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  • Journal article
    Pazoki R, Dehghan A, Evangelou E, Warren H, Gao H, Caulfield M, Elliott P, Tzoulaki Iet al., 2019,

    Genetic Predisposition to High Blood Pressure and Lifestyle Factors: Associations With Midlife Blood Pressure Levels and Cardiovascular Events (vol 137, pg 653, 2018)

    , CIRCULATION, Vol: 139, Pages: E2-E2, ISSN: 0009-7322
  • Journal article
    Piel FBJ, Brandon P, Hima D, Anna L H, Paul Eet al., 2018,

    The challenge of opt-outs from NHS data: a small-area perspective

    , Journal of Public Health, Vol: 40, Pages: e594-e600, ISSN: 1741-3842
  • Journal article
    Gibson R, Frost G, Chan Q, Elliott P, Singh D, Eriksen R, Heard A, Vergnaud ACet al., 2018,

    A cross-sectional investigation into the occupational and socio-demographic characteristics of British police force employees reporting a dietary pattern associated with cardiometabolic risk: Findings from the Airwave Health Monitoring Study

    , European Journal of Nutrition, Vol: 57, Pages: 2913-2926, ISSN: 0044-264X

    PurposeThe aims of this study were to (1) determine the association between diet quality using the Dietary Approaches to Stop Hypertension (DASH) score and cardiometabolic risk in a British working population and (2) identify employee characteristics associated with reporting a poorer quality dietary pattern.MethodsBritish police employees enrolled (2007–2012) into the Airwave Health Monitoring Study (n = 5527) were included for sex-specific cross-sectional analyses. Dietary intakes were measured using 7-day food records. DASH score was calculated to determine diet quality. Logistic regression evaluated associations between (1) diet quality and increased cardiometabolic risk (defined as ≥ 3 risk markers: dyslipidaemia, elevated blood pressure, waist circumference, CRP or HbA1c), and (2) poor diet quality (lowest fifth of DASH score distribution) and employee characteristics.ResultsEmployees recording a poor diet quality had greater odds (OR) of increased cardiometabolic risk independent of established risk factors (demographic, lifestyle and occupational) and BMI: men OR 1.50 (95% CI 1.12–2.00), women: OR 1.84 (95% CI 1.19–2.97) compared to the healthiest diet group. Characteristics associated with reporting a poor quality diet were employment in Scotland vs. England: men OR 1.88 (95% CI 1.53–2.32), women: OR 1.49 (95% CI 1.11–2.00), longer working hours (≥ 49 vs. ≤40 h) men: OR 1.53 (95% CI 1.21–1.92) women: OR 1.53 (95% CI 1.12–2.09). For men, job strain (high vs. low) was associated with reporting a poor diet quality OR 1.66 (95% CI 1.30–2.12).ConclusionsThe general population disparities in diet quality between England and Scotland were reflected in British police employees. The association of longer working hours and job strain with diet quality supports the targeting of workplace nutritional interventions.

  • Journal article
    Hoyles L, Jiménez-Pranteda MJ, Chilloux J, Brial F, Myridakis A, Aranias T, Magnan C, Gibson GR, Sanderson JD, Nicholson JK, Gauguier D, McCartney AL, Dumas MEet al., 2018,

