Fenland study publications on the MRC Epidemiology Unit publications database.

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Selected Fenland Publications

• Carrasco-Zanini, J. et al. Proteomic signatures for identification of impaired glucose tolerance. Nature Medicine 10 November 2022.

• Braithwaite, V.S. et al. An investigation of factors affecting changes in health behaviours during the COVID-19 pandemic in a UK population-based cohort study, Public Health, Volume 212, November 2022, Pages 46-54

• Pietzner, M et al. ELF5 is a respiratory epithelial cell-specific risk gene for severe COVID-19. Nature Communications; 15 Aug 2022.

• Pietzner, M. et al. Mapping the proteo-genomic convergence of human diseases. Science; 14 October 2021.

• Pietzner M. et al. Genetic architecture of host proteins involved in SARS-CoV-2 infection. Nature Communications, 16 December 2020.

• Lotta, L.A. et al. Association of Genetic Variants Related to Gluteofemoral vs Abdominal Fat Distribution With Type 2 Diabetes, Coronary Disease, and Cardiovascular Risk Factors. JAMA. 320(24):2553-2563 (2018)

• Mackenbach, J.D. et al. Accessibility and Affordability of Supermarkets: Associations With the DASH Diet. Am J Prev Med S0749-3797(17)30138-1 (2017) PMID:28336352

• Lotta, L.A. et al. Integrative genomic analysis implicates limited peripheral adipose storage capacity in the pathogenesis of human insulin resistance. Nature Genetics 49(1):17-26 (2017) PMID:27841877

• Lotta, L.A. et al. Genetic Predisposition to an Impaired Metabolism of the Branched-Chain Amino Acids and Risk of Type 2 Diabetes: A Mendelian Randomisation Analysis. PLoS Med. 3(11):e1002179 (2016)

• Godino, J.G. et al. Lifestyle Advice Combined with Personalized Estimates of Genetic or Phenotypic Risk of Type 2 Diabetes, and Objectively Measured Physical Activity: A Randomized Controlled Trial. PLoS Med. 13(11):e1002185. (2016). PMID:27898672 PMC:5127499

• Forouhi, N.G. et al. Effects of vitamin D2 or D3 supplementation on glycaemic control and cardiometabolic risk among people at risk of type 2 diabetes: results of a randomized double-blind placebo-controlled trial. Diabetes Obes Metab. 18(4), 392-400 (2016) PMID:26700109

• Mackenbach, J.D. et al. Does the importance of dietary costs for fruit and vegetable intake vary by socioeconomic position? Br J Nutr. 114(9), 1464-1470 (2015) PMID:26353803 PMC:4657115.

• O’Connor, L. et al. The cross-sectional association between snacking behaviour and measures of adiposity: the Fenland Study, UK. Br J Nutr. 114(8), 1286-1293 (2015) PMID:26343512 PMC:4594050.

• Burgoine, T. et al. Associations between exposure to takeaway food outlets, takeaway food consumption, and body weight in Cambridgeshire, UK: population based, cross sectional study. BMJ (Clinical research ed.) 348, g1464 (2014) PMID:24625460 PMC:3953373.

• Godino, J. G. et al. Awareness of physical activity in healthy middle-aged adults: a cross-sectional study of associations with sociodemographic, biological, behavioural, and psychological factors. BMC public health 14, 421 (2014) PMID:24886612 PMC:4012086.

• Burgoine, T. & Monsivais, P. Characterising food environment exposure at home, at work, and along commuting journeys using data on adults in the UK. The international journal of behavioral nutrition and physical activity 10, 85 (2013) PMID:23806008 PMC:3720205.

• Godino, J. G. et al. Impact of personalised feedback about physical activity on change in objectively measured physical activity (the FAB study): a randomised controlled trial. PloS One 8, e75398 (2013) PMID:24066178 PMC:3774634.

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Other Fenland Publications

• Rennie K.L. et al. Engagement with mHealth COVID-19 digital biomarker measurements in a longitudinal cohort study: a mixed methods evaluation. J Med Internet Res. 2022 Oct 3.

• Pietzner, M. et al. Synergistic insights into human health from aptamer- and antibody-based 1 proteomic profiling. Nature Communications 24 November 2021.

