New measurement techniques and methodology
The classical methods for identifying exposure and health effects are considered to be expensive and slow. Therefore, RIVM invests in new techniques, models and biomarkers to measure combined exposure. For example, for measuring external exposure, there are new sensor techniques, and - for measuring individual exposure - smartphone apps and wearables, such as sensors built into watches. Citizen science can also play a part. For biomarker research, there are techniques to determine the biological effects of exposure. These include epigenetics, immunological biomarkers and new types of 'omics' such as microbiomics. Innovative use of in vitro models makes it increasingly possible to unravel underlying biological mechanisms of action in combined exposures.
To analyse the often large amounts of available and new data generated by the new measurement techniques, RIVM invests in bioinformatics and biostatistics.
Combined exposure to environmental factors (focus on the environment)
RIVM has chosen two overarching public health issues within the Exposure & Health theme. The first theme focuses on combined exposure to environmental factors, such as air pollution and its impact on our respiratory system. This is the case, for example, in specific Dutch regions such as IJmuiden (company Tata Steel), Schiphol airport, and in the vicinity of intensive livestock farming. With the aid of new techniques and models, RIVM will investigate the impact of the cumulative mix of factors and the consequences, for example, for the safety standards for these substances. RIVM aims to investigate whether and how these factors influence the microorganisms in the respiratory tract (the respiratory microbiome) and thus affect our health.
Exposure and health (focus on a healthy life course)
The second theme focuses on exposure to many factors throughout the life course and their impact on health. These factors are constantly changing. Especially during pregnancy, the first years of life and puberty, the impact of external factors and lifestyle is great. The ultimate effects of exposure and lifestyle on our health mainly become evident later in life, with premature ageing and age-related illnesses. With the increasing ageing of our society, focus on healthy ageing is essential. It is important to determine the consequences of exposure to external factors and lifestyle in good time. RIVM will investigate which new techniques can be used to better identify and predict the impacts of combined exposure and lifestyle on a healthy life course. If the most vulnerable individuals can be identified at an early stage, for example with biomarkers, preventative measures can be taken.
This research will be based on existing epidemiological research cohorts, such as the Pienter cohorts and the Doetinchem cohort. The aim is to better map the combined effect of all external factors (the exposome), such as exposure to chemical substances, lifestyle and, for example, drug use, and to identify biomarkers for health status, to identify the most vulnerable groups. This is important to be able to offer better prevention to these vulnerable groups in the future.
Within the SPR Strategic Programme RIVM theme "Exposure and health effects" RIVM is conducting the following five research projects:
RIVM produces on average two to four native applications for mobile phones each year. To ensure the quality of new apps and the corresponding data, RIVM will develop a basic version of “a generic approach and tooling for developing mobile (native) Apps”: the App Factory.
This approach leads to an improved and more uniform quality of new RIVM apps. It improves the professional look and feel of RIVM apps and contributes to users’ trust in RIVM. It will enable re-use of app functionalities in other RIVM domains, saving development and maintenance costs.
The intended result is a basic version that can be expanded with additional functionalities. These functionalities will be developed and tested in close collaboration with future operators (RIVM staff and partners) and end-users such as citizens or participants in research studies.
Researchers and policymakers can use the basic version of the App Factory for their research projects or during incidents.
This project also falls within the SPR Strategic Programme RIVM supporting theme "Collection and analysis of data".
RIVM will develop methods for managing large amounts of data (big data). This research project focuses on the use of machine learning and the analysis of data provided by Next Generation Sequencing (NGS Next Generation Sequencing) with the emphasis on data from the microbiome. In this way, AMALGAM supports three other projects of the SPR Strategic Programme RIVM theme "Exposure and Health": COMPAIR, COMPLEXA, and TRIUMPH. AMALGAM is also part of the SPR theme "Collection and analysis of data".
Due to the digitization of society, large data streams are becoming available, which might potentially contain valuable information that can be used by RIVM in the execution of its tasks. In addition, the amount of information generated within RIVM by new technologies in biology, such as Next Generation Sequencing (NGS), is increasing. RIVM needs more knowledge and experience with analysing such data.
