Value of genomics for atherosclerotic cardiovascular disease risk prediction
Date
2019-05-03
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Abstract
Fakt, et südame-veresoonkonnahaigused on peamiseks suremuse põhjustajaks maailmas, rõhutab vajadust edendada ja täiustada olemasolevaid haiguse ennetus- ja ennustusstrateegiaid. Südame-veresoonkonnahaiguste riski hindamine põhineb tänases kliinilises praktikas klassikalisi fenotüübilisi riskitegureid arvestavatel riski hindamise mudelitel. Kuigi nimetatud strateegia võimaldab kõrge riskiga indiviide suhteliselt hästi tuvastada, jääb pea kolmandiku riski hinnang ebatäpseks ning ravimääramine ebaselgeks. Lisaks eelnevale peegeldub mudelite piiratud kasutus selles, et riskifaktorite loetlemisega hinnatakse tegelikkuses molekulaarsel tasandil juba toimunud muutusi. Seega leevendatakse praeguse strateegia kasutamisel pigem patoloogia progresseerunud kulgu, kui pärsitakse või ennetatakse molekulaarsete mehhanismide häirumist varases staadiumis. Üheks võimalikuks edasiarenduse meetmeks pakutakse haiguse geneetilise informatsiooni arvestamist. Seda eeskätt seetõttu, et südame-veresoonkonnahaiguste geneetiliste seoste uuringutega on täna jõutud hinnanguteni, millel on potentsiaali muuta oluliselt täpsemaks nii tervete indiviidide varast haigusriski hindamist kui ka haigete kliinilist käsitlust. Selle doktoritöö peamiseks eesmärgiks on anda ülevaade tänastest südame-veresoonkonnahaiguste riski hindamise meetmetest ning sellest, kas ja kuidas geneetilise informatsiooni kaasamine igapäeva kliinilistesse otsustesse neid edendada võiks. Lisaks toon näiteid, kuidas kõrge resolutsiooniga genoomi järjestusandmestik võimaldaks tunnusega seotud põhjuslikke geenivariante täpsemini tuvastada ning kuidas populatsiooni-põhise biopanga andmete kasutamine tõhustaks kõrge riskiga indiviidide kliinilist käsitlust.
Cardiovascular diseases are the main cause of morbidity and mortality worldwide, underscoring the requisite for improved strategies for disease prevention and risk prediction. The main approach applied in today's clinical practice to identify those at increased cardiovascular risk relies on the utilization of phenotypic risk models that facilitate the estimation of one's disease risk based on traditional risk factors. While this strategy is beneficial for avoiding disease incidence and it does on the whole target individuals at high risk for treatment sufficiently well, a third of individuals, who experience an adverse event, are misclassified into a lower risk category and are therefore advocated treatment ambiguously. Importantly, the current approach lacks in providing accurate estimation for primordial prevention, that is estimating risk before risk factors emerge. To overcome this issue and seek for approaches to enhance risk estimation, attention has now been turned to genetics with the aim of incorporating genetic information into established risk prediction strategies. The scrutiny of the genetic architecture of cardiovascular diseases conducted in recent decades has today resulted in estimates that can be of clinical utility and value. This doctoral thesis aims to give an overview of the status quo of the genomic research on cardiovascular diseases and contemplate on what the advances in molecular technology, computational capacities and large-scale initiatives have enabled, what the progress of these endeavours entail and whether these do bestow incremental value for clinical utility. Furthermore, I will bring examples of how the utilization of high-coverage sequencing data can enhance the search for the genetic underpinnings of cardiovascular disease-associated phenotypes, and how the use of large-scale cohorts and population-based biobanks can enable the anticipated improvement in disease risk estimation, especially when integrated into a national healthcare system.
Cardiovascular diseases are the main cause of morbidity and mortality worldwide, underscoring the requisite for improved strategies for disease prevention and risk prediction. The main approach applied in today's clinical practice to identify those at increased cardiovascular risk relies on the utilization of phenotypic risk models that facilitate the estimation of one's disease risk based on traditional risk factors. While this strategy is beneficial for avoiding disease incidence and it does on the whole target individuals at high risk for treatment sufficiently well, a third of individuals, who experience an adverse event, are misclassified into a lower risk category and are therefore advocated treatment ambiguously. Importantly, the current approach lacks in providing accurate estimation for primordial prevention, that is estimating risk before risk factors emerge. To overcome this issue and seek for approaches to enhance risk estimation, attention has now been turned to genetics with the aim of incorporating genetic information into established risk prediction strategies. The scrutiny of the genetic architecture of cardiovascular diseases conducted in recent decades has today resulted in estimates that can be of clinical utility and value. This doctoral thesis aims to give an overview of the status quo of the genomic research on cardiovascular diseases and contemplate on what the advances in molecular technology, computational capacities and large-scale initiatives have enabled, what the progress of these endeavours entail and whether these do bestow incremental value for clinical utility. Furthermore, I will bring examples of how the utilization of high-coverage sequencing data can enhance the search for the genetic underpinnings of cardiovascular disease-associated phenotypes, and how the use of large-scale cohorts and population-based biobanks can enable the anticipated improvement in disease risk estimation, especially when integrated into a national healthcare system.
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Keywords
südame-veresoonkonna haigused, riskitegurid, pärilik eelsoodumus, geneetiline informatsioon, kardiovaskulaarne risk, riskianalüüs