• Lotta, L.A. et al. A cross-platform approach identifies genetic regulators of human metabolism and health. Nature Genetics, 07 January 2021.

• Trichia, E. et al.  Associations of types of dairy consumption with adiposity: cross-sectional findings from over 12 000 adults in the Fenland Study, UK. Br J Nutr. 122(8):928-935 (2019)

• Tong, T.Y.N. et al. A Combination of Metabolites Predicts Adherence to the Mediterranean Diet Pattern and Its Associations with Insulin Sensitivity and Lipid Homeostasis in the General Population: The Fenland Study, United Kingdom. J Nutr. pii: nxz263 (2019)

• Khalatbari-Soltani,  S. et al. The association between adherence to the Mediterranean diet and hepatic steatosis: cross-sectional analysis of two independent studies, the UK Fenland Study and the Swiss CoLaus Study. BMC Med. 17(1):19 (2019)

• Yu, D. et al. Total/high density lipoprotein cholesterol and cardiovascular disease (re)hospitalization nadir in type 2 diabetes. J Lipid Res. 59(9):1745-1750 (2018)

• Sanders F.W.B. et al. Hepatic steatosis risk is partly driven by increased de novo lipogenesis following carbohydrate consumption. Genome Biol. 19(1):79 (2018)

• Mills S. et al. Sociodemographic characteristics and frequency of consuming home-cooked meals and meals from out-of-home sources: cross-sectional analysis of a population-based cohort study. Public Health Nutr. 21(12):2255-2266. (2018)

• Tong T.Y.N. et al.  Dietary cost associated with adherence to the Mediterranean diet, and its variation by socio-economic factors in the UK Fenland Study. Br J Nutr. 119(6):685-694 (2018)

• Mytton O.T. et al. Associations of active commuting with body fat and visceral adipose tissue: A cross-sectional population based study in the UK. Prev Med. 106:86-93 (2018)

• Burgoine T. et al. Interplay of Socioeconomic Status and Supermarket Distance Is Associated with Excess Obesity Risk: A UK Cross-Sectional Study. Int J Environ Res Public Health. 14(11). pii: E1290 (2017)

• Penney T.L. et al. Utilization of Away-From-Home Food Establishments, Dietary Approaches to Stop Hypertension Dietary Pattern, and Obesity. Am J Prev Med. 53(5):e155-e163 (2017)

• Burgoine T. et al. Association between distance to nearest supermarket and provision of fruits and vegetables in English nurseries. Health Place. 46:229-233 (2017)

• Maguire E.R. et al. Does exposure to the food environment differ by socioeconomic position? Comparing area-based and person-centred metrics in the Fenland Study, UK. Int J Health Geogr. 16(1):33 (2017)

• Barrett P. et al. Sociodemographic, lifestyle and behavioural factors associated with consumption of sweetened beverages among adults in Cambridgeshire, UK: the Fenland Study. Public Health Nutr.  20(15): 2766-2777 (2017)

• Burgoine T. et al. Does neighborhood fast-food outlet exposure amplify inequalities in diet and obesity? A cross-sectional study. Am J Clin Nutr. 103(6):1540-7 (2016)

• Clifton, E.A. et al. Associations between body mass index-related genetic variants and adult body composition: The Fenland cohort study. Int. J. Obes. 41(4):613-619 (2016) PMID:28096530

• Coffee and Caffeine Genetics Consortium, et al. Genome-wide meta-analysis identifies six novel loci associated with habitual coffee consumption. Molecular Psychiatry 20 647-656 (2015) PMID:25288136 PMC: 4388784

• Swerdlow, D.I. et al. HMG-coenzyme A reductase inhibition, type 2 diabetes, and bodyweight: evidence from genetic analysis and randomised trials. Lancet 385(9965) (2015) PMID:25262344 PMC:4322187

• Wood, A.R. et al. Defining the role of common variation in the genomic and biological architecture of adult human height. Nature Genetics 46 (11) 1173-1186 (2015) PMID:25282103 PMC:4250049

• Locke, A.E. et al. Genetic studies of body mass index yield new insights for obesity biology Nature 518 (7538) 197-206 (2015) PMID:25673413 PMC:4382211

