@conference{
author = "Jovičić, Snežana and Ignjatović, Svetlana and Kangrga, Ranka and Dajak, Marijana and Majkić-Singh, Nada",
year = "2015",
abstract = "U kliničkoj praksi koristi se nekoliko skorova za
procenu kratkoročnog (10-godišnjeg) rizika od
pojave različitih oblika kardiovaskularnih bolesti
(KVB) koji se zasnivaju na multivarijabilnim regre-
sionim jednačinama izvedenim iz rezultata praćenja
različitih kohortnih grupa. Međutim, pošto je starost
promenljiva kojoj se dodeljuje najveći broj poena u
modelima 10-godišnjeg rizika, mnoge osobe sa zna
čajnim opterećenjem faktorima rizika imaju kratko-
ročni rizik daleko ispod granice koja uslovljava inten-
zivan tretman, iako njihov dugoročni (30-godišnji)
rizik može biti značajan. Takođe, drugi biomarkeri
mogu da identifikuju osobe sa većim kardiovasku-
larnim rizikom od onog izračunatog primenom skoro-
va kratkoročnog rizika. Cilj rada bio je da se analizira
priroda uticaja ispitivanih biomarkera na kardiovas-
kularni rizik i njihovo grupisanje, kao i povezanost
dobijenih faktora sa kategorizacijom 30-godišnjeg
rizika faktorskom analizom. Pomoću interaktivnog
kalkulatora »30-year risk of cardiovascular disease«
izračunavan je dugoročni 30-godišnji rizik za pojavu
»kompletne« KVB (sve manifestacije KVB) i »teške«
KVB (potencijalno fatalne komplikacije KVB). Analiza
glavnih komponenti je korišćena za ispitivanje
grupisanja markera inflamacije [visoko-osetljivi C-
reaktivni protein (hsCRP), serumski amiloid A (SAA),
fibrinogen, a1-kiseli glikoprotein (A1AGP), haptoglo-
bin, C3 i C4 komponente komplementa], metabo-lizma lipida [non-HDL i LDL holesterol, trigliceridi,
apolipoprotein A-I (apo A-I), apolipoprotein B (apo
B), lipoprotein (a) (Lp(a))], bubrežne [kreatinin,
mokraćna kiselina, cistatin C (Cys-C)] i srčane funk-
cije [N-terminalni pro-natriuretički peptid tip B (NT-
proBNP), visoko-osetljivi srčani troponin T (hs-cTnT)],
dobijenih analizom uzoraka seruma 242 zdrave oso-
be. Faktorskom analizom identifikovano je 5 klastera,
kojima je objašnjeno je 67,4% ukupne varijacije, ras-
poređene na sledeći način 1) 29,7% »sistemska infla-
macija« (hsCRP, fibrinogen, SAA, A1AGP, haptoglo-
bin, C3 i C4 komponenta komplementa); 2) 12,5%
»aterogena dislipidemija« (LDL i non-HDL holesterol,
apo B i trigliceridi); 3) 11,0% »kardiorenalni faktor«
(kreatinin, mokraćna kiselina, Cys-C i hs-cTnT); 4)
7,6% »hemodinamski faktor« (NT-proBNP) i 5) 6,7%
»lipoproteinski faktor« [apo A-I, Lp(a)]. Prediktivne
vrednosti u proceni 30-godišnjeg rizika za »komplet-
nu KVB« i »tešku KVB« su bile značajne za četiri fak-
tora (OR 1,892–5,590; P<0,0001 i OR 2,183–
5,931; P<0,0001, redom), a »hemodinamski fak-
tor« nije imao statistički značajan prediktivni potenci-
jal za vrednosti iznad optimalnih/normalnih za odgo-
varajući pol i starost (P>0,05). Površine ispod ROC
krivih (AUC) modela sa pet faktora u predikciji
povećanog 30-godišnjeg rizika za »kompletnu KVB« i
»tešku KVB« iznosile su 0,881 i 0,888, redom, i nisu
bile statistički značajno različite od multivarijabilnog
logističkog modela od 18 polaznih parametara
(0,892 i 0,901; P>0,05; redom). Sistemska inflama-
cija, aterogena dislipidemija, kardiorenalna funkcija i
lipoproteinski status nezavisno doprinose dugo-
ročnom, 30-godišnjem riziku iznad normalnog/opti-
malnog kako za ozbiljne komplikacije KVB, tako i za
sve vrste kardiovaskularnih komplikacija., Several risk score algorithms for short-term (10-
year) cardiovascular risk assessment based on multi-
variable regression equations derived from different
cohorts are being used in clinical practice. However,
since the age is variable with the strongest influence
on short-term risk, many individuals with moderate
increase of other traditional risk factors would have a
10-year risk below cutoff for intensive treatment, but
a significant long-term (30-year) risk. Also, other bio-
markers might identify persons with higher actual
cardiovascular risk compared with calculated using
short-term risk scores. The aim of this study was to
analyze the nature of influence of examined biomark-
ers on cardiovascular risk and their clustering, as well
as relations of identified factors with long-term 30-
year risk categorization, using factor analysis.
