Publications Benefiting from Airwave Study

  1. Oude Griep, L. M., Frost, G., Holmes, E., Wareham, N. J., Elliott, P. Systems Approach to Investigate the Role of Fruit and Vegetable Types on Vascular Function in Pre-Hypertensive Participants: Protocol and Baseline Characteristics of a Randomised Crossover Dietary Intervention. Nutrients, 16(17), 2923 (2024) https://pubmed.ncbi.nlm.nih.gov/39275238/
  2. Rahman, M.S., Harrison, E., Biggs, H., et al. Dynamics of cognitive variability with age and its genetic underpinning in NIHR BioResource Genes and Cognition cohort participants. Nat Med 30, 1739–1748. (2024) https://pubmed.ncbi.nlm.nih.gov/38745010/
  3. AlBaloul, A.H., Griffin, J., Kopytek, A., et al. Evidence of gene-nutrient interaction association with waist circumference, cross-sectional analysis.. BMC Public Health 24, 1842 (2024). (2024) https://pubmed.ncbi.nlm.nih.gov/38987751/
  4. Elliot R. Dryer-Beers, et al. Higher dietary polyphenol intake is associated with lower blood inflammatory markers. The Journal of Nutrition,. (2024) https://pubmed.ncbi.nlm.nih.gov/38740187/
  5. Parkes S, et al. Sickness absence and associations with sociodemographic factors, health risk behaviours, occupational stressors and adverse mental health in 40,343 UK police employees. Epidemiol Psychiatr Sci. 2024 May 7;33:e26.. (2024) https://pubmed.ncbi.nlm.nih.gov/38712441/
  6. Jorge Domínguez-Barragán, et al. Blood DNA methylation signature of diet quality and association with cardiometabolic traits. Eur J Prev Cardiol. 2024 Jan 25;31(2):191-202. (2024) https://pubmed.ncbi.nlm.nih.gov/37793095/
  7. Aljuraiban GS, et al. Associations of Systematic Inflammatory Markers with Diet Quality, Blood Pressure, and Obesity in the AIRWAVE Health Monitoring Study. J Inflamm Res. 2024;17:3129-3141. (2024) https://pubmed.ncbi.nlm.nih.gov/38784102/
  8. Lewins, MM. Identifying genomic and phenotypic risks factors for the clinical progression of depressive symptoms. Edinburgh Research Archive. (2024) https://era.ed.ac.uk/handle/1842/41387
  9. Freire-Aradas, A., et al. Inference of tobacco and alcohol consumption habits from DNA methylation analysis of blood. Forensic Sci Int Genet. 2024 Jan 28;70:103022.. (2024) https://pubmed.ncbi.nlm.nih.gov/38309257/
  10. Aljuraiban GS, et al. Lifestyle Score and Risk of Hypertension in the Airwave Health Monitoring Study of British Police Force Employees. Int J Environ Res Public Health. 2023 Feb 24;20(5):4029. (2023) https://pubmed.ncbi.nlm.nih.gov/36901040/
  11. O’Farrell, F., Aleyakpo, B., Mustafa, R., et al. Evidence for involvement of the alcohol consumption WDPCP gene in lipid metabolism, and liver cirrhosis. Sci Rep 13, 20616. (2023) https://pubmed.ncbi.nlm.nih.gov/37996473/
  12. Huffman JE, et al. Whole genome analysis of plasma fibrinogen reveals population-differentiated genetic regulators with putative liver roles. . (2023) https://pubmed.ncbi.nlm.nih.gov/37398003/
  13. Hahn, J., et al. DNA methylation analysis is used to identify novel genetic loci associated with circulating fibrinogen levels in blood. J Thromb Haemost. 2023 May;21(5):1135-1147. (2023) https://pubmed.ncbi.nlm.nih.gov/36716967/
  14. Aljuraiban, G. S., et al. Evaluation of lifestyle scores in relation to blood pressure in young and older adults of the airwave health monitoring study of British police force employees. Proceedings of the Nutrition Society 82.OCE1 (2023): E14. (2023) https://www.cambridge.org/core/journals/proceedings-of-the-nutrition-society/article/evaluation-of-lifestyle-scores-in-relation-to-blood-pressure-in-young-and-older-adults-of-the-airwave-health-monitoring-study-of-british-police-force-employees/4D2055A067CE603D4081C400D6B22433#article
