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Journal articleGibson R, Eriksen R, Lamb K, et al., 2017,
Dietary assessment of British police force employees: a description of diet record coding procedures and cross-sectional evaluation of dietary energy intake reporting (the airwave health monitoring study)
, BMJ Open, Vol: 7, ISSN: 2044-6055Objectives: Dietary intake is a key aspect of occupational health. To capture the characteristics of dietary behaviour that is affected by occupational environment that may impact on disease risk, collection of prospective multi-day dietary records are required. The aims of this paper are to: i) the collection of multi day dietary data in the Airwaves health monitoring study, ii) describe the dietarycoding procedures applied and iii) investigate the plausibilityof dietary reporting in this occupational cohort. Design: A dietary coding protocol for this large-scale studywas developed to minimise coding error rate. Participants (n4,412) who completed 7-day food records were included for cross-sectional analyses. Energy intake misreporting wasestimated using the Goldberg method. Multivariate logistic regression models were applied to determine participant characteristics associated with energy intake misreporting. Setting: British police force employees enrolled (2007 to 2012) into the Airwave Health Monitoring Study. Results: The mean code error rate per food diary was3.7% (SD 3.2%). The strongest predictors of energy intake under-reporting were body mass index (BMI) and physical activity. Compared to participants withBMI <25kg/m2, thosewith BMI >30kg/m2 had increased odds of being classified as under-reporting energy intake (men OR 5.20 95%CI 3.92, 6.89; women OR 2.66 95%CI 1.85, 3.83). Men and women in the highest physical activity category compared to the lowest were also more likely to be classified as under-reporting (men OR 3.33 95%CI 2.46, 4.50; women OR 4.34 95%CI 2.91, 6.55). Conclusions: A reproducible dietary record coding procedure has been developed to minimise coding error in complex 7-day diet diaries. The prevalence of energy intake under-reporting is comparable to existingnational UK cohortsand, in agreement with p
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Journal articleGarcia Perez I, Posma JM, Gibson R, et al., 2017,
Objective assessment of dietary patterns using metabolic phenotyping: a randomized, controlled, crossover trial
, The Lancet Diabetes & Endocrinology, Vol: 5, Pages: 184-195, ISSN: 2213-8587Background: The burden of non-communicable diseases, such as obesity, diabetes, coronary heart disease and cancer, can be reduced by the consumption of healthy diets. Accurate monitoring of changes in dietary patterns in response to food policy implementation is challenging. Metabolic profiling allows simultaneous measurement of hundreds of metabolites in urine, many of them influenced by food intake. We aim to classify people according to dietary behaviour and enhance dietary reporting using metabolic profiling of urine.Methods: To develop metabolite models from 19 healthy volunteers who attended a clinical research unit for four day periods on four occasions. We used the World Health Organisation’s healthy eating guidelines (increase fruits, vegetables, wholegrains, dietary fibre and decrease fats, sugars, and salt) to develop four dietary interventions lasting for four days each that ranged from a diet associated with a low to high risk of developing non-communicable disease. Urine samples were measured by 1H-NMR spectroscopy. This study is registered as an International Standard Randomized Controlled Trial, number ISRCTN 43087333. INTERMAP U.K. (n=225) and a healthy-eating Danish cohort (n=66) were used as free-living validation datasets.Findings: There was clear separation between the urinary metabolite profiles of the four diets. We also demonstrated significant stepwise differences in metabolite levels between the lowest and highest metabolic risk diets and developed metabolite models for each diet. Application of the derived metabolite models to independent cohorts confirmed the association between urinary metabolic and dietary profiles in INTERMAP (P<0•001) and the Danish cohort (P<0•001).Interpretation: Urinary metabolite models, developed in a highly controlled environment, can classify groups of free-living people into consumers of dietary profiles associated with lower or higher non-communicable disease risk based on multivariate m