First, RIVM will explore which statistical and machine learning methods are available. Second, we will select the most suitable method for our goals, and finally we will test this method by applying it to a data set. Where possible, we choose an application that will also be analysed in COMPAIR, COMPLEXA or TRIUMPH. For the microbiome data, which will only become available at a later stage in these projects, data from RIVM’s VEGA study (study on ESBL-producing bacteria among vegetarians and meat eaters) will be used.
This project also falls under the SPR supporting theme “Collection and analysis of data”.
RIVM is investigating why people living in the vicinity of intensive livestock farming are more likely to develop infections of the lower respiratory tract. Because they are exposed to several substances and micro-organisms at the same time, it is more difficult to determine cause and effect. Therefore, RIVM is developing new methods to determine the health effects when people are exposed to multiple sources at the same time.
Intensive livestock farming is associated with complex environmental challenges, represented by increased air concentrations of particulate matter, of chemicals such as ammonia, and of infectious microbes. Populations living near goat farms have been shown to carry an increased risk of infectious diseases. As inhalation is the major route of exposure in this case, this route will be the focus of the case study in COMPAIR.
The research makes use of existing air measurement networks and research cohorts, such as the Pienter cohorts, and of laboratory research (in vitro models). In addition, new methods are needed to analyse large amounts of complex data (in-silico models). We will develop these new methods using bioinformatics and machine learning, in collaboration with the AMALGAM project.
This project also falls under the SPR Strategic Programme RIVM supporting theme "Collection and analysis of data".
We are growing older and want to stay fit and healthy. To ensure that people can live fit and healthy lives, it is essential to know what factors promote public health and how we can stimulate healthy ageing. So far, most studies have focused on one single exposure factor. That is why RIVM will investigate which combinations of factors influence health the most.
During their lifetime, people are exposed to a multitude of risk factors, such as air pollution, noise and microorganisms. However, environmental factors may also have a positive effect on health, such as greenery in the living environment. In addition, factors such as exercise, nutrition, underlying chronic diseases and the use of medication also affect our health. Research into this subject provides a great deal of data. Advanced techniques are needed to analyse them.
With new statistical and bioinformatics techniques to analyse large amounts of data (Big Law on the professions in individual health care data), RIVM will investigate which combinations of factors have the greatest impact on our health. We will develop new biomarkers (indicators) to determine what people are exposed to over their life course, what their state of health is and whether health problems may arise in the future. This can be done with epigenetic profiling, immunological resilience and the intestinal microbiome (intestinal flora). Furthermore, more insight is needed into how 'healthy defence' and 'biological age' can be determined.
To gain insight into the significance of the microbiome for human health, RIVM will compare the microbiomes of sick and healthy people. The microbiome comprises all micro-organisms that are naturally present on and in the human body, for example, in the intestines and the respiratory tract. This project will describe the characteristics of a healthy microbiome and the factors (epidemiological, clinical and ecological) that influence it with regard to the Dutch population.
In recent years it has become clear that the microbiome has a major impact on our health because it supports certain functions of the body. For example, it helps to absorb and digest nutrients and to protect the body against pathogens. It also provides a stable immune system. The microbiome probably plays an important role in the effect of external factors on health. Insight into the status of the microbiome can, therefore, indicate how healthy a person is.
The project will use samples of the national population-based sero-surveillance study conducted in the Netherlands in 2016/2017 ‘PIENTER3’. Laboratory analyses will be conducted, and the results will be epidemiologically analysed using bioinformatics techniques. This way, a knowledge platform will be established, for laboratory analyses, bioinformatics and down-stream data integration within RIVM. Besides the development of a harmonised methodology, TRIuMPH will generate respiratory and gut microbiome data as input for the COMPAiR and COMPLEXA projects, where further multidimensional data integration will be carried out in relation to combined environmental exposures and healthy ageing/lifestyle in close collaboration with the statistical methodological project AMALGAM.