Interactive calculator »30-year risk of cardiovascular
disease« was used for long-term 30-year risk calcula-
tion, for both »full CVD« (all manifestations of cardio-
vascular disease) and »hard CVD« (serious manifesta-
tions of CVD). Principal component analysis was used
to investigate clustering of markers of inflammation
[high sensitivity C-reactive protein (hsCRP), serum
amyloid A (SAA), fibrinogen, a1-acid glycoprotein
(A1AGP), haptoglobin, C3 and C4 complement
components], lipid metabolism [non-HDL and LDL
cholesterol, triglycerides, apolipoprotein A-I (apo A-
I), apolipoprotein B (apo B), lipoprotein (a) (Lp(a))], renal [creatinine, uric acid, cystatin C (Cys-C)] and
cardiac function [N-terminal pro-natriuretic peptide
type B (NT-proBNP), high sensitivity cardiac troponin
T (hs-cTnT)], obtained from 242 apparently healthy
individuals. Factor analysis identified five clusters,
which explained 67.4% of the total variance distrib-
uted as follows: 1) 29.7% »systemic inflammation«
(hsCRP, fibrinogen, SAA, A1AGP, haptoglobin, C3,
C4); 2) 12.5% »atherogenic dyslipidemia«, (LDL and
non-HDL cholesterol, apo B, triglycerides); 3) 11.0%
»cardiorenal factor« (creatinine, uric acid, Cys-C, hs-
cTnT); 4) 7.6% »hemodynamic factor« (NT-proBNP);
and 5) 6.7% »lipoprotein factor« [apo A-I, Lp(a)].
When estimating 30-year risk from both »full CVD«
and »hard CVD«, predictive values were significant
for four factors (OR 1.892–5.590, P<0.0001 and
OR 2.183–5.931, P<0.0001, respectively), and
»hemodynamic factor« had no statistical significance
in predicting potential for values above optimal/nor-
mal for corresponding gender and age (P>0.05).
The areas under the receiver operating characteristic
curves (AUCs) of the five factor model in predicting
increased 30-year risk for »full CVD« and »hard CVD«
were 0.881 and 0.888, respectively, which were not
statistically significantly different from AUCs of the
multivariable logistic model of 18 original parameters
(0.892 and 0.901, P>0.05, respectively). Long-
term, 30-year risk above normal/optimal for hard
CVD complications and for all kinds of cardiovascular
complications was independently contributed by sys-
temic inflammation, atherogenic dyslipidemia, car-
diorenal function and lipoprotein status.",
publisher = "Društvo medicinskih biohemičara Srbije, Beograd",
journal = "XIX kongres medicinske i laboratorijske medicine sa me|unarodnim učešćem, 2015, Journal of Medical Chemistry, 34, 1, 2015.",
title = "Faktorska analiza povezanosti inflamatornih, lipidnih, srčanih i bubrežnih biomarkera sa Klasifikacijom dugoročnog 30-godišnjeg kardiovaskularnog rizika, Factor analysis of association of lipid, inflammatory, cardiac and renal biomarkers with long-term 30-year cardiovascular risk classification",
volume = "34",
pages = "68-69",
url = "https://hdl.handle.net/21.15107/rcub_farfar_5491"
}