6.2.1 The origins of LIFEPATH
LIFEPATH came into being thanks to two previous projects, whose methods and findings contributed to its development. The first ‘ancestor’ of LIFEPATH was the project called EnviroGenomarkers. That project, funded by the European Union, investigated the effects of environmental exposures on the development of various diseases. The main goal was to measure the effects of environmental agents on diseases through the evolution of biomarkers. Two underlying ideas were crucial in the EnviroGenomarkers project.
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To begin with, to investigate the development of diseases, scientists considered the totality of environmental exposures affecting individuals during their lifetime. Such a totality, known by the name of ‘exposome’12 (Wild, 2012), comprises both ‘internal’ and
‘external’ exposures. For instance, some metabolites contained in our biological fluids can act as ‘internal’ exposures, affecting inflammation, oxidative stress and various metabolic pathways. These metabolites, furthermore, can be affected by some ‘external’ environmental exposures, like radiation.
In addition, the project EnviroGenomarkers was founded upon the concept of biomarkers produced through ‘-omic’ technologies. The nature of biomarkers will be discussed in detail in section 6.4, for the moment it is sufficient to know what ‘-omic’ technologies are, and what the term ‘biomarker’ represents. In EnviroGenomarkers, ‘-omic’ technologies are described as promising technologies able to find the missing links between exposome and disease. The main feature of ‘-omic’ technologies is their capacity to analyse vast sets of biological molecules, rather than a single biological structure (like a protein or gene). The emerging methods of measuring families of cellular molecules are known by the names of ‘genomics’, ‘transcriptomics’ (gene expression profiling), ‘proteomics’ and ‘metabolomics’.
In biology, the term ‘genome’ is used to refer to the whole hereditary information encoded in the DNA (or, for some viruses, in the RNA). To be more precise, the genome of an organism is the DNA sequence of one set of chromosomes. The study of an organism’s genome through ‘-omic’ technologies, aimed at understanding its complex function, is called genomics.
Transcriptomics is the application of evolving technologies to scan the fifty thousand genes that we currently know are transcribed into RNA molecules from the three-billion- letter human genome. Such mRNA transcripts in the cell reflect the genes that are actively expressed at any given time points. Measuring the expression of an organism’s genes at different time points, and in diverse tissues and conditions helps scientists to understand how genes are regulated, and how certain developmental pathways such as environmental stimuli responses are triggered (Debnath et al., 2010).
12 The concept was developed in parallel with the notion of ‘genome’, a term used to characterise the genetic
material of an organism. The ‘exposome’ complements the genome by addressing both genetic and non- genetic exposures.
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Proteomics indicates the analysis of gene products, proteins, on a large scale. In general, proteomics allows scientists to study protein expression profiles, protein modifications and protein networks in relation to cell function and biological processes (Macaulay et al., 2005).
Finally, metabolome refers to the complete set of small-molecule metabolites (such as metabolic intermediates, hormones and other signalling molecules, and secondary metabolites) that are found within a biological sample, such as a single organism (Debnath et al., 2010). Metabolomics, based on ‘-omic’ technologies, aims to comprehensively study the metabolome of cells, body fluids and tissues.
In EnviroGenomarkers, the major contribution of ‘-omic’ technologies was through the identification of relevant biomarkers. Biomarkers are measures of elements or characteristics of the environment and the organism that help to understand biological processes. In a first step, ‘-omic’ technologies allow researchers to collect vast amounts of data on particular molecules (like DNA, metabolites) in individuals with specific health conditions. The dataset is then analysed to determine whether it is possible to obtain biomarkers, that in turn can be used in different ways: some biomarkers help to diagnose accurately the health disorder of the patient; other biomarkers are used to trace the course of a specific disease; another group of biomarkers is studied to obtain information about the risk of developing diseases. Some of these biomarkers, together, help scientists to cast light on causal processes starting with certain environmental exposures and ending in the emergence of diseases.
