Cohort profile: LifeLines DEEP, a prospective, general population cohort study in the northern Netherlands: study design and baseline characteristics

Purpose There is a critical need for population-based prospective cohort studies because they follow individuals before the onset of disease, allowing for studies that can identify biomarkers and disease-modifying effects, and thereby contributing to systems epidemiology. Participants This paper describes the design and baseline characteristics of an intensively examined subpopulation of the LifeLines cohort in the Netherlands. In this unique subcohort, LifeLines DEEP, we included 1539 participants aged 18 years and older. Findings to date We collected additional blood (n=1387), exhaled air (n=1425) and faecal samples (n=1248), and elicited responses to gastrointestinal health questionnaires (n=1176) for analysis of the genome, epigenome, transcriptome, microbiome, metabolome and other biological levels. Here, we provide an overview of the different data layers in LifeLines DEEP and present baseline characteristics of the study population including food intake and quality of life. We also describe how the LifeLines DEEP cohort allows for the detailed investigation of genetic, genomic and metabolic variation for a wide range of phenotypic outcomes. Finally, we examine the determinants of gastrointestinal health, an area of particular interest to us that can be addressed by LifeLines DEEP. Future plans We have established a cohort of which multiple data levels allow for the integrative analysis of populations for translation of this information into biomarkers for disease, and which will offer new insights into disease mechanisms and prevention.

[1]  C. Wijmenga,et al.  Cohort Profile Cohort Profile : LifeLines , a three-generation cohort study and biobank , 2015 .

[2]  M. Bonder,et al.  Genotype harmonizer: automatic strand alignment and format conversion for genotype data integration , 2014, BMC Research Notes.

[3]  Pieter B. T. Neerincx,et al.  Supplementary Information Whole-genome sequence variation , population structure and demographic history of the Dutch population , 2022 .

[4]  Heorhiy Byelas,et al.  Improved imputation quality of low-frequency and rare variants in European samples using the ‘Genome of The Netherlands' , 2014, European Journal of Human Genetics.

[5]  R. Malekzadeh,et al.  Epidemiological transition of colorectal cancer in developing countries: environmental factors, molecular pathways, and opportunities for prevention. , 2014, World journal of gastroenterology.

[6]  Se Jin Song,et al.  The treatment-naive microbiome in new-onset Crohn's disease. , 2014, Cell host & microbe.

[7]  Peggy Hall,et al.  The NHGRI GWAS Catalog, a curated resource of SNP-trait associations , 2013, Nucleic Acids Res..

[8]  M. Daly,et al.  Exploring the genetics of irritable bowel syndrome: a GWA study in the general population and replication in multinational case-control cohorts , 2013, Gut.

[9]  Pieter B. T. Neerincx,et al.  The Genome of the Netherlands: design, and project goals , 2013, European Journal of Human Genetics.

[10]  J. Nicholson,et al.  Metabolic phenotyping and systems biology approaches to understanding metabolic syndrome and fatty liver disease. , 2014, Gastroenterology.

[11]  L. Berthiaume,et al.  Wnt acylation: seeing is believing. , 2014, Nature chemical biology.

[12]  Agnieszka Smolinska,et al.  Profile of volatile organic compounds in exhaled breath changes as a result of gluten-free diet , 2013, Journal of breath research.

[13]  Buhm Han,et al.  Imputing Amino Acid Polymorphisms in Human Leukocyte Antigens , 2013, PloS one.

[14]  C. Wijmenga,et al.  Improving coeliac disease risk prediction by testing non-HLA variants additional to HLA variants , 2013, Gut.

[15]  Alexander Meissner,et al.  Molecular pathological epidemiology of epigenetics: emerging integrative science to analyze environment, host, and disease , 2013, Modern Pathology.

[16]  Morris A. Swertz,et al.  Scaling Bio-Analyses from Computational Clusters to Grids , 2013, IWSG.

[17]  O. Delaneau,et al.  Supplementary Information for ‘ Improved whole chromosome phasing for disease and population genetic studies ’ , 2012 .

[18]  P. Munkholm,et al.  Genome‐wide peripheral blood leukocyte DNA methylation microarrays identified a single association with inflammatory bowel diseases , 2012, Inflammatory bowel diseases.

