EPIC4ND: European Prospective Investigation into Cancer and Nutrition follow-up for neurodegenerative diseases
Lill, C. M.; Homann, J.; Ohlei, O.; Smith-Byrne, K.; Viallon, V.; Huerta, J.-M.; Artaud, F.; Zhao, Y.; Britten, A.; Deecke, L.; Dobricic, V.; Mokoroa, O.; Guevara, M.; Petrova, D.; Trupp, M.; Sabin, J.; Groeninger, M.; Colorado-Yohar, S. M.; Martin, S.; Cabrera-Castro, N.; Travis, R.; Groppa, S.; Robinson, O.; Eriksson, S.; Sieri, S.; Jimenez, A.; Tong, T. Y.; Kaaks, R.; Severi, G.; Travis, R.; Wareham, N.; Benedet, A. L.; Zetterberg, H.; Franke, A.; Elbaz, A.; Bertram, L.; Vermeulen, R.; Middleton, L.; Masala, G.; Sacerdote, C.; Peters, S.; Katzke, V.; Ferrari, P.; Gunter, M. J.; Riboli, E.
Show abstract
The European Prospective Investigation into Cancer and Nutrition cohort (EPIC-Europe) is a prospective study including [~]520,000 participants recruited across Europe (1991-2000) with in-depth baseline data on nutritional, lifestyle, medical, and anthropometric variables, and baseline blood samples. Here we introduce EPIC4ND, a case-cohort study within EPIC designed to identify biomarkers predicting a future onset of dementia, Alzheimers disease (AD), Parkinsons disease (PD), and amyotrophic lateral sclerosis (ALS). EPIC4ND comprises 6,346 initially non-diseased participants (aged 35-80 years, mean age at baseline: 54{+/-}9, 65% women) with up to 30 years of follow-up and data on at least one omics domain available from pre-disease blood samples. EPIC4ND includes 4,604 subcohort members (4,447 non-cases and 157 incident cases) and 1,742 additional incident cases ascertained from the broader EPIC cohort. Among the incident cases, there are 1,190 dementia cases (818 AD), 534 PD cases, and 199 ALS cases. Additionally, 72 prevalent PD cases and 118 incident Parkinsonism cases are available for comparison. Molecular data generated encompass proteomics, genome-wide DNA methylation, and SNP genotyping with 4,065 EPIC4ND participants (2,497 non-cases, 1,568 incident cases) having data on all three domains. Smaller studies include data on metals, metabolites, and environmental chemicals, while ongoing efforts focus on ultrasensitive targeted biomarker measurements and small RNA sequencing. Genome-wide association studies and analyses of epidemiological risk factors validate the dataset by confirming many known risk factors. Leveraging these extensive pre-disease multi-layered omics data offers a unique opportunity to identify biomarker signatures predicting neurodegenerative diseases and to explore their interplay with epidemiological risk factors.
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