About our research group/lab
Cardiovascular risk prediction and stratificationPrevention of cardiovascular disease and diabetes remains feasible yet suboptimal. The common approach in primary prevention is to identify individuals at high enough risk for cardiovascular events to justify targeting them for more intensive lifestyle and/or interventions. This line focuses on implications of prevention guidelines at population level and also investigates whether newer risk markers for cardiovascular and diabetes risk prediction and stratification could improve the risk predictions beyond the risk prediction algorithms that form the basis for the current guidelines, and at a population level. Outcomes under study include coronary heart disease, heart failure, atrial fibrillation, and diabetes mellitus.
Cardiovascular imagingTo improve our understating of the atherosclerosis process and, ultimately, develop more efficient primary prevention strategies for cardiovascular disease, studying atherosclerosis process across its development is of particular importance. This line focuses on non-invasive imaging of atherosclerosis across its spectrum; including ultrasound assessment of carotid arteries; measures of arterial stiffness; computed tomography (CT) scan of aortic valve and arch, coronary and extra- and intra-cranial carotid arteries; magnetic resonance imaging (MRI) of carotid plaque components. This approach will enable us to identify the risk factors implicated in disease initiation and progression in its subclinical phase and before the abrupt clinical onset of the disease.
Cardiovascular omics & Big dataGenomic, metabolomics, and proteomic approaches promise to revolutionize our understanding of cardiovascular disease initiation and progression. This improved appreciation of pathophysiology could eventually be translated into avenues of clinical utility. Omics-based cardiovascular prediction carries the potential to refine diagnostic sub-classifications and improve risk assessment tools which, in turn, allow for earlier and more targeted intervention. Integration of clinical information, stable and dynamic omics, and molecular phenotyping serves to pave the way for personalized medicine. To this end and to translate this into useful applications for improved diagnosis, prediction, and prognostication, bioinformatics will play a crucial role.
Sex- and Gender-specific aspects of cardiovascular diseaseThere is a substantial difference in the burden of different forms of cardiovascular disease between women and men. Despite the growing recognition regarding the equal importance of cardiovascular disease for both men and women and the need for tailored prevention and intervention strategies, our knowledge of sex- and gender-specific markers remains limited. This line focuses on improving our understanding of sex- and gender- specific features of atherosclerosis, which would in turn translate to devising more efficient prediction schemes and prevention strategies.
Complete overview can be found at:
10 Selected publications for the past 3 years:
Lifetime risk and multimorbidity of non-communicable diseases and disease-free life expectancy in the general population: A population-based cohort study.
PLoS Medicine. 2019;16:e1002741.
Chronic obstructive pulmonary disease and the development of atrial fibrillation.
International Journal of Cardiology. 2019; 276: 118-124.
GWAS and colocalization analyses implicate carotid intima-media thickness and carotid plaque loci in cardiovascular outcomes.
Nature Communications. 2018; 9: 5141.
Multi-ethnic genome-wide association study of atrial fibrillation.
Nature genetics. 2018; 50: 1225-33.
Thyroid Function and the Risk of Atherosclerotic Cardiovascular Morbidity and Mortality: The Rotterdam Study.
Circulation Research. 2017;121:1392-400.
Epicardial Fat Volume and the Risk of Atrial Fibrillation in the General Population Free of Cardiovascular Disease.
JACC Cardiovascular Imaging. 2017; 10: 1405-1407.
Prevalence and prognostic implications of coronary artery calcification in low-risk women: a meta-analysis.
JAMA. 2016; 316: 2126-2134.
Association of age at onset of menopause and time since onset of menopause with cardiovascular outcomes, intermediate vascular traits, and all-cause mortality: a systematic review and meta-analysis.
JAMA Cardiology. 2016.
Sex steroids, sex hormone-binding globulin and cardiovascular health in men and postmenopausal women: the Rotterdam Study. T
he Journal of clinical endocrinology and metabolism. 2016; 101: 2844-2852.
Multiethnic Exome-Wide Association Study of Subclinical Atherosclerosis.
Circulation Cardiovascular Genetics. 2016; 9: 511-520.
Lifetime risk of developing impaired glucose metabolism and eventual progression from prediabetes to type 2 diabetes: a prospective cohort study.
Lancet Diabetes & Endocrinology. 2016; 4: 44-51.
Department of Radiology
Department of Cardiology
Department of Internal Medicine
Genetic Laboratory of Erasmus MCDepartment of Gynecology
Funding & Grants
‘Stratification of Obese Phenotypes to Optimize Future Obesity Therapy (SOPHIA)’
European Commission grant
‘Dynamic longitudinal exposome trajectories in cardiovascular and metabolic non-communicable diseases (LONGITOOLS)’
Erasmus MC (Mrace) grant
‘Towards a novel immunothrombosis signature for natural course of atrial fibrillation among women and men’
The Netherlands Organization for Health Research and Development (ZonMw) Implementation grant
‘Implementatie leidraad microvasculair coronairlijden in de
‘Global cardiometabolic risk profile and atrial fibrillation among women and men from general population’
‘Global cardiometabolic risk profile in diabetes; a gender specific approach’
The Netherlands Organization for Health Research and Development (ZonMw) Gender and Health grant
The Netherlands Organization for Health Research and Development (ZonMw) Veni Grant
‘Sex differences in the risk factor profile for atherosclerosis in various vessels’
National Institute of health (NIH) grant
‘Metabolomic signature of coronary artery disease associated phenotypes’
European Commission grant
H2020 (H2020-ICT-2016-2017) ‘Big data for medical analytics (BigMedalytics)
Principle Investigator:Maryam Kavousi, Dr. email@example.com
Management Assistant:Mirjam Roosen-Niesing, Ms. firstname.lastname@example.org
Postdoctoral scientists:Fariba Ahmadizar, Dr. email@example.com
Maxime Bos, Dr. firstname.lastname@example.org
PhD students:Elif Aribas, Dr. email@example.com
Hoyan Wen, Dr. firstname.lastname@example.org
Cindy Meun, Dr. email@example.com
Banafsheh Arshi, Drs. firstname.lastname@example.org
Sven Geurts, Dr. email@example.com
Janine van der Toorn, Drs. firstname.lastname@example.org
Marlou Limpens, Drs. email@example.com
Martijn Tilly, Dr. firstname.lastname@example.org
Zuolin Lu, Drs. email@example.com
Kan Wang, Drs. firstname.lastname@example.org
Fang Zhu, Drs. email@example.com
Master Students:Angelo Pezzullo