  15. Dryer-Beers, Elliot R., et al. Polyphenol intake beneficially associates with inflammation and cognitive performance, and is mediated by MRI-derived features. Alzheimer's & Dementia 19 (2023): e079646.. (2023) https://alz-journals.onlinelibrary.wiley.com/doi/abs/10.1002/alz.079646
  16. Stebbing, J., et al. Comparison of phenomics and cfDNA in a large breast screening population: the Breast Screening and Monitoring Study (BSMS). Oncogene 42, 825–832. (2023) https://pubmed.ncbi.nlm.nih.gov/36693953/
  17. Kojouri, M., et al. Metabolome-wide association study on physical activity. Sci Rep 13, 2374. (2023) https://pubmed.ncbi.nlm.nih.gov/36759570/
  18. Trompeter N, et al. Primary Care Consultations Among UK Police Officers and Staff: Links With Adverse Mental Health and Job Strain. J Occup Environ Med. 2023 Jun 1;65(6):502-509. (2023) https://pubmed.ncbi.nlm.nih.gov/36843096/
  19. Dehghan, A., et al. Metabolome-wide association study on ABCA7 indicates a role of ceramide metabolism in Alzheimer's disease. PNAS Vol 119 (43) e2206083119. (2022) https://pubmed.ncbi.nlm.nih.gov/36269859/
  20. Garcia-Segura, M. E., et al. Pathway-based integration of multi-omics data reveals lipidomics alterations validated in an Alzheimer´s Disease mouse model and risk loci carriers. Journal of Neurochemistry, 164, 57– 76. (2022) https://pubmed.ncbi.nlm.nih.gov/36326588/
  21. Winkler, T.W., Rasheed, H., Teumer, A., et al. Differential and shared genetic effects on kidney function between diabetic and non-diabetic individuals. Commun Biol 5, 580 (2022). (2022) https://pubmed.ncbi.nlm.nih.gov/35697829/
  22. Said, S., Pazoki, R., Karhunen, V., et al. Genetic analysis of over half a million people characterises C-reactive protein loci. Nat Commun 13, 2198 (2022).. (2022) https://pubmed.ncbi.nlm.nih.gov/35459240/
  23. Rosadas, C., et al. Detection and quantification of antibody to SARS CoV 2 receptor binding domain provides enhanced sensitivity, specificity and utility. J Virol Methods.; 302:114475. (2022) https://pubmed.ncbi.nlm.nih.gov/35077719/
  24. Khan, M., et al. Simple, sensitive, specific self-sampling assay secures SARS-CoV-2 antibody signals in sero-prevalence and post-vaccine studies. Scientific Reports, 12(1):1885. (2022) https://pubmed.ncbi.nlm.nih.gov/35115570/
  25. Wielscher, M., et al. DNA methylation signature of chronic low-grade inflammation and its role in cardio-respiratory diseases. Nature Comms, 13(1), 2408. (2022) https://pubmed.ncbi.nlm.nih.gov/35504910/
  26. Irizar, P, et al. A latent class analysis of health risk behaviours in the UK Police Service and their associations with mental health and job strain. BMC Psychiatry. 2022 Jun 24;22(1):426. (2022) https://pubmed.ncbi.nlm.nih.gov/35751116/
  27. Irizar, P, et al. A comparison of probable post-traumatic stress disorder and alcohol consumption among active female members of the UK Police Service and UK Armed Forces. Soc Psychiatry Psychiatr Epidemiol. 2022 Sep 7. Epub ahead of print. PMID: 36071141. (2022) https://pubmed.ncbi.nlm.nih.gov/36071141/
  28. van Vliet, N. A., et al. Higher thyrotropin leads to unfavorable lipid profile and somewhat higher cardiovascular disease risk: evidence from multi-cohort Mendelian randomization and metabolomic profiling. BMC Medicine, 19(1), 266. (2021) https://pubmed.ncbi.nlm.nih.gov/34727949/