    Metabolic retroconversion of trimethylamine N-oxide and the gut microbiota

    , Microbiome, Vol: 6, ISSN: 2049-2618

    Background:The dietary methylamines choline, carnitine, and phosphatidylcholine are used by the gut microbiota to produce a range of metabolites, including trimethylamine (TMA). However, little is known about the use of trimethylamine N-oxide (TMAO) by this consortium of microbes.Results:A feeding study using deuterated TMAO in C57BL6/J mice demonstrated microbial conversion of TMAO to TMA, with uptake of TMA into the bloodstream and its conversion to TMAO. Microbial activity necessary to convert TMAO to TMA was suppressed in antibiotic-treated mice, with deuterated TMAO being taken up directly into the bloodstream. In batch-culture fermentation systems inoculated with human faeces, growth of Enterobacteriaceae was stimulated in the presence of TMAO. Human-derived faecal and caecal bacteria (n = 66 isolates) were screened on solid and liquid media for their ability to use TMAO, with metabolites in spent media analysed by 1H-NMR. As with the in vitro fermentation experiments, TMAO stimulated the growth of Enterobacteriaceae; these bacteria produced most TMA from TMAO. Caecal/small intestinal isolates of Escherichia coli produced more TMA from TMAO than their faecal counterparts. Lactic acid bacteria produced increased amounts of lactate when grown in the presence of TMAO but did not produce large amounts of TMA. Clostridia (sensu stricto), bifidobacteria, and coriobacteria were significantly correlated with TMA production in the mixed fermentation system but did not produce notable quantities of TMA from TMAO in pure culture.Conclusions:Reduction of TMAO by the gut microbiota (predominantly Enterobacteriaceae) to TMA followed by host uptake of TMA into the bloodstream from the intestine and its conversion back to TMAO by host hepatic enzymes is an example of metabolic retroconversion. TMAO influences microbial metabolism depending on isolation source and taxon of gut bacterium. Correlation of metabolomic and abundance data from mixed microbiota fermenta

  • Journal article
    Hoyles L, Snelling T, Umlai UK, Nicholson JK, Carding SR, Glen RC, McArthur Set al., 2018,

    Microbiome–host systems interactions: protective effects of propionate upon the blood–brain barrier

    , Microbiome, Vol: 6, ISSN: 2049-2618

    Background: Gut microbiota composition and function are symbiotically linked with host health, and altered in metabolic, inflammatory and neurodegenerative disorders. Three recognized mechanisms exist by which the microbiome influences the gut--brain axis: modification of autonomic/sensorimotor connections, immune activation, and neuroendocrine pathway regulation. We hypothesized interactions between circulating gut-derived microbial metabolites and the blood--brain barrier (BBB) also contribute to the gut--brain axis. Propionate, produced from dietary substrates by colonic bacteria, stimulates intestinal gluconeogenesis and is associated with reduced stress behaviours, but its potential endocrine role has not been addressed. Results: After demonstrating expression of the propionate receptor FFAR3 on human brain endothelium, we examined the impact of a physiologically relevant propionate concentration (1 μM) on BBB properties in vitro. Propionate inhibited pathways associated with non-specific microbial infections via a CD14-dependent mechanism, suppressed expression of LRP-1 and protected the BBB from oxidative stress via NRF2 (NFE2L2) signaling. Conclusions: Together, these results suggest gut-derived microbial metabolites interact with the BBB, representing a fourth facet of the gut--brain axis that warrants further attention.

  • Journal article
    Kaluarachchi M, Boulangé C, Karaman I, Lindon JC, Ebbels T, Elliott P, Tracy R, Olson NCet al., 2018,

    A comparison of human serum and plasma metabolites using untargeted 1H NMR spectroscopy and UPLC-MS

