%0 Journal Article %J Lancet %D 2015 %T HMG-coenzyme A reductase inhibition, type 2 diabetes, and bodyweight: evidence from genetic analysis and randomised trials. %A Swerdlow, Daniel I %A Preiss, David %A Kuchenbaecker, Karoline B %A Holmes, Michael V %A Engmann, Jorgen E L %A Shah, Tina %A Sofat, Reecha %A Stender, Stefan %A Johnson, Paul C D %A Scott, Robert A %A Leusink, Maarten %A Verweij, Niek %A Sharp, Stephen J %A Guo, Yiran %A Giambartolomei, Claudia %A Chung, Christina %A Peasey, Anne %A Amuzu, Antoinette %A Li, KaWah %A Palmen, Jutta %A Howard, Philip %A Cooper, Jackie A %A Drenos, Fotios %A Li, Yun R %A Lowe, Gordon %A Gallacher, John %A Stewart, Marlene C W %A Tzoulaki, Ioanna %A Buxbaum, Sarah G %A van der A, Daphne L %A Forouhi, Nita G %A Onland-Moret, N Charlotte %A van der Schouw, Yvonne T %A Schnabel, Renate B %A Hubacek, Jaroslav A %A Kubinova, Ruzena %A Baceviciene, Migle %A Tamosiunas, Abdonas %A Pajak, Andrzej %A Topor-Madry, Roman %A Stepaniak, Urszula %A Malyutina, Sofia %A Baldassarre, Damiano %A Sennblad, Bengt %A Tremoli, Elena %A de Faire, Ulf %A Veglia, Fabrizio %A Ford, Ian %A Jukema, J Wouter %A Westendorp, Rudi G J %A de Borst, Gert Jan %A de Jong, Pim A %A Algra, Ale %A Spiering, Wilko %A Maitland-van der Zee, Anke H %A Klungel, Olaf H %A de Boer, Anthonius %A Doevendans, Pieter A %A Eaton, Charles B %A Robinson, Jennifer G %A Duggan, David %A Kjekshus, John %A Downs, John R %A Gotto, Antonio M %A Keech, Anthony C %A Marchioli, Roberto %A Tognoni, Gianni %A Sever, Peter S %A Poulter, Neil R %A Waters, David D %A Pedersen, Terje R %A Amarenco, Pierre %A Nakamura, Haruo %A McMurray, John J V %A Lewsey, James D %A Chasman, Daniel I %A Ridker, Paul M %A Maggioni, Aldo P %A Tavazzi, Luigi %A Ray, Kausik K %A Seshasai, Sreenivasa Rao Kondapally %A Manson, JoAnn E %A Price, Jackie F %A Whincup, Peter H %A Morris, Richard W %A Lawlor, Debbie A %A Smith, George Davey %A Ben-Shlomo, Yoav %A Schreiner, Pamela J %A Fornage, Myriam %A Siscovick, David S %A Cushman, Mary %A Kumari, Meena %A Wareham, Nick J %A Verschuren, W M Monique %A Redline, Susan %A Patel, Sanjay R %A Whittaker, John C %A Hamsten, Anders %A Delaney, Joseph A %A Dale, Caroline %A Gaunt, Tom R %A Wong, Andrew %A Kuh, Diana %A Hardy, Rebecca %A Kathiresan, Sekar %A Castillo, Berta A %A van der Harst, Pim %A Brunner, Eric J %A Tybjaerg-Hansen, Anne %A Marmot, Michael G %A Krauss, Ronald M %A Tsai, Michael %A Coresh, Josef %A Hoogeveen, Ronald C %A Psaty, Bruce M %A Lange, Leslie A %A Hakonarson, Hakon %A Dudbridge, Frank %A Humphries, Steve E %A Talmud, Philippa J %A Kivimaki, Mika %A Timpson, Nicholas J %A Langenberg, Claudia %A Asselbergs, Folkert W %A Voevoda, Mikhail %A Bobak, Martin %A Pikhart, Hynek %A Wilson, James G %A Reiner, Alex P %A Keating, Brendan J %A Hingorani, Aroon D %A Sattar, Naveed %K Aged %K Body Mass Index %K Body Weight %K Cholesterol, HDL %K Cholesterol, LDL %K Diabetes Mellitus, Type 2 %K Female %K Genetic Testing %K Humans %K Hydroxymethylglutaryl CoA Reductases %K Hydroxymethylglutaryl-CoA Reductase Inhibitors %K Male %K Middle Aged %K Polymorphism, Single Nucleotide %K Randomized Controlled Trials as Topic %K Risk Factors %X

BACKGROUND: Statins increase the risk of new-onset type 2 diabetes mellitus. We aimed to assess whether this increase in risk is a consequence of inhibition of 3-hydroxy-3-methylglutaryl-CoA reductase (HMGCR), the intended drug target.

METHODS: We used single nucleotide polymorphisms in the HMGCR gene, rs17238484 (for the main analysis) and rs12916 (for a subsidiary analysis) as proxies for HMGCR inhibition by statins. We examined associations of these variants with plasma lipid, glucose, and insulin concentrations; bodyweight; waist circumference; and prevalent and incident type 2 diabetes. Study-specific effect estimates per copy of each LDL-lowering allele were pooled by meta-analysis. These findings were compared with a meta-analysis of new-onset type 2 diabetes and bodyweight change data from randomised trials of statin drugs. The effects of statins in each randomised trial were assessed using meta-analysis.