  29. Davies, B., et al. Acceptability, Usability, and Performance of Lateral Flow Immunoassay Tests for Severe Acute Respiratory Syndrome Coronavirus 2 Antibodies: REACT-2 Study of Self-Testing in Non-healthcare Key Workers. Open Forum Infectious Diseases, Volume 8, Issue 11, November 2021. (2021) https://pubmed.ncbi.nlm.nih.gov/34796246/
  30. Vasiliki Lagou, et al. Random glucose GWAS in 476,326 individuals provides insights into diabetes pathophysiology, complications and treatment stratification. . (2021) https://pubmed.ncbi.nlm.nih.gov/37679419/
  31. Sanjay Jain, et al. A new combination testing methodology to identify accurate and economical point-of-care testing strategies. . (2021) https://doi.org/10.1101/2021.06.15.21257351
  32. Karabegovic, I., Portilla-Fernandez, E., Li, Y., et al. Epigenome-wide association meta-analysis of DNA methylation with coffee and tea consumption. Nat Commun 12, 2830 (2021).. (2021) https://pubmed.ncbi.nlm.nih.gov/33990564/
  33. Moshe M, et al. SARS-CoV-2 lateral flow assays for possible use in covid-19 seroprevalence surveys (REACT 2): diagnostic accuracy study. Br Med J, 37:n423. (2021) https://pubmed.ncbi.nlm.nih.gov/33653694/
  34. Irizar, P, et al. Probable post-traumatic stress disorder and harmful alcohol use among male members of the British Police Forces and the British Armed Forces: a comparative study. Eur J Psychotraumatol 12, 1891734. (2021) https://pubmed.ncbi.nlm.nih.gov/33968324/
  35. Pazoki R, et al. Genetic analysis in European ancestry individuals identifies 517 loci associated with liver enzymes. Nat Commun. 2021 May 10;12(1):2579. (2021) https://pubmed.ncbi.nlm.nih.gov/33972514/
  36. Yang JJ, et al. Circulating trimethylamine N-oxide in association with diet and cardiometabolic biomarkers: an international pooled analysis. Am J Clin Nutr. 2021 May 8;113(5):1145-1156. (2021) https://pubmed.ncbi.nlm.nih.gov/33826706/
  37. McCartney, D. L., et al. Genome-wide association studies identify 137 genetic loci for DNA methylation biomarkers of aging. Genome Biol, 22(1): 194. (2021) https://pubmed.ncbi.nlm.nih.gov/34187551/
  38. Griffin J, et al. Effect of ultraprocessed food intake on cardiometabolic risk is mediated by diet quality: a cross-sectional study. BMJ Nutr Prev Health. 2021 Apr 7;4(1):174-180. (2021) https://pubmed.ncbi.nlm.nih.gov/34308125/
  39. Irizar, P, et al. The prevalence of hazardous and harmful drinking in the UK Police Service, and their co-occurrence with job strain and mental health problems. Epidemiol Psychiatr Sci. 2021 Jun 21;30:e51. (2021) https://pubmed.ncbi.nlm.nih.gov/34402422/
  40. Chen MH, et al. Trans-ethnic and Ancestry-Specific Blood-Cell Genetics in 746,667 Individuals from 5 Global Populations. Cell. Sep 3;182(5):1198-1213.e14 (2020). (2020) https://pubmed.ncbi.nlm.nih.gov/32888493/
  41. Koychev I, Young S, Holve H, et al. Dementias Platform UK Clinical Studies and Great Minds Register: protocol of a targeted brain health studies recontact database. BMJ Open 2020;10:e040766. (2020) https://pubmed.ncbi.nlm.nih.gov/33247021/
  42. Surendran, P., Feofanova, E.V., Lahrouchi, N., et al. Discovery of rare variants associated with blood pressure regulation through meta-analysis of 1.3 million individuals. Nat Genet 52, 1314–1332 (2020). (2020) https://pubmed.ncbi.nlm.nih.gov/33230300/
  43. Vuckovic D, et al. The Polygenic and Monogenic Basis of Blood Traits and Diseases. Cell. 2020 Sep 3;182(5):1214-1231.e11. (2020) https://pubmed.ncbi.nlm.nih.gov/32888494/
