Summary
Intracranial aneurysms usually go undetected until rupture occurs leading to aneurysmal subarachnoid hemorrhage (ASAH), a type of stroke with devastating effects. Early detection and preventive treatment of aneurysms fall short as we do not know who is it at risk and why, as we have insufficient insight in the contribution and interplay of genetic, environmental and intermediate phenotypic risk factors. Given the rarity of the disease, there is a paucity of large and rich cohorts to study risk factors separately with sufficient power. To add to the problem, my preliminary findings suggest disease heterogeneity with subgroup specific risk factors for aneurysms. The sex-related heterogeneity is most eminent in the disease with 2/3 of patients being women. I aim to advance disease understanding to allow early recognition of intracranial aneurysms to prevent ASAH.
I have established a new conceptual approach that integrates genetic and environmental risk factors with imaging markers as intermediate phenotypes for genetic factors. With data reduction and machine-learning approaches I will for the first time address disease heterogeneity and aneurysm risk with adequate power. I will develop and validate a tool to automatically detect new imaging markers predicting aneurysm development applying feature-learning models. Next I will elucidate the genetic basis underlying differential imaging risk patterns (imaging genetic factors). I will apply a new hypothesis-free strategy to detect and validate yet unknown environmental risk factors predicting aneurysm presence. I will assess the contribution to disease of all factors detected according to sex. All risk factors will be combined in an aneurysm prediction risk model to understand relative contribution of different risk factors in different subgroups. It will advance disease understanding and individualized risk prediction of aneurysms leading to precision medicine in early aneurysm detection to reduce the burden of ASAH.
I have established a new conceptual approach that integrates genetic and environmental risk factors with imaging markers as intermediate phenotypes for genetic factors. With data reduction and machine-learning approaches I will for the first time address disease heterogeneity and aneurysm risk with adequate power. I will develop and validate a tool to automatically detect new imaging markers predicting aneurysm development applying feature-learning models. Next I will elucidate the genetic basis underlying differential imaging risk patterns (imaging genetic factors). I will apply a new hypothesis-free strategy to detect and validate yet unknown environmental risk factors predicting aneurysm presence. I will assess the contribution to disease of all factors detected according to sex. All risk factors will be combined in an aneurysm prediction risk model to understand relative contribution of different risk factors in different subgroups. It will advance disease understanding and individualized risk prediction of aneurysms leading to precision medicine in early aneurysm detection to reduce the burden of ASAH.
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/852173 |
| Start date: | 01-02-2020 |
| End date: | 31-01-2025 |
| Total budget - Public funding: | 1 499 108,00 Euro - 1 499 108,00 Euro |
Cordis data
Original description
Intracranial aneurysms usually go undetected until rupture occurs leading to aneurysmal subarachnoid hemorrhage (ASAH), a type of stroke with devastating effects. Early detection and preventive treatment of aneurysms fall short as we do not know who is it at risk and why, as we have insufficient insight in the contribution and interplay of genetic, environmental and intermediate phenotypic risk factors. Given the rarity of the disease, there is a paucity of large and rich cohorts to study risk factors separately with sufficient power. To add to the problem, my preliminary findings suggest disease heterogeneity with subgroup specific risk factors for aneurysms. The sex-related heterogeneity is most eminent in the disease with 2/3 of patients being women. I aim to advance disease understanding to allow early recognition of intracranial aneurysms to prevent ASAH.I have established a new conceptual approach that integrates genetic and environmental risk factors with imaging markers as intermediate phenotypes for genetic factors. With data reduction and machine-learning approaches I will for the first time address disease heterogeneity and aneurysm risk with adequate power. I will develop and validate a tool to automatically detect new imaging markers predicting aneurysm development applying feature-learning models. Next I will elucidate the genetic basis underlying differential imaging risk patterns (imaging genetic factors). I will apply a new hypothesis-free strategy to detect and validate yet unknown environmental risk factors predicting aneurysm presence. I will assess the contribution to disease of all factors detected according to sex. All risk factors will be combined in an aneurysm prediction risk model to understand relative contribution of different risk factors in different subgroups. It will advance disease understanding and individualized risk prediction of aneurysms leading to precision medicine in early aneurysm detection to reduce the burden of ASAH.
Status
SIGNEDCall topic
ERC-2019-STGUpdate Date
27-04-2024
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