Dairy Product Intake and Risk of Type 2 Diabetes in EPIC-InterAct: A Mendelian Randomization Study

Publication: Diabetes Care

Vissers LET, Sluijs I, van der Schouw YT, Forouhi NG, Imamura F, Burgess S, et al.

6 February 2019


Eating healthily on a daily basis is a major step to prevent development of type 2 diabetes. Higher intake of dairy products has been associated with a lower risk of diabetes in a meta-analysis of observational studies. Yogurt and cheese intake particularly were associated with lower diabetes risk, whereas milk intake was not, with substantial heterogeneity for most dairy products.

However, potential confounding and reverse causation cannot be excluded. Owing to these limitations, the causal role of dairy products in diabetes prevention remains debatable.

The relationship between dairy products and risk of diabetes could be investigated by applying a Mendelian randomization (MR) approach, using genetic variability in the MCM6 gene associated with lactase persistence (LP) in adults as an instrumental variable (IV).

Lactase is necessary to break down the sugars that are found in dairy products, i.e., lactose. Single nucleotide polymorphisms (SNPs) in the MCM6 region have been associated with LP (6). rs4988235 (LCT-12910C>T) has been associated with LP in European populations and has been associated with a higher intake of milk in European cohorts, albeit not in all.

Previous MR studies reported no association between LP-associated milk intake and diabetes. However, variation in the MCM6 gene is likely to lead to population stratification, which would introduce bias to an MR analysis, and previous MR studies did not sufficiently adjust for population substructure. Also, previous studies did not investigate whether rs4988235 was specifically associated with dairy product intake after adjusting for population substructure.

We therefore investigated whether rs4988235 associated with intake of dairy products and other foods in a pan-European study in eight countries with different dietary habits. We adjusted for genetic principal components (PCs) and study center to adjust for population substructure (16). Next, we used rs4988235 in an IV analysis to investigate whether there is a causal relationship between the LP-associated exposure and risk of diabetes.

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Thyroid Function and Dysfunction in Relation to 16 Cardiovascular Diseases

Publication: Circulation: Genomic and Precision Medicine.

Larsson SC, Allara E, Mason AM, Michaelsson K, Burgess S.

31 January 2019

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Interleukin-6 Receptor Signaling and Abdominal Aortic Aneurysm Growth Rates

Publication: Genomic and Precision Medicine

Paige E, Clement M, Lareyre F, Sweeting M, Raffort J, Grenier C, et al.

18 January 2019

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Equalization of four cardiovascular risk algorithms after systematic recalibration: individual-participant meta-analysis of 86 prospective studies

Publication: Oxford Academic

Lisa Pennells, Stephen Kaptoge, AngelaWood, Mike Sweeting , Xiaohui Zhao, Ian White, Stephen Burgess, Peter Willeit, Thomas Bolton, Karel G.M. Moons, Yvonne T. van der Schouw, Randi Selmer, Kay-Tee Khaw, Vilmundur Gudnason, Gerd Assmann, Philippe Amouyel, Veikko Salomaa, Mika Kivimaki, Børge G. Nordestgaard, Michael J. Blaha, Lewis H. Kuller,
Hermann Brenner, Richard F. Gillum, Christa Meisinger, Ian Ford, MatthewW. Knuiman, Annika Rosengren, Debbie A. Lawlor, Henry Vo¨ lzke, Cyrus Cooper, Alejandro Marı´n Iba~nez, Edoardo Casiglia, Jussi Kauhanen, Jackie A. Cooper, Beatriz Rodriguez, Johan Sundstro¨m, Elizabeth Barrett-Connor, Rachel Dankner, Paul J. Nietert, KarinaW. Davidson, Robert B.Wallace, Dan G. Blazer, Cecilia Bjo¨ rkelund, Chiara Donfrancesco, Harlan M. Krumholz, Aulikki Nissinen, Barry R. Davis, Sean Coady, Peter H.Whincup, Torben Jørgensen, Pierre Ducimetiere, Maurizio Trevisan, Gunnar Engstro¨m, Carlos J. Crespo, TomW. Meade,  Marjolein Visser, Daan Kromhout, Stefan Kiechl, Makoto Daimon, Jackie F. Price, Agustin Go´mez de la Ca´mara, JWouter Jukema, Benoıˆt Lamarche, Altan Onat, Leon A. Simons, Maryam Kavousi, Yoav Ben-Shlomo, John Gallacher, Jacqueline M. Dekker, Hisatomi Arima, Nawar Shara, RobertW. Tipping, Ronan Roussel, Eric J Brunner, Wolfgang Koenig, Masaru Sakurai, Jelena Pavlovic, Ron T. Gansevoort, Dorothea Nagel, Uri Goldbourt, Elizabeth L.M. Barr, Luigi Palmieri, Inger Njølstad, Shinichi Sato,W.M. Monique Verschuren, Cherian V. Varghese, Ian Graham, Oyere Onuma, Philip Greenland, MarkWoodward, Majid Ezzati, Bruce M. Psaty, Naveed Sattar, Rod Jackson, Paul M. Ridker, Nancy R. Cook, Ralph B. D’Agostino, Sr, Simon G Thompson, John Danesh, Emanuele Di Angelantonio