• Shungin, D. et al. New genetic loci link adipose and insulin biology to body fat distribution. Nature 518 (7538) 187-196 (2015) PMID:25673412 PMC:4338562

• Waterworth, D. M. et al. A low-frequency variant in MAPK14 provides mechanistic evidence of a link with myeloperoxidase: a prognostic cardiovascular risk marker. Journal of the American Heart Association 3 (2014) PMID:25164947

• Wijndaele, K. et al. Reliability and validity of a domain-specific last-7-day sedentary time questionnaire. Med Sci Sport Exer (2014) PMID:24492633 PMC:4047320

• van Vliet-Ostaptchouk, J. V. et al. Pleiotropic effects of obesity-susceptibility loci on metabolic traits: a meta-analysis of up to 37,874 individuals. Diabetologia 56, 2134-2146 (2013) PMID:23827965

• Tanaka, T. et al. Genome-wide meta-analysis of observational studies shows common genetic variants associated with macronutrient intake. The American journal of clinical nutrition 97, 1395-1402 (2013) PMID:23636237 PMC:3652928.

• Sofat, R. et al. Distribution and determinants of circulating complement factor H concentration determined by a high-throughput immunonephelometric assay. Journal of immunological methods 390, 63-73 (2013) PMID:23376722

• Smith, C. E. et al. Lipoprotein receptor-related protein 1 variants and dietary fatty acids: meta-analysis of European origin and African American studies. International journal of obesity (2005) 37, 1211-1220 (2013) PMID:23357958 PMC:3770755.

• Randall, J. C. et al. Sex-stratified genome-wide association studies including 270,000 individuals show sexual dimorphism in genetic loci for anthropometric traits. PLoS genetics 9, e1003500 (2013) PMID:23754948 PMC:3674993.

• Global Lipids Genetics Consortium et al. Discovery and refinement of loci associated with lipid levels. Nature genetics 45, 1274-1283 (2013) PMID:24097068 PMC:3838666.

• den Hoed, M. et al. Evaluation of common genetic variants identified by GWAS for early onset and morbid obesity in population-based samples. International journal of obesity (2005) 37, 191-196 (2013) PMID:22430306 PMC:3680864.

• den Hoed, M. et al. Identification of heart rate-associated loci and their effects on cardiac conduction and rhythm disorders. Nature genetics 45, 621-631 (2013) PMID:23583979 PMC:3696959.

• Berndt, S. I. et al. Genome-wide meta-analysis identifies 11 new loci for anthropometric traits and provides insights into genetic architecture. Nature genetics 45, 501-512 (2013) PMID:23563607 PMC:3973018.

• Ahmad, S. et al. Gene x physical activity interactions in obesity: combined analysis of 111,421 individuals of European ancestry. PLoS genetics 9, e1003607 (2013) PMID:23935507 PMC:3723486.

• Yang, J. et al. FTO genotype is associated with phenotypic variability of body mass index. Nature 490, 267-272 (2012) PMID:22982992 PMC:3564953.

• Scott, R. A. et al. Large-scale association analyses identify new loci influencing glycemic traits and provide insight into the underlying biological pathways. Nature genetics 44, 991-1005 (2012) PMID:22885924 PMC:3433394.

• Scott, R. A. et al. No interactions between previously associated 2-hour glucose gene variants and physical activity or BMI on 2-hour glucose levels. Diabetes 61, 1291-1296 (2012) PMID:22415877 PMC:3331745.

• Manning, A. K. et al. A genome-wide approach accounting for body mass index identifies genetic variants influencing fasting glycemic traits and insulin resistance. Nature genetics 44, 659-669 (2012) PMID:22581228 PMC:3613127.

• Interleukin-6 Receptor Mendelian Randomisation Analysis Consortium, Hingorani, A. D. & Casas, J. P. The interleukin-6 receptor as a target for prevention of coronary heart disease: a mendelian randomisation analysis. Lancet 379, 1214-1224 (2012) PMID:22421340 PMC:3316968.

• Godino, J. G. et al. Effect of communicating genetic and phenotypic risk for type 2 diabetes in combination with lifestyle advice on objectively measured physical activity: protocol of a randomised controlled trial. BMC public health 12, 444 (2012) PMID:22708638 PMC:3490832.