  44. Bauermeister S, et al. The Dementias Platform UK (DPUK Data Portal). Eur J Epidemiol. Apr 23.. (2020) https://pubmed.ncbi.nlm.nih.gov/32328990/
  45. Hunt, L. C., et al. Quantifying Diet Intake and Its Association with Cardiometabolic Risk in the UK Airwave Health Monitoring Study: A Data-Driven Approach. Nutrients. 2020 Apr 22;12(4):1170. (2020) https://pubmed.ncbi.nlm.nih.gov/32331378/
  46. Robinson, O., et al. Determinants of accelerated metabolomic and epigenetic aging in a UK cohort. Aging Cell 19, e13149. (2020) https://pubmed.ncbi.nlm.nih.gov/32363781/
  47. de Las Fuentes, L., et al. Gene-educational attainment interactions in a multi-ancestry genome-wide meta-analysis identify novel blood pressure loci. Mol Psychiatry. 2021 Jun;26(6):2111-2125. (2020) https://pubmed.ncbi.nlm.nih.gov/32372009/
  48. Stevelink, S. A. M., et al. Probable PTSD, depression and anxiety in 40,299 UK police officers and staff: Prevalence, risk factors and associations with blood pressure. PLoS One 15, e0240902. (2020) https://pubmed.ncbi.nlm.nih.gov/33180769/
  49. Noordam R, et al. Multi-ancestry sleep-by-SNP interaction analysis in 126,926 individuals reveals lipid loci stratified by sleep duration. Nat Commun. Nov 12; 10(1):5121. (2019) https://pubmed.ncbi.nlm.nih.gov/31719535/
  50. Justice AE, et al. Protein-coding variants implicate novel genes related to lipid homeostasis contributing to body-fat distribution. Nat Genet. 2019 Mar;51(3):452-469. (2019) https://pubmed.ncbi.nlm.nih.gov/30778226/
  51. Gao, H., et al. Personal radio use and cancer risks among 48,518 British police officers and staff from the Airwave Health Monitoring Study. Br J Cancer 120, 375-378. (2019) https://www.ncbi.nlm.nih.gov/pubmed/30585256/
  52. Kraja, A. T., et al. Associations of Mitochondrial and Nuclear Mitochondrial Variants and Genes with Seven Metabolic Traits. Am J Hum Genet 104, 112-138. (2019) https://www.ncbi.nlm.nih.gov/pubmed/30595373/
  53. Erzurumluoglu AM, et al. Meta-analysis of up to 622,409 individuals identifies 40 novel smoking behaviour associated genetic loci. Mol Psychiatry 25, 2392–2409 (2020). (2019) https://pubmed.ncbi.nlm.nih.gov/30617275/
  54. Kilpeläinen TO, et al. Multi-ancestry study of blood lipid levels identifies four loci interacting with physical activity. Nat Commun. Jan 22;10(1):376. (2019) https://pubmed.ncbi.nlm.nih.gov/30670697/
  55. Bentley, A. R., et al. Multi-ancestry genome-wide gene-smoking interaction study of 387,272 individuals identifies new loci associated with serum lipids. Nat Genet 51, 636-648. (2019) https://www.ncbi.nlm.nih.gov/pubmed/30926973/
  56. Elliott, P., et al. Use of TETRA personal radios and sickness absence in the Airwave Health Monitoring Study of the British police forces. Environ Res 175, 148-155. (2019) https://www.ncbi.nlm.nih.gov/pubmed/31125718/
  57. Sung, Y. J., et al. A multi-ancestry genome-wide study incorporating gene-smoking interactions identifies multiple new loci for pulse pressure and mean arterial pressure. Hum Mol Genet. 2019 Aug 1;28(15):2615-2633. (2019) https://pubmed.ncbi.nlm.nih.gov/31127295/
  58. Wuttke, M., et al. A catalog of genetic loci associated with kidney function from analyses of a million individuals. Nat Genet 51, 957-972. (2019) https://www.ncbi.nlm.nih.gov/pubmed/31152163/
  59. Yu B, et al. The Consortium of Metabolomics Studies (COMETS): Metabolomics in 47 Prospective Cohort Studies. Am J Epidemiol. Jun 1;188(6):991-1012. (2019) https://pubmed.ncbi.nlm.nih.gov/31155658/
