DTA

Archivio Digitale delle Tesi e degli elaborati finali elettronici

 

Tesi etd-03292021-140317

Tipo di tesi
Dottorato
Autore
CHIAPPINO, SARA
URN
etd-03292021-140317
Titolo
Total and bGGT plasma fraction activity, and cardiovascular risk stratification in a general population. Relationships with epicardial fat, coronary artery calcium score
Settore scientifico disciplinare
MED/11
Corso di studi
Istituto di Scienze della Vita - TRANSLATIONAL MEDICINE
Commissione
relatore Prof. EMDIN, MICHELE
Parole chiave
  • cardiovascular risk
  • coronary calcium score
  • epicardial fat
  • gamma-glutamyltransferase
Data inizio appello
15/07/2021;
Disponibilità
parziale
Riassunto analitico
Coronary heart disease (CAD) is the first cause of mortality in the industrialized countries. The risk of cardiovascular events in asymptomatic subjects is generally evaluated through prediction models that have some limitations. This PhD program had the main goal to identify novel tools for risk stratification in the general population, among circulating biomarkers and computed tomography findings.
We evaluated around 900 subjects free from cardiovascular and lung disease, aged between 45 and 75 years, living in the small Tuscan town of Montignoso, Italy. The subjects were enrolled between May 2010 and October 2011 and submitted to a questionnaire about cardiovascular risk factors, comorbidities lifestile and drug intake. Than a physical examination, transthoracic echocardiogram, a laboratory examination, and a non-contrast CT chest scan with coronary calcium score evaluation were performed.
In this project we aimed to define the prognostic value of gamma-glutamyltransferase (GGT) and its fractions. GGT has been established as a risk predictor for atherosclerotic coronary artery disease, with a highly debated role. The GGT catalytic activity, which are expressed on the cellular membrane surface in serum, is responsible of the extracellular catabolism of glutathione, the main intracellular antioxidant in mammals. The cysteinyl-glycine that derives from the hydrolysis of glutathione by the GGT, triggers the iron-dependent production of free radicals and cause the lipoprotein oxidation in vitro. The presence of GGT inside the coronary atherosclerotic plaque represent the pathophysiological basis for the hypothesis of a direct participation of the GGT in the low-density lipoprotein oxidation inside the plaque and in the atherogenesis progression. Four GGT fractions have been identified by a procedure based on gel filtration chromatography followed by postcolumn injection of fluorescent GGT substrate: b-GGT, m-GGT, s-GGT (likely lipoprotein-bound, molecular masses >2000, 940, and 140kDa, respectively), and a free fraction (f-GGT, 70kDa). The fraction with the highest molecular weight, big-GGT (b-GGT), correlates with traditional cardiovascular risk factors and has been detected into the atherosclerotic plaques. In the present study we analyzed the correlation between GGT fractions and conventional risk factors and their prognostic role.
The extent of coronary artery calcifications, expressed as the coronary artery calcium (CAC) score on non-contrast chest computed tomography (CT), may be evaluated for the purpose of risk stratification in primary cardiovascular prevention. Indeed, elevated coronary artery calcium (CAC) score denote an increase of cardiovascular events risk independently on the known risk factors, particularly in subjects considered at intermediate risk according to the Framingham score. Furthermore, the epicardial fat is a metabolically and immunologically active tissue involved in atherogenesis, and epicardial fat volume (EFV) has been associated with cardiovascular risk factors, the extent and severity of CAD, and final outcome. The project evaluated the prognostic significance of CAC score and EFV in this specific population as well its relationship with GGT and its fractions. As no standard method for EFV calculation is currently available, manual calculation is cumbersome, time-consuming, and operator-dependent, we developed a deep learning method for automated calculation of the EFV.
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