22 November 2018

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Genomic Risk Prediction of Coronary Artery Disease in 480,000 Adults

Publication: Journal of the American College of Cardiology

Michael Inouye, Gad Abraham, Christopher P. Nelson, Angela M. Wood, Michael J. Sweeting, Frank Dudbridge, Florence Y. Lai, Stephen Kaptoge, Marta Brozynska, Tingting Wang, Shu Ye, Thomas R. Webb, Martin K. Rutter, Ioanna Tzoulaki,Riyaz S. Patel, Ruth J.F. Loos, Bernard Keavney, Harry Hemingway, John Thompson, Hugh Watkins, Panos Deloukas,Emanuele Di Angelantonio, Adam S. Butterworth, John Danesh, Nilesh J. Samani

8 October 2018


Summary:

Genetic factors have long been known to be major contributors of someone’s risk of developing coronary heart disease – the leading cause of heart attacks. Currently to identify those at risk doctors use scores based on lifestyle and clinical conditions associated with coronary heart disease such as cholesterol level, blood pressure, diabetes and smoking. But these scores are imprecise, age-dependent and miss a large proportion of people who appear ‘healthy’, but will still develop the disease.

The ‘big-data’ GRS technique takes into account 1.7 million genetic variants in a person’s DNA to calculate their underlying genetic risk for coronary heart disease.

The team analysed genomic data of nearly half a million people from the UK Biobank research project aged between 40-69 years. This included over 22,000 people who had coronary heart disease.

The GRS was better at predicting someone’s risk of developing heart disease than each of the classic risk factors for coronary heart disease alone. The ability of the GRS to predict coronary heart disease was also largely independent of these known risk factors. This showed that the genes which increase the risk of coronary heart disease don’t simply work by elevating blood pressure or cholesterol, for example.

People with a genomic risk score in the top 20 per cent of the population were over four-times more likely to develop coronary heart disease than someone with a genomic risk score in the bottom 20 per cent. Read the full press release

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Genome-wide mapping of plasma protein QTLs identifies putatively causal genes and pathways for cardiovascular disease

Publication: Nature Communications

Yao C, Chen G, Song C, Keefe J, Mendelson M, Huan T, et al. .

15 August 2018

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Analysis of clinical benefit, harms, and cost-effectiveness of screening women for abdominal aortic aneurysm

Publication: The Lancet

Michael J Sweeting, Katya L Masconi, PhD, Edmund Jones, PhD, Pinar Ulug, PhD, Matthew J Glover, MSc, Prof Jonathan A Michaels, MChir, Prof Matthew J Bown, MD, Prof Janet T Powell, MD, Prof Simon G Thompson, DSc

26 July 2018


Summary:

The NHS introduced ultrasound screening in men aged 65 and over in 2009 to detect and treat the condition – which arises when the main blood vessel swells in the abdomen, and is symptomless until the point of rupture. Since the launch, the programme has been successfully screening and identifying men at risk of an AAA.

Researchers wanted to see if UK women – who are less likely to have AAAs – could also benefit from a similar screening programme. Read the full press release here

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Genomic atlas of the human plasma proteome.

Publication: Nature

Sun BB, Maranville JC, Peters JE, Stacey D, Staley JR, Blackshaw J, et al.

6 June 2018

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Automated typing of red blood cell and platelet antigens: a whole-genome sequencing study.

Publication: The Lancet Haemotology

Lane WJ, Westhoff CM, Gleadall NS, Aguad M, Smeland-Wagman R, Vege S, et al.

1 June 2018

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Cross-ancestry genome-wide association analysis of corneal thickness strengthens link between complex and Mendelian eye diseases

Publication: Nature Communications

Iglesias AI, Mishra A, Vitart V, Bykhovskaya Y, Hohn R, Springelkamp H, et al.

14 May 2018

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