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

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          Abstract

          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.

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          Most cited references16

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          General cardiovascular risk profile for use in primary care: the Framingham Heart Study.

          Separate multivariable risk algorithms are commonly used to assess risk of specific atherosclerotic cardiovascular disease (CVD) events, ie, coronary heart disease, cerebrovascular disease, peripheral vascular disease, and heart failure. The present report presents a single multivariable risk function that predicts risk of developing all CVD and of its constituents. We used Cox proportional-hazards regression to evaluate the risk of developing a first CVD event in 8491 Framingham study participants (mean age, 49 years; 4522 women) who attended a routine examination between 30 and 74 years of age and were free of CVD. Sex-specific multivariable risk functions ("general CVD" algorithms) were derived that incorporated age, total and high-density lipoprotein cholesterol, systolic blood pressure, treatment for hypertension, smoking, and diabetes status. We assessed the performance of the general CVD algorithms for predicting individual CVD events (coronary heart disease, stroke, peripheral artery disease, or heart failure). Over 12 years of follow-up, 1174 participants (456 women) developed a first CVD event. All traditional risk factors evaluated predicted CVD risk (multivariable-adjusted P<0.0001). The general CVD algorithm demonstrated good discrimination (C statistic, 0.763 [men] and 0.793 [women]) and calibration. Simple adjustments to the general CVD risk algorithms allowed estimation of the risks of each CVD component. Two simple risk scores are presented, 1 based on all traditional risk factors and the other based on non-laboratory-based predictors. A sex-specific multivariable risk factor algorithm can be conveniently used to assess general CVD risk and risk of individual CVD events (coronary, cerebrovascular, and peripheral arterial disease and heart failure). The estimated absolute CVD event rates can be used to quantify risk and to guide preventive care.
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            AGA technical review on irritable bowel syndrome.

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              The impact of irritable bowel syndrome on health-related quality of life.

              Few data are available to evaluate health-related quality of life (HRQOL) of people with irritable bowel syndrome (IBS). We evaluated and compared the impact of IBS on HRQOL using previously reported HRQOL data for the U.S. general population and for people with selected chronic diseases. Using the SF-36 Health Survey, we compared the HRQOL of IBS patients (n = 877) with previously reported SF-36 data for the general U.S. population and for patients with gastroesophageal reflux disease (GERD), diabetes mellitus, depression, and dialysis-dependent end-stage renal disease (ESRD). On all 8 SF-36 scales, IBS patients had significantly worse HRQOL than the U.S. general population (P < 0. 001). Compared with GERD patients, IBS patients scored significantly lower on all SF-36 scales (P < 0.001) except physical functioning. Similarly, IBS patients had significantly worse HRQOL on selected SF-36 scales than patients with diabetes mellitus and ESRD. IBS patients had significantly better mental health SF-36 scale scores than patients with depression (P < 0.001). IBS patients experience significant impairment in HRQOL. Decrements in HRQOL are most pronounced in energy/fatigue, role limitations caused by physical health problems, bodily pain, and general health perceptions. These data offer further insight into the impact of IBS on patient functional status and well-being.
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                Author and article information

                Journal
                BMJ Open
                BMJ Open
                bmjopen
                bmjopen
                BMJ Open
                BMJ Publishing Group (BMA House, Tavistock Square, London, WC1H 9JR )
                2044-6055
                2015
                28 August 2015
                : 5
                : 8
                : e006772
                Affiliations
                [1 ]Department of Genetics, University of Groningen, University Medical Center Groningen , Groningen, The Netherlands
                [2 ]Top Institute Food and Nutrition , Wageningen, The Netherlands
                [3 ]Laboratory of Microbiology, Wageningen University , Wageningen, The Netherlands
                [4 ]Department of Toxicology, Nutrition and Toxicology Research (NUTRIM), Maastricht University Medical Center+ , Maastricht, The Netherlands
                [5 ]Division of Gastroenterology-Hepatology, Maastricht University Medical Center+ , Maastricht, The Netherlands
                [6 ]University of Groningen, University Medical Center Groningen, Genomics Coordination Center , Groningen, The Netherlands
                [7 ]Research Group in Food and Human Nutrition, University of Antioquia , Medellín, Colombia
                [8 ]LifeLines Cohort Study , Groningen, The Netherlands
                [9 ]Department of Epidemiology, University of Groningen, University Medical Center Groningen , Groningen, The Netherlands
                [10 ]Division of Human Nutrition, Section Nutrition and Epidemiology, Wageningen University , Wageningen, The Netherlands
                Author notes
                [Correspondence to ] Ettje F Tigchelaar; e.f.tigchelaar@ 123456umcg.nl
                Article
                bmjopen-2014-006772
                10.1136/bmjopen-2014-006772
                4554905
                26319774
                ac16623c-18b5-4c60-a197-fe3e63929f66
                Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions

                This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/

                History
                : 29 September 2014
                : 20 May 2015
                : 15 June 2015
                Categories
                Epidemiology
                Cohort Profile
                1506
                1692
                1697
                1724

                Medicine
                epidemiology,public health,genetics
                Medicine
                epidemiology, public health, genetics

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