    , Metabolomics, Vol: 14, ISSN: 1573-3882

    Introduction:Differences in the metabolite profiles between serum and plasma are incompletely understood.Objectives:To evaluate metabolic profile differences between serum and plasma and among plasma sample subtypes.Methods:We analyzed serum, platelet rich plasma (PRP), platelet poor plasma (PPP), and platelet free plasma (PFP), collected from 8 non-fasting apparently healthy women, using untargeted standard 1D and CPMG 1H NMR and reverse phase and hydrophilic (HILIC) UPLC-MS. Differences between metabolic profiles were evaluated using validated principal component and orthogonal partial least squares discriminant analysis.ResultsExplorative analysis showed the main source of variation among samples was due to inter-individual differences with no grouping by sample type. After correcting for inter-individual differences, lipoproteins, lipids in VLDL/LDL, lactate, glutamine, and glucose were found to discriminate serum from plasma in NMR analyses. In UPLC-MS analyses, lysophosphatidylethanolamine (lysoPE)(18:0) and lysophosphatidic acid(20:0) were higher in serum, and phosphatidylcholines (PC)(16:1/18:2, 20:3/18:0, O-20:0/22:4), lysoPC(16:0), PE(O-18:2/20:4), sphingomyelin(18:0/22:0), and linoleic acid were lower. In plasma subtype analyses, isoleucine, leucine, valine, phenylalanine, glutamate, and pyruvate were higher among PRP samples compared with PPP and PFP by NMR while lipids in VLDL/LDL, citrate, and glutamine were lower. By UPLC-MS, PE(18:0/18:2) and PC(P-16:0/20:4) were higher in PRP compared with PFP samples.Conclusions:Correction for inter-individual variation was required to detect metabolite differences between serum and plasma. Our results suggest the potential importance of inter-individual effects and sample type on the results from serum and plasma metabolic phenotyping studies.

  • Journal article
    Posma JM, Garcia Perez I, Ebbels TMD, Lindon JC, Stamler J, Elliott P, Holmes E, Nicholson Jet al., 2018,

    Optimized phenotypic biomarker discovery and confounder elimination via covariate-adjusted projection to latent structures from metabolic spectroscopy data

    , Journal of Proteome Research, Vol: 17, Pages: 1586-1595, ISSN: 1535-3893

    Metabolism is altered by genetics, diet, disease status, environment and many other factors. Modelling either one of these is often done without considering the effects of the other covariates. Attributing differences in metabolic profile to one of these factors needs to be done while controlling for the metabolic influence of the rest. We describe here a data analysis framework and novel confounder-adjustment algorithm for multivariate analysis of metabolic profiling data. Using simulated data we show that similar numbers of true associations and significantly less false positives are found compared to other commonly used methods. Covariate-Adjusted Projections to Latent Structures (CA-PLS) is exemplified here using a large-scale metabolic phenotyping study of two Chinese populations at different risks for cardiovascular disease. Using CA-PLS we find that some previously reported differences are actually associated with external factors and discover a number of previously unreported biomarkers linked to different metabolic pathways. CA-PLS can be applied to any multivariate data where confounding may be an issue and the confounder-adjustment procedure is translatable to other multivariate regression techniques.

  • Journal article
    Sung YJ, Lehne B, Scott WR, Sever P, Chambers J, Froguel P, Kooner JS, Scott J, Elliott P, Chasman DIet al., 2018,

    A large-scale multi-ancestry genome-wide study accounting for smoking bahavior identifies multiple genome-wide significant loci for systolic and diastolic blood pressure

    , American Journal of Human Genetics, Vol: 102, Pages: 375-400, ISSN: 0002-9297

    Genome-wide association analysis advanced understanding of blood pressure (BP), a major risk factor for vascular conditions such as coronary heart disease and stroke. Accounting for smoking behavior may help identify novel BP loci and extend our knowledge of its genetic architecture. We performed genome-wide association meta-analyses of systolic and diastolic BP incorporating gene-smoking interactionsin 610,091 individuals. Stage 1 analysis examined ~18.8 million SNPs and small insertion/deletion variants in 129,913 individuals from four ancestries (European, African, Asian, and Hispanic) with follow-upanalysis of promising variants in 480,178 additional individuals from five ancestries. Weidentified 15 new loci that were genome-wide significant (P < 5×10-8) in Stage 1 and formally replicated in Stage 2. A combined Stage 1 and 2 meta-analysis identified 66 additional genome-wide significant loci ( 13, 35, and 18 loci in European, African and trans-ancestry, respectively). A total of 56 known BP loci were also identified by our results (P < 5×10-8).O f the newly identified loci, 10 showed significant interaction with smoking status, but none of them were replicated in Stage 2. Several loci were identified in African ancestry, highlighting the importance of genetic studies in diverse populations. The identified loci show strong evidence for regulatory features and support shared pathophysiology with cardiometabolic and addiction traits. They also highlight a role in BP regulation for biological candidates such as modulators of vascular structure and function (CDKN1B, BCAR1-CFDP1, PXDN, EEA1), ciliopathies(SDCCAG8,RPGRIP1L), telomere maintenance (TNKS, PINX1, AKTIP), and central dopaminergic signaling (MSRA, EBF2)

