ERC FUNDED PROJECTS

In todays advanced technological world, we can track the exact movement of individuals, analyse their genetic makeup and predict predisposition to certain diseases. However, we are unable to accurately assess an individual’s dietary intake. This is without a doubt one of the main stumbling blocks in assessing the link between diet and disease/health. The present proposal (A-DIET) will address this issue with the overarching objective to develop novel strategies for assessment of dietary intake. Using approaches to (1) identify biomarkers of specific foods (2) classify people into dietary patterns (nutritypes) and (3) develop a tool for integration of dietary and biomarker data, A-DIET has the potential to dramatically enhance our ability to accurately assess dietary intake. The ultimate output from A-DIET will be a dietary assessment tool which can be used to obtain an accurate assessment of dietary intake by combining dietary and biomarker data which in turn will allow investigations into relationships between diet, health and disease. New biomarkers of specific foods will be identified and validated using intervention studies and metabolomic analyses. Methods will be developed to classify individuals into dietary patterns based on biomarker/metabolomic profiles thus demonstrating the novel concept of nutritypes. Strategies for integration of dietary and biomarker data will be developed and translated into a tool that will be made available to the wider scientific community. Advances made in A-DIET will enable nutrition epidemiologist’s to properly examine the relationship between diet and disease and develop clear public health messages with regard to diet and health. Additionally results from A-DIET will allow researchers to accurately assess people’s diet and implement health promotion strategies and enable dieticians in a clinical environment to assess compliance to therapeutic diets such as adherence to a high fibre diet or a gluten free diet.

1 995 548 €

Start date: 2015-08-01, End date: 2020-07-31

Today, our daily diet typically contains dozens of food additives (e.g. colours, emulsifiers, sweeteners: ~350 substances allowed on the EU market). Safety assessment is performed by health agencies to protect consumers against potential adverse effects of each additive, yet such an assessment is only based on current available evidence, i.e., for most additives, only in-vitro/in-vivo toxicological studies and exposure simulations. Meanwhile, the long-term health impact of additives intake and any potential ‘cocktail’ effects remain largely unknown and have become a source of serious concern. Growing evidence link the consumption of ultra-processed foods, containing numerous additives, to adverse health outcomes, in particular our recent results on cancer (Fiolet BMJ 2018). While most additives allowed in the EU are likely to be neutral for health and some may even be beneficial, recent animal and cell-based studies have suggested detrimental effects of several such compounds. In humans, data is lacking. No epidemiological study has ever assessed individual-level exposure to a wide range of food additives and its association with health, hampered by unsuited traditional dietary assessment tools facing the high additive content variability across commercial brands. Hence, a major breakthrough will come from the novel and unique tools I developed with my team, notably within the NutriNet-Santé cohort (n=164,000), collecting precise and repeated data on foods and beverages usually consumed, including names and brands of industrial products. With this unique resource, I propose a project at the forefront of international research to provide answers to a question of major importance for public health. Built as a combination of epidemiological studies and in-vitro/in-vivo experiments, this project will shed light on individual exposure to food additive 'cocktails' in relation to obesity, cancer, cardiovascular diseases and mortality, while depicting underlying mechanisms.

2 000 000 €

Start date: 2020-05-01, End date: 2025-04-30

Chronic kidney disease (CKD) is a major global health problem, affecting 10% of the world population and projected to be the fifth major cause of death in 2040. CKD patients are one of the most complex and multi-morbid populations in internal medicine while at the same time having the least translational randomized clinical trials and limited treatment options. One of the major reasons for this is the lack of reproducible approaches specifically reflecting intrarenal pathological processes and disease activity. The overall goal of AIM.imaging.CKD is to specifically address this unmet need by developing, validating and integrating image-based diagnostics for CKD. The integration of broad interdisciplinary expertise will enable to develop a multiscale approach from nano- to micro- to macromorphological and molecular diagnostics. Specifically, the project will develop augmented full-spectrum ultrastructural (“nano”) and histological (“micro”) renal biopsy diagnostics, focusing on reproducible, quantitative nephropathological analyses and prediction of clinically relevant outcome parameters. The project will also explore macro-morphological and molecular imaging in CKD, focusing on translatable non-invasive approaches. The central feature will be the development of advanced, scalable and modular image analyses models utilizing artificial intelligence (AI), particularly machine and deep learning. Using preclinical testing and clinical validation, the main emphasis will be on accelerated or, whenever possible, direct implementation into the clinical practice. The integration of the above-mentioned tools and technologies provides a comprehensive multiscale and multiplex approach for improved diagnostics of CKD patients and facilitate future randomized clinical trials. At each level, and even more so when integrated, the results are expected to augment and transform image-based diagnostics of kidney diseases, and thereby lead to improved patient management and outcome.

1 999 375 €

Start date: 2021-05-01, End date: 2026-04-30

Pain is one of the most problematic symptoms of rheumatic disease such as rheumatoid arthritis (RA) and fibromyalgia (FM). We have earlier discovered that antibodies (immunoglobulin, IgG) purified from blood of seropositive rheumatoid arthritis (RA) patients induce pain-like behavior when transferred to mice, independent of inflammatory reactions. Even though FM is not considered an autoimmune disease, it has been suggested that neuroimmune dysregulation contribute to the pathogenesis. Therefore, we purified IgG from FM patients and found that also IgG from FM patients, but not healthy controls, have pronociceptive properties in mice, and surprisingly, bind to satellite glial cells in dorsal root ganglia. Our findings highlights the importance of expanding our view on which chronic pain conditions that could have an underlying autoimmunity as part of the pain pathology. Thus, the overall objective of this project is to investigate both general, and disease specific, pain-inducing mechanisms mediated by RA and FM IgG. Objective 1. Investigate how IgG from RA and FM patients induce pain-like behavior after transfer to mice Objective 2. Search for RA and FM IgG induced maladaptive changes in sensory neurons that mediate hyperexcitability and long-term pain-like behavior Using patient and healthy control samples, in vivo mouse behavioral assays, primary neuronal and non-neuronal cell cultures together with stat-of-the-art methodology, we will investigate how RA and FM-associated autoantibodies alter sensory neuronal excitability. If successful our project will not only challenge the view of how antibodies can contribute to pain but also pin-point specific mechanisms by which disease-relevant antibodies induce and maintain pain independent of previously described inflammatory mechanisms. Such findings promise to resolve currently unanswered questions concerning symptoms of pain in RA and FM, and to pave the way for the development of new pain-relieving therapies.

1 993 763 €

Start date: 2020-10-01, End date: 2025-09-30