  60. Evangelou, E., et al. New alcohol-related genes suggest shared genetic mechanisms with neuropsychiatric disorders. Nat Hum Behav, 2019 Jul 29. (2019) https://www.ncbi.nlm.nih.gov/pubmed/31358974/
  61. Gibson, R., et al. Intakes and Food Sources of Dietary Fibre and Their Associations with Measures of Body Composition and Inflammation in UK Adults: Cross-Sectional Analysis of the Airwave Health Monitoring Study. Nutrients. 2019 Aug 8;11(8):1839. (2019) https://pubmed.ncbi.nlm.nih.gov/31398891/
  62. Eriksen, R., et al. Gene-diet quality interactions on haemoglobin A1c and type 2 diabetes risk: The Airwave Health Monitoring Study. Endocrinol Diabetes Metab 2, e00074. (2019) https://pubmed.ncbi.nlm.nih.gov/31592155/
  63. Clark, D. W., et al. Associations of autozygosity with a broad range of human phenotypes. Nat Commun 10, 4957. (2019) https://pubmed.ncbi.nlm.nih.gov/31673082/
  64. Pazoki R, et al. GWAS for urinary sodium and potassium excretion highlights pathways shared with cardiovascular traits. Nat Commun. 2019 Aug 13;10(1):3653. (2019) https://pubmed.ncbi.nlm.nih.gov/31409800/
  65. Evangelos Evangelou, et al. Genome-wide association and functional studies identify 46 novel loci for alcohol consumption and suggest common genetic mechanisms with neuropsychiatric disorders. . (2018) https://doi.org/10.1101/453332
  66. Gibson, R., et al. 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. Eur J Nutr 57, 2913-2926. (2018) https://www.ncbi.nlm.nih.gov/pubmed/29098424/
  67. Turcot, V., et al. Protein-altering variants associated with body mass index implicate pathways that control energy intake and expenditure in obesity. Nat Genet 50, 26-41. (2018) https://www.ncbi.nlm.nih.gov/pubmed/29273807/
  68. Eriksen, R., et al. Nutrient profiling and adherence to components of the UK national dietary guidelines association with metabolic risk factors for CVD and diabetes: Airwave Health Monitoring Study. Br J Nutr 119, 695-705. (2018) https://www.ncbi.nlm.nih.gov/pubmed/29455679/
  69. Sung, Y. J., et al. A Large-Scale Multi-ancestry Genome-wide Study Accounting for Smoking Behavior Identifies Multiple Significant Loci for Blood Pressure. Am J Hum Genet. 2018 Mar 1;102(3):375-400. (2018) https://pubmed.ncbi.nlm.nih.gov/29455858/
  70. Davies, G., et al. Study of 300,486 individuals identifies 148 independent genetic loci influencing general cognitive function. Nat Commun 9, 2098. (2018) https://www.ncbi.nlm.nih.gov/pubmed/29844566/
  71. Vergnaud, A. C., et al. Estimation of TETRA radio use in the Airwave Health Monitoring Study of the British police forces. Environ Res 167, 169-174. (2018) https://www.ncbi.nlm.nih.gov/pubmed/30014898/
  72. Evangelou, E., et al. Genetic analysis of over 1 million people identifies 535 new loci associated with blood pressure traits. Nat Genet. 2018 Oct;50(10):1412-1425. (2018) https://pubmed.ncbi.nlm.nih.gov/30224653/
  73. O. Robinson, et al. Metabolomic aging and its association with depression in a UK cohort. Revue d'Épidémiologie et de Santé Publique, Volume 66, Supplement 5. (2018) https://www.sciencedirect.com/science/article/abs/pii/S0398762018308228/
  74. Warren, H. R., et al. Genome-wide association analysis identifies novel blood pressure loci and offers biological insights into cardiovascular risk. Nat Genet 49, 403-415. (2017) https://www.ncbi.nlm.nih.gov/pubmed/28135244/
  75. Marouli, E., et al. Rare and low-frequency coding variants alter human adult height. Nature 542, 186-190. (2017) https://www.ncbi.nlm.nih.gov/pubmed/28146470/
