NCCNederlands Cohort Consortium (NCC)


Dr. Miranda Schram
MUMC+, Randwycksingel 35, 6229 EG Maastricht
Amsterdam UMC, Erasmus MC, LUMC, MUMC, Maastricht University, RIVM, UMC Groningen, UMC Utrecht, Vrije Universiteit and VUMC.

The Netherlands Cohorts Consortium (NCC) combines forces among Dutch population-representative cohort studies enabling an important contribution to Preventive Medicine. The transition to prevention is urgently needed in our changing society to lower healthcare costs and disease burden. NCC provides the necessary scaling-up to study multimorbidity in the Dutch context. Deep-phenotyping data, both clinical and biomedical, will become more easily available among >500.000 individuals, which facilitates (epidemiological) research into the prevention, etiology and course of chronic non-communicable diseases, such as type 2 diabetes, cardiovascular disease, dementia, COPD, cancer and depression. NCC aims to provide an infrastructure in which the extremely detailed data on health can be shared in a FAIR and federated manner, increasing the possibilities of new technical developments e.g. Artificial Intelligence (AI) and machine learning, to provide new insights into the development of chronic disease and multimorbidity.

NCC joins the forces of the large Dutch cohort studies. The Netherlands has an outstanding reputation in human population-representative cohort studies. There is ample experience, a high degree of organizational power and, importantly, a relatively low degree of attrition partly because the mobility amongst participants is quite low. Included cohort studies are De Maastricht Studie, Doetinchem cohort studie, Erasmus Rotterdam Gezondheid Onderzoek (Rotterdam Study), LASA, Leidsche Rijn Gezondheidsproject, EPIC-NL, NTR, Leiden Longevity Study, Helius, Lifelines, and the NEO study, thus forming a national representative sample. We aim to include all population-representative Dutch cohort studies that provide advanced longitudinal clinical phenotyping and biomedical data. This consortium comprises a diversity of data concerning mental health, physical health, social networks, stress, lifestyle, environment, socio-demographic and economic factors. The consortium will translate results from advanced clinical phenotyping on the early stages of chronic diseases into practical solutions to improve population health. In addition, this information will be combined with available omics, genetic and imaging data. Thereby, the NCC fills in the gap between advanced, innovative, clinical, social and social-economical phenotyping and omics, genetics and imaging phenotyping.

NCC includes cohort studies that have decades of experience in collecting advanced phenotypic data, the translation of these data into high impact scientific work, and in sharing data through for example data catalogues, linkage of data to existing registries and federated data analysis. All cohorts are part of a national consortium, such as the Netherlands Consortium of Dementia Cohorts, the PSY-CA consortium or the GECCO consortium (aiming at linking environmental data), in which datalinkage and FAIR data sharing are important work packages. Many cohorts already linked their dataset to PALGA, NKR, NIVEL, CBS, SFK. NCC provides cohorts with support on linkage through TTP's, which is very important to protect the privacy of the participants and patients providing the data.
The enormous high-quality data already available, and linking data of several cohorts, also in combination with registries, increases important new rapidly developing analysing techniques, such as AI. In NCC the expertise on analysing big data can be shared, strengthening research and preventive medicine even further.

Types of research
Longitudinal population-representative cohort studies are primarily designed to study primary prevention at a population level, as this can hardly be studied by use of clinical trials or experimental designs. A key objective of NCC is to study modifiable disease risk factors, and their potential for prevention of chronic diseases and multimorbidity. With this approach we aim to provide the scientific evidence that can improve population health and stop the growing occurrence of (preventable) chronic diseases.

AI is ideally suited to further unlock the rich data sources that are available from the cohort studies, and to find novel biomarkers for chronic disease. Moreover, the development of AI as an aid to clinical treatments is still in its infancy, therefore, making use of already available advanced data from cohort studies can amplify the contribution of AI to improve medical diagnosis. In addition, many clinical health care records and questionnaires include open text fields, AI applications for text mining can further unlock these data. This will enhance the progress in (preventive and diagnostic) medicine, and fits the governmental policy to assign AI and machine learning as a top priority.

Aansluiting bij strategische ontwikkelingen
Life Sciences & Health
Personalised medicine: uitgaan van het individu
Gezondheidszorgonderzoek, preventie en behandeling
NeuroLabNL: dè werkplaats voor hersen-, cognitie- en gedragsonderzoek
Waardecreatie door verantwoorde toegang tot en gebruik van big data
Kwaliteit van de omgeving
Sport en Bewegen