Habitual dietary intake of IBD patients differs from population controls:a case-control study

BACKGROUND: Since evidence-based dietary guidelines are lacking for IBD patients, they tend to follow "unguided" dietary habits; potentially leading to nutritional deficiencies and detrimental effects on disease course. Therefore, we compared dietary intake of IBD patients with controls. METHODS: Dietary intake of macronutrients and 25 food groups of 493 patients (207 UC, 286 CD), and 1291 controls was obtained via a food frequency questionnaire. RESULTS: 38.6% of patients in remission had protein intakes below the recommended 0.8 g/kg and 86.7% with active disease below the recommended 1.2 g/kg. Multinomial logistic regression, corrected for age, gender and BMI, showed that (compared to controls) UC patients consumed more meat and spreads, but less alcohol, breads, coffee and dairy; CD patients consumed more non-alcoholic drinks, potatoes, savoury snacks and sugar and sweets but less alcohol, dairy, nuts, pasta and prepared meals. Patients with active disease consumed more meat, soup and sugar and sweets but less alcohol, coffee, dairy, prepared meals and rice; patients in remission consumed more potatoes and spreads but less alcohol, breads, dairy, nuts, pasta and prepared meals. CONCLUSIONS: Patients avoiding potentially favourable foods and gourmandizing potentially unfavourable foods are of concern. Special attention is needed for protein intake in the treatment of these patients

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

Additional file 10: of Blood lipids influence DNA methylation in circulating cells

Power calculations were performed for the situations where the EWAS effect size estimate is (1) the true causal effect size, (2) half the true causal effect size, and (3) double the true causal effect size. (CSV 1Â kb

Additional file 11: of Blood lipids influence DNA methylation in circulating cells

Effect of lipid levels on DNA methylation corrected for nearest PS SNP within 1Â Mb. Estimate is the percentage change in DNA methylation per standard deviation change in lipid levels. (CSV 289 bytes

Additional file 5: of Blood lipids influence DNA methylation in circulating cells

The lipid-associated SNPs used to construct the polygenic scores. (CSV 1Â kb

Additional file 7: of Blood lipids influence DNA methylation in circulating cells

The HDL-C PS is nominally associated with confounder neutrophil counts. (CSV 202 bytes

Additional file 11: of Blood lipids influence DNA methylation in circulating cells

Effect of lipid levels on DNA methylation corrected for nearest PS SNP within 1Â Mb. Estimate is the percentage change in DNA methylation per standard deviation change in lipid levels. (CSV 289 bytes

Blood lipids influence DNA methylation in circulating cells

BACKGROUND: Cells can be primed by external stimuli to obtain a long-term epigenetic memory. We hypothesize that long-term exposure to elevated blood lipids can prime circulating immune cells through changes in DNA methylation, a process that may contribute to the development of atherosclerosis. To interrogate the causal relationship between triglyceride, low-density lipoprotein (LDL) cholesterol, and high-density lipoprotein (HDL) cholesterol levels and genome-wide DNA methylation while excluding confounding and pleiotropy, we perform a stepwise Mendelian randomization analysis in whole blood of 3296 individuals. RESULTS: This analysis shows that differential methylation is the consequence of inter-individual variation in blood lipid levels and not vice versa. Specifically, we observe an effect of triglycerides on DNA methylation at three CpGs, of LDL cholesterol at one CpG, and of HDL cholesterol at two CpGs using multivariable Mendelian randomization. Using RNA-seq data available for a large subset of individuals (N = 2044), DNA methylation of these six CpGs is associated with the expression of CPT1A and SREBF1 (for triglycerides), DHCR24 (for LDL cholesterol) and ABCG1 (for HDL cholesterol), which are all key regulators of lipid metabolism. CONCLUSIONS: Our analysis suggests a role for epigenetic priming in end-product feedback control of lipid metabolism and highlights Mendelian randomization as an effective tool to infer causal relationships in integrative genomics data

Additional file 7: of Blood lipids influence DNA methylation in circulating cells

The HDL-C PS is nominally associated with confounder neutrophil counts. (CSV 202 bytes