  76. Gibson, R., et al. 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 7, e012927. (2017) https://www.ncbi.nlm.nih.gov/pubmed/28377391/
  77. Wain, L. V., et al. Novel Blood Pressure Locus and Gene Discovery Using Genome-Wide Association Study and Expression Data Sets From Blood and the Kidney. Hypertension. 2017 Sep;70(3):e4-e19. (2017) https://pubmed.ncbi.nlm.nih.gov/28739976/
  78. van Leeuwen, E. M., et al. Meta-analysis of 49 549 individuals imputed with the 1000 Genomes Project reveals an exonic damaging variant in ANGPTL4 determining fasting TG levels. J Med Genet 53, 441-449. (2016) https://www.ncbi.nlm.nih.gov/pubmed/27036123/
  79. Blaise, B. J., et al. Power Analysis and Sample Size Determination in Metabolic Phenotyping. Anal Chem 88, 5179-5188. (2016) https://www.ncbi.nlm.nih.gov/pubmed/27116637/
  80. Vergnaud, A. C., et al. Validation of objective records and misreporting of personal radio use in a cohort of British Police forces (the Airwave Health Monitoring Study). Environ Res 148, 367-375. (2016) https://www.ncbi.nlm.nih.gov/pubmed/27131074/
  81. Chami, N., et al. Exome Genotyping Identifies Pleiotropic Variants Associated with Red Blood Cell Traits. Am J Hum Genet 99, 8-21. (2016) https://www.ncbi.nlm.nih.gov/pubmed/27346685/
  82. Eicher, J. D., et al. Platelet-Related Variants Identified by Exomechip Meta-analysis in 157,293 Individuals. Am J Hum Genet 99, 40-55. (2016) https://www.ncbi.nlm.nih.gov/pubmed/27346686/
  83. Tajuddin, S. M., et al. Large-Scale Exome-wide Association Analysis Identifies Loci for White Blood Cell Traits and Pleiotropy with Immune-Mediated Diseases. Am J Hum Genet 99, 22-39. (2016) https://www.ncbi.nlm.nih.gov/pubmed/27346689/
  84. Burgess, A. P., et al. Acute Exposure to Terrestrial Trunked Radio (TETRA) has effects on the electroencephalogram and electrocardiogram, consistent with vagal nerve stimulation. Environ Res 150, 461-469. (2016) https://www.ncbi.nlm.nih.gov/pubmed/27419367/
  85. Lewis, M. R., et al. Development and Application of Ultra-Performance Liquid Chromatography-TOF MS for Precision Large Scale Urinary Metabolic Phenotyping. Anal Chem 88, 9004-9013. (2016) https://www.ncbi.nlm.nih.gov/pubmed/27479709/
  86. Surendran, P., et al. Trans-ancestry meta-analyses identify rare and common variants associated with blood pressure and hypertension. Nat Genet 48, 1151-1161. (2016) https://www.ncbi.nlm.nih.gov/pubmed/27618447/
  87. Schumann, G., et al. KLB is associated with alcohol drinking, and its gene product beta-Klotho is necessary for FGF21 regulation of alcohol preference. Proc Natl Acad Sci U S A 113, 14372-14377. (2016) https://www.ncbi.nlm.nih.gov/pubmed/27911795/
  88. Gibson, R., et al. Sex differences in the relationship between work patterns and diet in British police force employees: a nested cross-sectional study. Proceedings of the Nutrition Society 75(Oce1): E20-E20. (2016) https://www.cambridge.org/core/journals/proceedings-of-the-nutrition-society/article/sex-differences-in-the-relationship-between-work-patterns-and-diet-in-british-police-force-employees-a-nested-crosssectional-study/C3995C962F118C27C100B786CCFA2111/
  89. Epstein, S., et al. Numerical assessment of the stiffness index. Conf Proc IEEE Eng Med Biol Soc 2014: 1969-1972. (2014) https://pubmed.ncbi.nlm.nih.gov/25570367/
  90. Elliott, P., et al. The Airwave Health Monitoring Study of police officers and staff in Great Britain: rationale, design and methods. Environ Res 134, 280-285. (2014) https://www.ncbi.nlm.nih.gov/pubmed/25194498/
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