• Dastani, Z. et al. Novel loci for adiponectin levels and their influence on type 2 diabetes and metabolic traits: a multi-ethnic meta-analysis of 45,891 individuals. PLoS genetics 8, e1002607 (2012) PMID:22479202 PMC:3315470.

• Boraska, V. et al. Genome-wide meta-analysis of common variant differences between men and women. Human molecular genetics 21, 4805-4815 (2012) PMID:22843499 PMC:3471397.

• Wain, L. V. et al. Genome-wide association study identifies six new loci influencing pulse pressure and mean arterial pressure. Nature genetics 43, 1005-1011 (2011) PMID:21909110 PMC:3445021.

• Strawbridge, R. J. et al. Genome-wide association identifies nine common variants associated with fasting proinsulin levels and provides new insights into the pathophysiology of type 2 diabetes. Diabetes 60, 2624-2634 (2011) PMID:21873549 PMC:3178302.

• Schumann, G. et al. Genome-wide association and genetic functional studies identify autism susceptibility candidate 2 gene (AUTS2) in the regulation of alcohol consumption. Proceedings of the National Academy of Sciences of the United States of America 108, 7119-7124 (2011) PMID:21471458 PMC:3084048.

• Kilpelainen, T. O. et al. Obesity-susceptibility loci have a limited influence on birth weight: a meta-analysis of up to 28,219 individuals. The American journal of clinical nutrition 93, 851-860 (2011) PMID:21248185

• Kanoni, S. et al. Total zinc intake may modify the glucose-raising effect of a zinc transporter (SLC30A8) variant: a 14-cohort meta-analysis. Diabetes 60, 2407-2416 (2011) PMID:21810599 PMC:3161318.

• International Consortium for Blood Pressure Genome-Wide Association et al. Genetic variants in novel pathways influence blood pressure and cardiovascular disease risk. Nature 478, 103-109 (2011) PMID:21909115 PMC:3340926.

• De Silva, N. M. et al. Mendelian randomization studies do not support a role for raised circulating triglyceride levels influencing type 2 diabetes, glucose levels, or insulin resistance. Diabetes 60, 1008-1018 (2011) PMID:21282362 PMC:3046819.

• Chambers, J. C. et al. Genome-wide association study identifies loci influencing concentrations of liver enzymes in plasma. Nature genetics 43, 1131-1138 (2011) PMID:22001757 PMC:3482372.

• Boger, C. A. et al. CUBN is a gene locus for albuminuria. Journal of the American Society of Nephrology 22, 555-570 (2011) PMID:21355061 PMC:3060449.

• Watkinson, C. et al. Randomised controlled trial of the effects of physical activity feedback on awareness and behaviour in UK adults: the FAB study protocol [ISRCTN92551397]. BMC public health 10, 144 (2010) PMID:20298560 PMC:2859395.

• Teslovich, T. M. et al. Biological, clinical and population relevance of 95 loci for blood lipids. Nature 466, 707-713 (2010) PMID:20686565 PMC:3039276.

• Speliotes, E. K. et al. Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. Nature genetics 42, 937-948 (2010) PMID:20935630 PMC:3014648.

• Soranzo, N. et al. Common variants at 10 genomic loci influence hemoglobin A(1)(C) levels via glycemic and nonglycemic pathways. Diabetes 59, 3229-3239 (2010) PMID:20858683 PMC:2992787.

• Rolfe Ede, L. et al. Association between birth weight and visceral fat in adults. The American journal of clinical nutrition 92, 347-352 (2010) PMID:20519560

• Padmanabhan, S. et al. Genome-wide association study of blood pressure extremes identifies variant near UMOD associated with hypertension. PLoS genetics 6, e1001177 (2010) PMID:21082022 PMC:2965757.

• Heid, I. M. et al. Meta-analysis identifies 13 new loci associated with waist-hip ratio and reveals sexual dimorphism in the genetic basis of fat distribution. Nature genetics 42, 949-960 (2010) PMID:20935629 PMC:3000924.

• Dupuis, J. et al. New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk. Nature genetics 42, 105-116 (2010) PMID:20081858 PMC:3018764.

• Chambers, J. C. et al. Genetic loci influencing kidney function and chronic kidney disease. Nature genetics 42, 373-375 (2010) PMID:20383145 PMC:3748585.