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Journal articleWarren HR, Evangelou E, Cabrera CP, et al., 2017,
Genome-wide association analysis identifies novel blood pressure loci and offers biological insights into cardiovascular risk
, NATURE GENETICS, Vol: 49, Pages: 403-415, ISSN: 1061-4036- Author Web Link
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- Citations: 352
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Journal articleChekmeneva E, Correia GDS, Chan Q, et al., 2017,
Optimization and Application of Direct Infusion Nanoelectrospray HRMS Method for Large-Scale Urinary Metabolic Phenotyping in Molecular Epidemiology
, JOURNAL OF PROTEOME RESEARCH, Vol: 16, Pages: 1646-1658, ISSN: 1535-3893Large-scale metabolic profiling requires the development of novel economical high-throughput analytical methods to facilitate characterization of systemic metabolic variation in population phenotypes. We report a fit-for-purpose direct infusion nanoelectrospray high-resolution mass spectrometry (DI-nESI-HRMS) method with time-of-flight detection for rapid targeted parallel analysis of over 40 urinary metabolites. The newly developed 2 min infusion method requires <10 μL of urine sample and generates high-resolution MS profiles in both positive and negative polarities, enabling further data mining and relative quantification of hundreds of metabolites. Here we present optimization of the DI-nESI-HRMS method in a detailed step-by-step guide and provide a workflow with rigorous quality assessment for large-scale studies. We demonstrate for the first time the application of the method for urinary metabolic profiling in human epidemiological investigations. Implementation of the presented DI-nESI-HRMS method enabled cost-efficient analysis of >10 000 24 h urine samples from the INTERMAP study in 12 weeks and >2200 spot urine samples from the ARIC study in <3 weeks with the required sensitivity and accuracy. We illustrate the application of the technique by characterizing the differences in metabolic phenotypes of the USA and Japanese population from the INTERMAP study.
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Journal articleInglese P, McKenzie JS, Mroz A, et al., 2017,
Deep learning and 3D-DESI imaging reveal the hidden metabolic heterogeneity of cancer
, Chemical Science, Vol: 8, Pages: 3500-3511, ISSN: 2041-6539Visual inspection of tumour tissues does not reveal the complex metabolic changes that differentiate cancer and its sub-types from healthy tissues. Mass spectrometry imaging, which quantifies the underlying chemistry, represents a powerful tool for the molecular exploration of tumour tissues. A 3-dimensional topological description of the chemical properties of the tumour permits the formulation of hypotheses about the biological composition and interactions and the possible causes of its heterogeneous structure. The large amount of information contained in such datasets requires powerful tools for its analysis, visualisation and interpretation. Linear methods for unsupervised dimensionality reduction, such as PCA, are inadequate to capture the complex non-linear relationships present in these data. For this reason, a deep unsupervised neural network based technique, parametric t-SNE, is adopted to map a 3D-DESI-MS dataset from a human colorectal adenocarcinoma biopsy onto a 2-dimensional manifold. This technique allows the identification of clusters not visible with linear methods. The unsupervised clustering of the tumour tissue results in the identification of sub-regions characterised by the abundance of identified metabolites, making possible the formulation of hypotheses to account for their significance and the underlying biological heterogeneity in the tumour.
