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

Lifelines NEXT:a prospective birth cohort adding the next generation to the three-generation Lifelines cohort study

Epidemiological research has shown there to be a strong relationship between preconceptional, prenatal, birth and early-life factors and lifelong health. The Lifelines NEXT is a birth cohort designed to study the effects of intrinsic and extrinsic determinants on health and disease in a four-generation design. It is embedded within the Lifelines cohort study, a prospective three-generation population-based cohort study recording the health and health-related aspects of 167,729 individuals living in Northern Netherlands. In Lifelines NEXT we aim to include 1500 pregnant Lifelines participants and intensively follow them, their partners and their children until at least 1 year after birth. Longer-term follow-up of physical and psychological health will then be embedded following Lifelines procedures. During the Lifelines NEXT study period biomaterials-including maternal and neonatal (cord) blood, placental tissue, feces, breast milk, nasal swabs and urine-will be collected from the mother and child at 10 time points. We will also collect data on medical, social, lifestyle and environmental factors via questionnaires at 14 different time points and continuous data via connected devices. The extensive collection of different (bio)materials from mother and child during pregnancy and afterwards will provide the means to relate environmental factors including maternal and neonatal microbiome composition) to (epi)genetics, health and developmental outcomes. The nesting of the study within Lifelines enables us to include preconceptional transgenerational data and can be used to identify other extended families within the cohort

The Gut Microbiome Contributes to a Substantial Proportion of the Variation in Blood Lipids

Rationale: Evidence suggests that the gut microbiome is involved in the development of cardiovascular disease, with the host–microbe interaction regulating immune and metabolic pathways. However, there was no firm evidence for associations between microbiota and metabolic risk factors for cardiovascular disease from large-scale studies in humans. In particular, there was no strong evidence for association between cardiovascular disease and aberrant blood lipid levels. Objectives: To identify intestinal bacteria taxa, whose proportions correlate with body mass index and lipid levels, and to determine whether lipid variance can be explained by microbiota relative to age, sex, and host genetics. Methods and Results: We studied 893 subjects from the LifeLines-DEEP population cohort. After correcting for age and sex, we identified 34 bacterial taxa associated with body mass index and blood lipids; most are novel associations. Cross-validation analysis revealed that microbiota explain 4.5% of the variance in body mass index, 6% in triglycerides, and 4% in high-density lipoproteins, independent of age, sex, and genetic risk factors. A novel risk model, including the gut microbiome explained ≤25.9% of high-density lipoprotein variance, significantly outperforming the risk model without microbiome. Strikingly, the microbiome had little effect on low-density lipoproteins or total cholesterol. Conclusions: Our studies suggest that the gut microbiome may play an important role in the variation in body mass index and blood lipid levels, independent of age, sex, and host genetics. Our findings support the potential of therapies altering the gut microbiome to control body mass, triglycerides, and high-density lipoproteins

Occupational exposure to gases/fumes and mineral dust affect DNA methylation levels of genes regulating expression

Many workers are daily exposed to occupational agents like gases/fumes, mineral dust or biological dust, which could induce adverse health effects. Epigenetic mechanisms, such as DNA methylation, have been suggested to play a role. We therefore aimed to identify differentially methylated regions (DMRs) upon occupational exposures in never-smokers and investigated if these DMRs associated with gene expression levels. To determine the effects of occupational exposures independent of smoking, 903 never-smokers of the LifeLines cohort study were included. We performed three genome-wide methylation analyses (Illumina 450 K), one per occupational exposure being gases/fumes, mineral dust and biological dust, using robust linear regression adjusted for appropriate confounders. DMRs were identified using comb-p in Python. Results were validated in the Rotterdam Study (233 never-smokers) and methylation-expression associations were assessed using Biobank-based Integrative Omics Study data (n = 2802). Of the total 21 significant DMRs, 14 DMRs were associated with gases/fumes and 7 with mineral dust. Three of these DMRs were associated with both exposures (RPLP1 and LINC02169 (2x)) and 11 DMRs were located within transcript start sites of gene expression regulating genes. We replicated two DMRs with gases/fumes (VTRNA2-1 and GNAS) and one with mineral dust (CCDC144NL). In addition, nine gases/fumes DMRs and six mineral dust DMRs significantly associated with gene expression levels. Our data suggest that occupational exposures may induce differential methylation of gene expression regulating genes and thereby may induce adverse health effects. Given the millions of workers that are exposed daily to occupational exposures, further studies on this epigenetic mechanism and health outcomes are warranted

Age-related accrual of methylomic variability is linked to fundamental ageing mechanisms

Background: Epigenetic change is a hallmark of ageing but its link to ageing mechanisms in humans remains poorly understood. While DNA methylation at many CpG sites closely tracks chronological age, DNA methylation changes relevant to biological age are expected to gradually dissociate from chronological age, mirroring the increased heterogeneity in health status at older ages. Results: Here, we report on the large-scale identification of 6366 age-related variably methylated positions (aVMPs) identified in 3295 whole blood DNA methylation profiles, 2044 of which have a matching RNA-seq gene expression profile. aVMPs are enriched at polycomb repressed regions and, accordingly, methylation at those positions is associated with the expression of genes encoding components of polycomb repressive complex 2 (PRC2) in trans. Further analysis revealed trans-associations for 1816 aVMPs with an additional 854 genes. These trans-associated aVMPs are characterized by either an age-related

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