On the 20th of March, the annual scientific meeting of The Maastricht Study took place at the Maastricht UMC+. This year, duos of senior and junior researchers presented diverse topics studied within The Maastricht Study. The senior researcher introduced the topic, followed by the junior researcher, who presented a selection of their results.
Prof. David Linden, Scientific Director of the School for Mental Health and Neuroscience, opened the meeting, followed by an update of The Maastricht Study by Prof. Coen Stehouwer. Data of The Maastricht Study will be available and visible in one findable, accessible, interoperable, and reusable (FAIR) system. Furthermore, challenges in the field of data cleaning and research ethics were marked.
Dr. Kristiaan Wouters, gave an interesting lecture about his research involving immune cells in The Maastricht Study. A variability of analyse methods for immune cells was presented, followed by the differences in bidimensional and multidimensional analysis methods, which underline the importance of deep phenotyping in The Maastricht Study.
The first duo, dr. Boy Houben and Wenjie Li, presented their work in the field of microvascular dysfunction. The concept of microcirculation was defined and explained. Interestingly, they found that there was an association between prediabetes, type 2 diabetes and other indices of hyperglycaemia, and wider retinal arterioles, but a relatively weaker association with wider retinal venules. The findings of this study support the ‘ticking clock hypothesis’, which states that microvascular dysfunction precedes the clinical diagnosis of type 2 diabetes.
Prof. Bram Kroon and Tan Lai Zhou presented their work in the field of blood pressure variability (BPV). The variability of blood pressure carries prognostic information, over and above the effect of average blood pressure levels. One of their studies showed that greater very short- to mid-term BPV was associated with aortic stiffness and maladaptive carotid arterial remodeling. This may, at least partially, explain the link between BPV and cardiovascular disease, such as stroke. In another study they found that greater very short- to mid-term diastolic, and to a lesser extent, systolic BPV was associated with lower cognitive performance. These studies suggest that greater BPV is an important risk factor, and may be a potential target for prevention and treatment of cardiovascular and neurodegenerative disease.
Prof. Joop van den Bergh and Cindy Sarodnik presented their work in the field of bone quality. Different imaging technics were explained to assess bone quality. In The Maastricht Study, with the use of high quality bone imaging (high-resolution CT and DEXA scanning), they will investigate fracture risk in patients with type 2 diabetes. Preliminary results indicate that fracture incidence differs per fracture location in patients with T2DM as compared to controls.
Prof. Frans Verhey and Anouk Geraets presented their work in the field of vascular depression. The ‘vascular depression hypothesis’ posits that cerebrovascular disease may cause or contribute to late-life depression. With the use of the annual follow-up questionnaires, they were able to present one of the first longitudinal results of The Maastricht Study, which showed that markers of microvascular dysfunction in the retina and plasma are associated with the incidence of clinically relevant depressive symptoms over a four-year follow-up period.
Keynote speaker, Dr. Jeroen Lakerveld from the Amsterdam UMC, closed the Scientific Meeting of The Maastricht Study. His work in ‘upstream’ determinants of cardiovascular disease, i.e. governmental policies, environmental and lifestyle factors, highlight the importance of investigating these overlooked factors. In addition, he spoke about the Geoscience and Health Cohort Consortium (GECCO) collaboration in which large-scale Dutch cohort studies including The Maastricht Study, share their databases. This enables research on environmental determinants of lifestyle behaviors and non-communicable diseases. These big data ask for novel analysis methods, as traditional methods may not suffice.
