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Your search keyword '"Georgakis, Marios K."' showing total 13 results
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13 results on '"Georgakis, Marios K."'

7. Modifiable Lifestyle Factors and Risk of Stroke

Author

Harshfield, Eric L, Georgakis, Marios K, Malik, Rainer, Dichgans, Martin, Markus, Hugh S, Harshfield, Eric [0000-0001-8767-0928], Markus, Hugh [0000-0002-9794-5996], and Apollo - University of Cambridge Repository

Subjects
Risk, Gerontology, medicine.medical_specialty, Mendelian randomization analysis, body mass index, Health Promotion, Polymorphism, Single Nucleotide, educational status, Brain Ischemia, Stroke risk, 03 medical and health sciences, 0302 clinical medicine, Risk Factors, Internal medicine, Mendelian randomization, Epidemiology, Genetic predisposition, Humans, Medicine, genetics, Genetic Predisposition to Disease, ddc:610, Life Style, Stroke, 030304 developmental biology, 2. Zero hunger, Advanced and Specialized Nursing, 0303 health sciences, business.industry, Public health, Smoking, 030305 genetics & heredity, Mendelian Randomization Analysis, Anthropometry, medicine.disease, diagnosis [Stroke], stroke, Causality, 3. Good health, Lifestyle factors, Neurology (clinical), Cardiology and Cardiovascular Medicine, business, Body mass index, epidemiology [Stroke], 030217 neurology & neurosurgery, prevention & control [Stroke], Genome-Wide Association Study
Abstract

AimsAssessing whether modifiable risk factors are causally associated with reduced stroke risk is important in planning public health measures, but determining causality can be difficult in epidemiological data. Leveraging large-scale genetic data in a technique known as Mendelian randomisation, we aimed to determine whether modifiable lifestyle factors including educational attainment, smoking, and body mass index are causal risk factors for ischaemic stroke and its different subtypes and haemorrhagic stroke.Methods and ResultsWe performed two-sample and multivariable Mendelian randomization to assess the causal effect of twelve lifestyle factors on risk of stroke and whether these effects are independent. We found genetic predisposition to increased number of years of education to be inversely associated with ischaemic, large-artery, and small-vessel stroke, as well as with intracerebral haemorrhage. Genetic predisposition to ever smoking regularly, higher body mass index (BMI), and higher waist-hip ratio are also associated with ischaemic and large-artery stroke. Additionally, we found that the effects of education, BMI, and smoking on ischaemic stroke to be independent of each other.ConclusionGenetic predisposition to higher educational attainment can reduce the risk of ischaemic, large-artery, and small-vessel stroke, while genetic predisposition to smoking and higher anthropometry measures can increase the risk of these stroke subtypes. This suggests that lifestyle modification addressing these risk factors will reduce stroke risk.

Published
2021

8. A Mendelian Randomization Analysis

Author

Harshfield, Eric L., Georgakis, Marios K., Malik, Rainer, Dichgans, Martin, and Markus, Hugh S.

Subjects
Risk, Original Contributions, Mendelian randomization analysis, body mass index, Health Promotion, stroke, Polymorphism, Single Nucleotide, smoking, educational status, Brain Ischemia, Clinical and Population Sciences, Risk Factors, ComputingMethodologies_DOCUMENTANDTEXTPROCESSING, Humans, genetics, Genetic Predisposition to Disease, Life Style, Genome-Wide Association Study
Abstract

Supplemental Digital Content is available in the text., Background and Purpose: Assessing whether modifiable risk factors are causally associated with stroke risk is important in planning public health measures, but determining causality can be difficult in epidemiological data. We evaluated whether modifiable lifestyle factors including educational attainment, smoking, and body mass index are causal risk factors for ischemic stroke and its subtypes and hemorrhagic stroke. Methods: We performed 2-sample and multivariable Mendelian randomization to assess the causal effect of 12 lifestyle factors on risk of stroke and whether these effects are independent. Results: Genetically predicted years of education was inversely associated with ischemic, large artery, and small vessel stroke, and intracerebral hemorrhage. Genetically predicted smoking, body mass index, and waist-hip ratio were associated with ischemic and large artery stroke. The effects of education, body mass index, and smoking on ischemic stroke were independent. Conclusions: Our findings support the hypothesis that reduced education and increased smoking and obesity increase risk of ischemic, large artery, and small vessel stroke, suggesting that lifestyle modifications addressing these risk factors will reduce stroke risk.

Published
2021

11. Abstract P771: Monocyte-Chemoattractant Protein-1 Levels in Human Carotid Atherosclerosis Associate With Hallmarks of Plaque Vulnerability

Author

Georgakis, Marios K, primary, van der Laan, Sander W, additional, Asare, Yaw, additional, Mekke, Joost M, additional, Haitjema, Saskia, additional, Schoneveld, Arjan, additional, de Kleijn, Dominique, additional, de Borst, Gert Jan, additional, Pasterkamp, Gerard, additional, and Dichgans, Martin, additional

Published
2021
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13. Texture Features of Magnetic Resonance Images Predict Poststroke Cognitive Impairment: Validation in a Multicenter Study.

Author

Betrouni N, Jiang J, Duering M, Georgakis MK, Oestreich L, Sachdev PS, O'Sullivan M, Wright P, Lo JW, and Bordet R

Subjects
Humans, Reproducibility of Results, Image Processing, Computer-Assisted methods, Machine Learning, Magnetic Resonance Imaging methods, Cognitive Dysfunction diagnostic imaging, Cognitive Dysfunction etiology
Abstract

Background: Imaging features derived from T1-weighted (T1w) images texture analysis were shown to be potential markers of poststroke cognitive impairment, with better sensitivity than atrophy measurement. However, in magnetic resonance images, the signal distribution is subject to variations and can limit transferability of the method between centers. This study examined the reliability of texture features against imaging settings using data from different centers., Methods: Data were collected from 327 patients within the Stroke and Cognition Consortium from centers in France, Germany, Australia, and the United Kingdom. T1w images were preprocessed to normalize the signal intensities and then texture features, including first- and second-order statistics, were measured in the hippocampus and the entorhinal cortex. Differences between the data led to the use of 2 methods of analysis. First, a machine learning modeling, using random forest, was used to build a poststroke cognitive impairment prediction model using one dataset and this was validated on another dataset as external unseen data. Second, the predictive ability of the texture features was examined in the 2 remaining datasets by ANCOVA with false discovery rate correction for multiple comparisons., Results: The prediction model had a mean accuracy of 90% for individual classification of patients in the learning base while for the validation base it was ≈ 77%. ANCOVA showed significant differences, in all datasets, for the kurtosis and inverse difference moment texture features when measured in patients with cognitive impairment and those without., Conclusions: These results suggest that texture features obtained from routine clinical MR images are robust early predictors of poststroke cognitive impairment and can be combined with other demographic and clinical predictors to build an accurate prediction model.

Published
2022
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