[19]  V. Tremaroli,et al.  Functional interactions between the gut microbiota and host metabolism , 2012, Nature.

[20]  F. V. van Schooten,et al.  The versatile use of exhaled volatile organic compounds in human health and disease , 2012, Journal of breath research.

[21]  Cisca Wijmenga,et al.  From genome-wide association studies to disease mechanisms: celiac disease as a model for autoimmune diseases , 2012, Seminars in Immunopathology.

[22]  R. Haring,et al.  Diving through the "-omics": the case for deep phenotyping and systems epidemiology. , 2012, Omics : a journal of integrative biology.

[23]  L. Kiemeney,et al.  How to kickstart a national biobanking infrastructure – experiences and prospects of BBMRI-NL , 2012 .

[24]  Markus Perola,et al.  Genome-wide association study identifies multiple loci influencing human serum metabolite levels , 2012, Nature Genetics.

[25]  J. Marchini,et al.  Genotype Imputation with Thousands of Genomes , 2011, G3: Genes | Genomes | Genetics.

[26]  Sarah Edkins,et al.  Dense genotyping identifies and localizes multiple common and rare variant association signals in celiac disease , 2011, Nature Genetics.

[27]  M. Waldenberger,et al.  Comprehensive catalog of European biobanks , 2011, Nature Biotechnology.

[28]  Peter Nürnberg,et al.  Wnt signaling and Dupuytren's disease. , 2011, The New England journal of medicine.

[29]  A. Geelen,et al.  Self-reported energy intake by FFQ compared with actual energy intake to maintain body weight in 516 adults , 2011, British Journal of Nutrition.

[30]  William A. Walters,et al.  QIIME allows analysis of high-throughput community sequencing data , 2010, Nature Methods.

[31]  M. Pencina,et al.  General Cardiovascular Risk Profile for Use in Primary Care: The Framingham Heart Study , 2008, Circulation.

[32]  Lin Chang,et al.  FUNCTIONAL BOWEL DISORDERS , 2018, The American Journal of Gastroenterology.

[33]  Manuel A. R. Ferreira,et al.  PLINK: a tool set for whole-genome association and population-based linkage analyses. , 2007, American journal of human genetics.

[34]  D. Postma,et al.  Universal risk factors for multifactorial diseases , 2007, European Journal of Epidemiology.

[35]  P. Lansdorp,et al.  Flow cytometry and FISH to measure the average length of telomeres (flow FISH) , 2006, Nature Protocols.

[36]  Francis S. Collins,et al.  Genes, environment and the value of prospective cohort studies , 2006, Nature Reviews Genetics.

[37]  Eoin L. Brodie,et al.  Greengenes, a Chimera-Checked 16S rRNA Gene Database and Workbench Compatible with ARB , 2006, Applied and Environmental Microbiology.

[38]  D. Drossman,et al.  AGA technical review on irritable bowel syndrome. , 2002, Gastroenterology.

[39]  H. El‐Serag,et al.  Health‐related quality of life among persons with irritable bowel syndrome: a systematic review , 2002, Alimentary pharmacology & therapeutics.

[40]  A. Kilbourne,et al.  The impact of irritable bowel syndrome on health-related quality of life. , 2000, Gastroenterology.

[41]  N. Aaronson,et al.  Translation, validation, and norming of the Dutch language version of the SF-36 Health Survey in community and chronic disease populations. , 1998, Journal of clinical epidemiology.

[42]  M Sullivan,et al.  The equivalence of SF-36 summary health scores estimated using standard and country-specific algorithms in 10 countries: results from the IQOLA Project. International Quality of Life Assessment. , 1998, Journal of clinical epidemiology.

[43]  W H Rogers,et al.  Comparison of methods for the scoring and statistical analysis of SF-36 health profile and summary measures: summary of results from the Medical Outcomes Study. , 1995, Medical care.

[44]  Roger Jones,et al.  Irritable bowel syndrome in the general population. , 1992, BMJ.

[45]  J. Virjee,et al.  Detection of pseudodiarrhoea by simple clinical assessment of intestinal transit rate. , 1990, BMJ.