  • Journal article
    Pazoki R, Dehghan A, Evangelou E, Warren H, Gao H, Caulfield M, Elliott P, Tzoulaki Iet al., 2018,

    Genetic predisposition to high blood pressure and lifestyle factors. Associations with midlife blood pressure levels and cardiovascular events

    , Circulation, Vol: 137, Pages: 653-661, ISSN: 0009-7322

    Background:High blood pressure (BP) is a major risk factor for cardiovascular diseases (CVDs), the leading cause of mortality worldwide. Both heritable and lifestyle risk factors contribute to elevated BP levels. We aimed to investigate the extent to which lifestyle factors could offset the effect of an adverse BP genetic profile and its effect on CVD risk.Methods:We constructed a genetic risk score for high BP by using 314 published BP loci in 277 005 individuals without previous CVD from the UK Biobank study, a prospective cohort of individuals aged 40 to 69 years, with a median of 6.11 years of follow-up. We scored participants according to their lifestyle factors including body mass index, healthy diet, sedentary lifestyle, alcohol consumption, smoking, and urinary sodium excretion levels measured at recruitment. We examined the association between tertiles of genetic risk and tertiles of lifestyle score with BP levels and incident CVD by using linear regression and Cox regression models, respectively.Results:Healthy lifestyle score was strongly associated with BP (P<10–320) for systolic and diastolic BP and CVD events regardless of the underlying BP genetic risk. Participants with a favorable in comparison with an unfavorable lifestyle (bottom versus top tertile lifestyle score) had 4.9, 4.3, and 4.1 mm Hg lower systolic BP in low, middle, and high genetic risk groups, respectively (P for interaction=0.0006). Similarly, favorable in comparison with unfavorable lifestyle showed 30%, 33%, and 31% lower risk of CVD among participants in low, middle, and high genetic risk groups, respectively (P for interaction=0.99).Conclusions:Our data further support population-wide efforts to lower BP in the population via lifestyle modification. The advantages and disadvantages of disclosing genetic predisposition to high BP for risk stratification needs careful evaluation.

  • Journal article
    Flannick J, Froguel P, Prokopenko I, Lehne B, Kooner JS, Chambers J, Scott J, Loh M, Elliott P, Zhang W, Scott W, Nagai Yet al., 2017,

    Sequence data and association statistics from 12,940 type 2 diabetes cases and controls

    , Scientific Data, Vol: 4, ISSN: 2052-4463

    To investigate the genetic basis of type 2 diabetes (T2D) to high resolution, the GoT2D and T2D-GENES consortia catalogued variation from whole-genome sequencing of 2,657 European individuals and exome sequencing of 12,940 individuals of multiple ancestries. Over 27M SNPs, indels, and structural variants were identified, including 99% of low-frequency (minor allele frequency [MAF] 0.1–5%) non-coding variants in the whole-genome sequenced individuals and 99.7% of low-frequency coding variants in the whole-exome sequenced individuals. Each variant was tested for association with T2D in the sequenced individuals, and, to increase power, most were tested in larger numbers of individuals (>80% of low-frequency coding variants in ~82 K Europeans via the exome chip, and ~90% of low-frequency non-coding variants in ~44 K Europeans via genotype imputation). The variants, genotypes, and association statistics from these analyses provide the largest reference to date of human genetic information relevant to T2D, for use in activities such as T2D-focused genotype imputation, functional characterization of variants or genes, and other novel analyses to detect associations between sequence variation and T2D.

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