To develop, present, and discuss methodology for life course epidemiology. Life course epidemiology can be defined as the study of long-term effects on chronic disease risk of exposures during gestation, childhood, adolescence, young adulthood and later adult life. In epidemiological practice this means longitudinal follow-up research. Since ageing starts at fertilization, these studies should not be limited to specific age groups (e.g. the elderly) alone, but instead should have include or combine cohort studies with different age groups.
The type of methodology should be suitable or applicable to cohort studies like LifeLines to be able to understand the association of changes in risk factors and disease outcome. LifeLines is a prospective population-based study among ultimately 165.000 inhabitants from the three northern provinces of the Netherlands, using a three-generation family design. The overall aim of LifeLines is to unravel the interaction between genetic and environmental factors in the development of multifactorial diseases over the life course.
Description of the Programme
Traditional epidemiology typically focuses on the association of risk factors and exposures with disease outcome, using cross-sectional, case-control, or cohort studies, without specifically addressing the dynamic character of changes in risk factors and exposures over a life time of a person.
LCE wants to assess the relationship between changes in risk factors over time and disease outcomes. In epidemiological practice this can be best studied with longitudinal follow-up research. Since ageing starts at fertilization, these studies should not be limited to adults or even the elderly, where most disease obviously occurs.
A cornerstone of LCE’s research concerns LifeLines, which is a prospective population-based study among ultimately 165.000 inhabitants from the three northern provinces of the Netherlands, using a three-generation family design. The overall aim of LifeLines is to unravel the interaction between genetic and environmental factors in the development of multifactorial diseases over the life course. Besides LifeLines, the LCE program contains the vast long lasting experience of population cohort studies at the UMCG, ranging from the oldest population study in the Netherlands (Vlagtwedde-Vlaardingen study) to the largest (LifeLines). Other important cohort studies included in LCE are TRAILS, Prevend, and GECKO.
This program focuses on one technique in epidemiology to unravel healthy ageing, i.e. longitudinal observational cohort studies. The common interest and expertise concerns the methodology to analyze data from these, often large-scale, datasets. Medical statistics as well as genetic epidemiology, both important methodological disciplines, are included in LCE. Research themes include
- Methodological approaches for longitudinal data analysis of cohort studies
- Etiology and prognosis of chronic age-related diseases
The research is conducted in multidisciplinary collaboration of methodological experts within LCE and disease-specific clinical experts. Members of the program are primarily based in the Department of Epidemiology, ICPE and Public Health. The researchers have a strong common interest in both research themes and aims.
Statistical methodology focuses on longitudinal data analysis of cohort studies, in particular on generalized, non-linear, and linear mixed effects models. This type of models entail a large set of statistical models that could determine the association of risk factors with longitudinal disease outcomes and describe the dynamics of risk factors and health outcome simultaneously. It would include variance component models, latent variable models in its widest form, non-linear growth curves, and many other types of models. The family of models could also deal with different types of health outcomes (continuous, ordinal, binary). Furthermore, the program would also focus on multivariate type of analysis to be able to analyze the longitudinal data in risk factors and health outcomes. This class of methods would include structural equation modeling, causal search inference, and other pathway analysis.