Description

Background - The process of chronological aging significantly contributes to structural and functional changes within the vasculature, emerging as a major risk factor for atherosclerotic disease and acute thrombotic events [1,2]. Furthermore, age-related vascular deterioration can be influenced by lifestyle choices, environmental factors, and external stimuli, resulting in a gradual decline in vascular integrity and functionality [3,4].

In order, to identify potential targets for therapeutic intervention to delay or reverse the deleterious consequences of vascular aging, it is crucial to better understand the cellular and molecular mechanisms of vascular aging as well as to better define how environmental factors can accelerate the process [3-5].

Over the past decades, DNA damage-both telomeric and non-telomeric, alongside mitochondrial impairments-has emerged as a pivotal trigger in vascular aging and the development of atherosclerosis [4-11]. A wealth of evidence supports the presence of oxidative DNA lesions, telomere attrition, and mitochondrial DNA damage in both experimental models and human plaque samples [12-17], as well as in the peripheral cells of individuals with atherosclerosis [9,18-21].

Moreover, it is increasingly evident that genomic instability can directly impact vascular cellular function by triggering signaling pathways that lead to a multitude of pathophysiological changes. These changes encompass inflammation, apoptosis, autophagy, and ultimately, cellular senescence, which is marked by the secretion of the "senescence-associated secretory phenotype" (SASP). [4-11]. The robust mechanistic association between DNA damage and cellular aging underscores DNA damage as a prime candidate for the primary cause of aging [22]. Targeting DNA damage and its mechanistic correlates may provide a logical basis for the development of unified interventions aimed at mitigating age-related dysfunction and disease [22].

Nevertheless, the precise mechanisms linking DNA damage to SASP in vascular cells, as well as its role in the pathogenesis of atherosclerosis and vulnerable atheroma, remain elusive.

Recent evidence underscores mutual crosstalk between telomere dysfunction and mitochondrial dysmetabolism in the process of cellular senescence [23-26], emphasizing the need to further elucidate this complex and intricate connection, which can open new potential therapeutic strategies for age-related disease [27].

Further studies are warranted to understand the underlying mechanisms by which mitochondrial dysfunction influences telomere length and vice versa, and how their interplay contributes to the process of vascular aging [26].

We hypothesize that complex molecular mechanisms that link telomere dysfunction, mtDNA damage and non-coding RNA deregulation are involved in the process of vascular aging, promoting the development and progression of atherosclerosis. Consequently, peripheral aging markers of genetic damage and non-coding RNA may be useful to characterize the development of a vulnerable plaque in culprit vessels and to improve the prognosis of patients.

Aims - The specific aims of the present proposal are: 1) to investigate the association between peripheral markers of cell aging [telomere length (LTL) and mitochondrial DNA (mtDNAcn) content] and non-coding RNA deregulation (lncRNA TERRA, MitomiR) in the different spectra of angina pectoris (stable angina, unstable, NSTEMI, and STEMI) and 2) to evaluate their association with major adverse cardiovascular events (MACE) within 12 months of enrolment.

Patients with cardiac shock, congestive heart failure, end-stage renal disease, and coronary artery bypass graft will be excluded. An accurate evaluation of the plaque morphology will be acquired for patients who will be screened for optical coherence tomography (OCT) examination or intravascular ultrasound (IVUS) of the culprit artery.

Study design - Prospective, single center non-randomized observational study. Sample and data collection - A peripheral blood sample (approximately 10 mL) will be collected in each patient before undergoing diagnostic or therapeutic angiography procedures and used for the extraction of DNA and/or RNA. A biobank of other biological samples (whole blood, plasma, serum, clot, PBMCs) will be established. Approximately 12 months after enrollment, a clinical follow-up will be performed for all patients enrolled in the study through a routine medical visit or telephone interview to evaluate adverse cardiovascular events (MACE - death, myocardial infarction or need for subsequent revascularizations).

Biomarkers determination - LTL and mtDNAcn are measured after DNA extraction from blood leucocytes, while lnc-RNA TERRA and mitomiR, after RNA extraction from blood leucocytes, and analyzed using Real-Time PCR techniques.

Sample size - To detect a medium effect size (f = 0.25) in the difference of the mean LTL value between the groups, we estimate that a sample size of at least 232 total patients is required, with an alpha level of 0.05 and a power of 90% or higher. Accounting for a 10% dropout rate, the total number of patients to be enrolled should be at least 260.

Statistical analysis - The normal distribution of the data will be tested using the Kolmogorov-Smirnov test.

Continuous variables will be presented as mean, standard deviation, median, first and third quartile. Categorical variables will be expressed as numbers and percentages.

Comparisons between two groups will be performed using the Student's t test for independent samples for continuous variables, and the chi-square test or Fisher's exact test for categorical ones. Comparisons between more than two groups will be tested with the one-way analysis of variance (ANOVA) followed by Bonferroni post-hoc tests for 2-group comparisons. If the assumption of normality of the data distribution is not satisfied, the equivalent non-parametric tests will be used. The Pearson or Spearman coefficient will be calculated to investigate the correlation between the variables in a univariate way. Event-free survival curves will be constructed using the Kaplan-Meier model and tested with the log-rank test between different groups. The univariate and multivariate Cox proportional hazards model will be used to identify the predictive value of each variable against the MACE event; the data will be expressed with HR and their 95% confidence interval