The more recent ‘antecedent’ of LIFEPATH is the project EXPOsOMICS, whose main goal and methods were very similar to those of EnviroGenomarkers. EXPOsOMICS, based on the study of biomarkers and on the concept of exposome, acted as a bridge between EnviroGenomarkers, focused solely on the effects of environmental exposures, and LIFEPATH, interested in the causal link between socio-economic conditions and diseases. It was especially during this project, indeed, that scientists began to consider socio-economic factors as causally relevant to health conditions. As an example, in the article published by Vineis et al. (2017), EXPOsOMICS researchers stated that:
“It is generally accepted that the majority of important chronic diseases are likely to result from the combination of environmental exposures to chemical and physical stressors and human genetics. There is also evidence that the
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effects are location-specific and influenced by climatic, lifestyle and socio- economic characteristics.” (Vineis, Chadeau-Hyam, et al., 2017, p. 143)
If we consider EnviroGenomarkers, EXPOsOMICS and LIFEPATH, the hypothesis that health effects are caused by socio-economic characteristics was developed when scientists were conducting EXPOsOMICS, but was not tested in that project. Scientists in EXPOsOMICS focused their attention on the causal relationships between environmental exposures and diseases. While some exposures were thought to be caused by certain socio-economic conditions, researchers in EXPOsOMICS did not use socio- economic indicators: the EXPOsOMICS datasets were made only of biological and exposure data (such as measures of chemicals in water or air). The causal relationship between socio-economic characteristics and health became then the heart of LIFEPATH, as we will see in the next section.
6.2.2 The LIFEPATH project
LIFEPATH is a big data project funded by the European Community and devoted to the investigation of the causal relationships between socio-economic factors and the development of diseases. Over the last few decades, many researchers have collected evidence that individuals from more disadvantaged socio-economic positions have higher mortality rates than people of the same age from higher socio-economic positions (Chetty et al., 2016; House et al., 2005; Mackenbach et al., 2008). Furthermore, studies have shown that such people are more likely to suffer from diseases and disabilities in their life. While the associations between socio-economic factors and health are often discussed within social epidemiology, the paths through which psychosocial and economic experiences trigger biological causes that ultimately influence health are still underdeveloped if compared to the biological pathways (Adler & Ostrove, 1999). The aim of LIFEPATH is to fill this gap by illuminating how social, environmental and economic conditions (like wealth, education, job and housing) may lead to short and long- term health effects. In order to do this, researchers in LIFEPATH use both statistical analyses, exploring correlations between certain socio-economic factors and some diseases (see d’Errico et al., 2017), and mechanistic studies aimed at finding the intermediate mechanisms and pathways through which socio-economic conditions influence organic parameters (see Vineis, Avendano-Pabon, et al., 2017). As reconstructed by Vineis, Illari and Russo (2017, p. 5), only when researchers confirm the
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presence both of a difference-making relationship and of a mechanistic relationship between certain socio-economic factors and health conditions, causation is established. To establish correlations and mechanisms, furthermore, vast amounts of data concerning socio-economic factors and health conditions are required.
Many of these data are analysed to find relevant correlations, and to identify intermediating markers that might allow researchers to recognise the processes linking socio-economic factors and biological changes. The aim of LIFEPATH is to trace the processes both at the social and biological levels, however social and biological factors are explored in different ways. On the one hand, the roles played by social factors, as we will see, are explored by using only the traditional socio-economic position indicators. On the other hand, the processes at the biological level are explored by means of biomarkers. It follows that, while the social components are analysed at a rather coarse- grained level, the biological components of the mechanisms of health are explored at a fine-grained level.
To identify biomarkers, scientists working in LIFEPATH follow the same approach used in EnviroGenomarkers and EXPOsOMICS: to begin with, they obtain new data by means of ‘-omic’ technologies adopting a holistic view of the molecules that make up cells, tissues or organisms (Horgan & Kenny, 2011). Then, they examine whether from such data it is possible to obtain biomarkers. In the next sections of this chapter, I shall explore how such biological markers are identified, and how they might enhance our understanding of causal processes. Before answering this question, however, it is important to clarify the strategies through which socio-economic and biological data are collected.