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Journal articleKuo CS, Pavlidis S, Loza M, et al., 2017,
A transcriptome-driven analysis of epithelial brushings and bronchial biopsies to define asthma phenotypes in U-BIOPRED
, American Journal of Respiratory and Critical Care Medicine, Vol: 195, Pages: 443-455, ISSN: 1535-4970RATIONALE AND OBJECTIVES: Asthma is a heterogeneous disease driven by diverse immunologic and inflammatory mechanisms. We used transcriptomic profiling of airway tissues to help define asthma phenotypes. METHODS: The transcriptome from bronchial biopsies and epithelial brushings of 107 moderate-to-severe asthmatics were annotated by gene-set variation analysis (GSVA) using 42 gene-signatures relevant to asthma, inflammation and immune function. Topological data analysis (TDA) of clinical and histological data was used to derive clusters and the nearest shrunken centroid algorithm used for signature refinement. RESULTS: 9 GSVA signatures expressed in bronchial biopsies and airway epithelial brushings distinguished two distinct asthma subtypes associated with high expression of T-helper type 2 (Th-2) cytokines and lack of corticosteroid response (Group 1 and Group 3). Group 1 had the highest submucosal eosinophils, high exhaled nitric oxide (FeNO) levels, exacerbation rates and oral corticosteroid (OCS) use whilst Group 3 patients showed the highest levels of sputum eosinophils and had a high BMI. In contrast, Group 2 and Group 4 patients had an 86% and 64% probability of having non-eosinophilic inflammation. Using machine-learning tools, we describe an inference scheme using the currently-available inflammatory biomarkers sputum eosinophilia and exhaled nitric oxide levels along with OCS use that could predict the subtypes of gene expression within bronchial biopsies and epithelial cells with good sensitivity and specificity. CONCLUSION: This analysis demonstrates the usefulness of a transcriptomic-driven approach to phenotyping that segments patients who may benefit the most from specific agents that target Th2-mediated inflammation and/or corticosteroid insensitivity.
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Journal articleNoble A, Durant L, Hoyles L, et al., 2017,
Dysregulation of cellular vs humoral immunity to the intestinal microbiota in inflammatory bowel disease
, JOURNAL OF CROHNS & COLITIS, Vol: 11, Pages: S123-S124, ISSN: 1873-9946 -
Journal articleMarouli E, Graff M, Medina-Gomez C, et al., 2017,
Rare and low-frequency coding variants alter human adult height
, Nature, Vol: 542, Pages: 186-190, ISSN: 0028-0836Heightis a highly heritable, classic polygenic traitwith~700common associated variants identified so far through genome-wide association studies. Here,we report 83 height-associated codingvariants with lowerminor allele frequencies(range of0.1-4.8%)and effects ofup to 2 16cm/allele(e.g.in IHH, STC2, ARand CRISPLD2), >10timesthe average effect of common variants.In functional follow-upstudies,rare height-increasing allelesof STC2(+1-2 cm/allele) compromisedproteolytic inhibition of PAPP-A and increased cleavage of IGFBP-4in vitro, resulting in higher bioavailability of insulin-like growth factors.These 83height-associated variants overlapgenes mutated in monogenic growth disordersand highlight new biological candidates (e.g. ADAMTS3, IL11RA, NOX4) and pathways (e.g. proteoglycan/glycosaminoglycan synthesis)involved in growth.Our results demonstratethatsufficiently large sample sizescan uncoverrare and low-frequency variants of moderate to large effect associated with polygenic human phenotypes,andthat these variantsimplicate relevant genes and pathways.
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Journal articleNoble A, Durant L, Hoyles L, et al., 2017,
P093 Dysregulation of cellular vs humoral immunity to the intestinal microbiota in inflammatory bowel disease.
, J Crohns Colitis, Vol: 11, Pages: S123-S124 -
Journal articleAinsworth D, Sternberg MJE, Raczy C, et al., 2016,
k-SLAM: Accurate and ultra-fast taxonomic classification and gene identification for large metagenomic datasets
, Nucleic Acids Research, Vol: 45, Pages: 1649-1656, ISSN: 1362-4962k-SLAM is a highly e cient algorithm for the characterisa-tion of metagenomic data. Unlike other ultra-fast metage-nomic classi ers, full sequence alignment is performed allow-ing for gene identi cation and variant calling in addition toaccurate taxonomic classi cation. Ak-mer based methodprovides greater taxonomic accuracy than other classi ersand a three orders of magnitude speed increase over align-ment based approaches. The use of alignments to nd vari-ants and genes along with their taxonomic origins enablesnovel strains to be characterised. k-SLAM's speed allows afull taxonomic classi cation and gene identi cation to betractable on modern large datasets. A pseudo-assemblymethod is used to increase classi cation accuracy by up to40% for species which have high sequence homology withintheir genus.
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