AUER in both the CCS and no-CCS patients. The primary and secondary prevention of CHD could also include interventions to reduce job strain.

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Atherosclerosis and Its Related Laboratory Biomarkers

Cited by 6 publications

References 111 publications

Identification of key genes for atherosclerosis in different arterial beds

Identification of key genes for atherosclerosis in different arterial beds

Impact of lipoprotein(a) and fibrinogen on prognosis in patients with coronary artery disease: A retrospective cohort study

The Relationship between Job Strain and Ischemic Heart Disease Mediated by Endothelial Dysfunction Markers and Imaging

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