FINDINGS: Data were available for up to 223 463 individuals from 43 genetic studies. Each additional rs17238484-G allele was associated with a mean 0·06 mmol/L (95% CI 0·05-0·07) lower LDL cholesterol and higher body weight (0·30 kg, 0·18-0·43), waist circumference (0·32 cm, 0·16-0·47), plasma insulin concentration (1·62%, 0·53-2·72), and plasma glucose concentration (0·23%, 0·02-0·44). The rs12916 SNP had similar effects on LDL cholesterol, bodyweight, and waist circumference. The rs17238484-G allele seemed to be associated with higher risk of type 2 diabetes (odds ratio [OR] per allele 1·02, 95% CI 1·00-1·05); the rs12916-T allele association was consistent (1·06, 1·03-1·09). In 129 170 individuals in randomised trials, statins lowered LDL cholesterol by 0·92 mmol/L (95% CI 0·18-1·67) at 1-year of follow-up, increased bodyweight by 0·24 kg (95% CI 0·10-0·38 in all trials; 0·33 kg, 95% CI 0·24-0·42 in placebo or standard care controlled trials and -0·15 kg, 95% CI -0·39 to 0·08 in intensive-dose vs moderate-dose trials) at a mean of 4·2 years (range 1·9-6·7) of follow-up, and increased the odds of new-onset type 2 diabetes (OR 1·12, 95% CI 1·06-1·18 in all trials; 1·11, 95% CI 1·03-1·20 in placebo or standard care controlled trials and 1·12, 95% CI 1·04-1·22 in intensive-dose vs moderate dose trials).

INTERPRETATION: The increased risk of type 2 diabetes noted with statins is at least partially explained by HMGCR inhibition.

FUNDING: The funding sources are cited at the end of the paper.

%B Lancet %V 385 %P 351-61 %8 2015 Jan 24 %G eng %N 9965 %1 http://www.ncbi.nlm.nih.gov/pubmed/25262344?dopt=Abstract %R 10.1016/S0140-6736(14)61183-1 %0 Journal Article %J BioData Min %D 2017 %T Discovery and replication of SNP-SNP interactions for quantitative lipid traits in over 60,000 individuals. %A Holzinger, Emily R %A Verma, Shefali S %A Moore, Carrie B %A Hall, Molly %A De, Rishika %A Gilbert-Diamond, Diane %A Lanktree, Matthew B %A Pankratz, Nathan %A Amuzu, Antoinette %A Burt, Amber %A Dale, Caroline %A Dudek, Scott %A Furlong, Clement E %A Gaunt, Tom R %A Kim, Daniel Seung %A Riess, Helene %A Sivapalaratnam, Suthesh %A Tragante, Vinicius %A van Iperen, Erik P A %A Brautbar, Ariel %A Carrell, David S %A Crosslin, David R %A Jarvik, Gail P %A Kuivaniemi, Helena %A Kullo, Iftikhar J %A Larson, Eric B %A Rasmussen-Torvik, Laura J %A Tromp, Gerard %A Baumert, Jens %A Cruickshanks, Karen J %A Farrall, Martin %A Hingorani, Aroon D %A Hovingh, G K %A Kleber, Marcus E %A Klein, Barbara E %A Klein, Ronald %A Koenig, Wolfgang %A Lange, Leslie A %A Mӓrz, Winfried %A North, Kari E %A Charlotte Onland-Moret, N %A Reiner, Alex P %A Talmud, Philippa J %A van der Schouw, Yvonne T %A Wilson, James G %A Kivimaki, Mika %A Kumari, Meena %A Moore, Jason H %A Drenos, Fotios %A Asselbergs, Folkert W %A Keating, Brendan J %A Ritchie, Marylyn D %X

BACKGROUND: The genetic etiology of human lipid quantitative traits is not fully elucidated, and interactions between variants may play a role. We performed a gene-centric interaction study for four different lipid traits: low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), total cholesterol (TC), and triglycerides (TG).

RESULTS: Our analysis consisted of a discovery phase using a merged dataset of five different cohorts (n = 12,853 to n = 16,849 depending on lipid phenotype) and a replication phase with ten independent cohorts totaling up to 36,938 additional samples. Filters are often applied before interaction testing to correct for the burden of testing all pairwise interactions. We used two different filters: 1. A filter that tested only single nucleotide polymorphisms (SNPs) with a main effect of p < 0.001 in a previous association study. 2. A filter that only tested interactions identified by Biofilter 2.0. Pairwise models that reached an interaction significance level of p < 0.001 in the discovery dataset were tested for replication. We identified thirteen SNP-SNP models that were significant in more than one replication cohort after accounting for multiple testing.

CONCLUSIONS: These results may reveal novel insights into the genetic etiology of lipid levels. Furthermore, we developed a pipeline to perform a computationally efficient interaction analysis with multi-cohort replication.

%B BioData Min %V 10 %P 25 %8 2017 %G eng %R 10.1186/s13040-017-0145-5