%0 Journal Article %J Diabetes %D 2011 %T Total zinc intake may modify the glucose-raising effect of a zinc transporter (SLC30A8) variant: a 14-cohort meta-analysis. %A Kanoni, Stavroula %A Nettleton, Jennifer A %A Hivert, Marie-France %A Ye, Zheng %A van Rooij, Frank J A %A Shungin, Dmitry %A Sonestedt, Emily %A Ngwa, Julius S %A Wojczynski, Mary K %A Lemaitre, Rozenn N %A Gustafsson, Stefan %A Anderson, Jennifer S %A Tanaka, Toshiko %A Hindy, George %A Saylor, Georgia %A Renstrom, Frida %A Bennett, Amanda J %A van Duijn, Cornelia M %A Florez, Jose C %A Fox, Caroline S %A Hofman, Albert %A Hoogeveen, Ron C %A Houston, Denise K %A Hu, Frank B %A Jacques, Paul F %A Johansson, Ingegerd %A Lind, Lars %A Liu, Yongmei %A McKeown, Nicola %A Ordovas, Jose %A Pankow, James S %A Sijbrands, Eric J G %A Syvänen, Ann-Christine %A Uitterlinden, André G %A Yannakoulia, Mary %A Zillikens, M Carola %A Wareham, Nick J %A Prokopenko, Inga %A Bandinelli, Stefania %A Forouhi, Nita G %A Cupples, L Adrienne %A Loos, Ruth J %A Hallmans, Göran %A Dupuis, Josée %A Langenberg, Claudia %A Ferrucci, Luigi %A Kritchevsky, Stephen B %A McCarthy, Mark I %A Ingelsson, Erik %A Borecki, Ingrid B %A Witteman, Jacqueline C M %A Orho-Melander, Marju %A Siscovick, David S %A Meigs, James B %A Franks, Paul W %A Dedoussis, George V %K Blood Glucose %K Cation Transport Proteins %K Cohort Studies %K Humans %K Polymorphism, Single Nucleotide %K Zinc %K Zinc Transporter 8 %X

OBJECTIVE: Many genetic variants have been associated with glucose homeostasis and type 2 diabetes in genome-wide association studies. Zinc is an essential micronutrient that is important for β-cell function and glucose homeostasis. We tested the hypothesis that zinc intake could influence the glucose-raising effect of specific variants.

RESEARCH DESIGN AND METHODS: We conducted a 14-cohort meta-analysis to assess the interaction of 20 genetic variants known to be related to glycemic traits and zinc metabolism with dietary zinc intake (food sources) and a 5-cohort meta-analysis to assess the interaction with total zinc intake (food sources and supplements) on fasting glucose levels among individuals of European ancestry without diabetes.

RESULTS: We observed a significant association of total zinc intake with lower fasting glucose levels (β-coefficient ± SE per 1 mg/day of zinc intake: -0.0012 ± 0.0003 mmol/L, summary P value = 0.0003), while the association of dietary zinc intake was not significant. We identified a nominally significant interaction between total zinc intake and the SLC30A8 rs11558471 variant on fasting glucose levels (β-coefficient ± SE per A allele for 1 mg/day of greater total zinc intake: -0.0017 ± 0.0006 mmol/L, summary interaction P value = 0.005); this result suggests a stronger inverse association between total zinc intake and fasting glucose in individuals carrying the glucose-raising A allele compared with individuals who do not carry it. None of the other interaction tests were statistically significant.

CONCLUSIONS: Our results suggest that higher total zinc intake may attenuate the glucose-raising effect of the rs11558471 SLC30A8 (zinc transporter) variant. Our findings also support evidence for the association of higher total zinc intake with lower fasting glucose levels.

%B Diabetes %V 60 %P 2407-16 %8 2011 Sep %G eng %N 9 %1 http://www.ncbi.nlm.nih.gov/pubmed/21810599?dopt=Abstract %R 10.2337/db11-0176 %0 Journal Article %J Nat Genet %D 2012 %T Genome-wide meta-analysis identifies 56 bone mineral density loci and reveals 14 loci associated with risk of fracture. %A Estrada, Karol %A Styrkarsdottir, Unnur %A Evangelou, Evangelos %A Hsu, Yi-Hsiang %A Duncan, Emma L %A Ntzani, Evangelia E %A Oei, Ling %A Albagha, Omar M E %A Amin, Najaf %A Kemp, John P %A Koller, Daniel L %A Li, Guo %A Liu, Ching-Ti %A Minster, Ryan L %A Moayyeri, Alireza %A Vandenput, Liesbeth %A Willner, Dana %A Xiao, Su-Mei %A Yerges-Armstrong, Laura M %A Zheng, Hou-Feng %A Alonso, Nerea %A Eriksson, Joel %A Kammerer, Candace M %A Kaptoge, Stephen K %A Leo, Paul J %A Thorleifsson, Gudmar %A Wilson, Scott G %A Wilson, James F %A Aalto, Ville %A Alen, Markku %A Aragaki, Aaron K %A Aspelund, Thor %A Center, Jacqueline R %A Dailiana, Zoe %A Duggan, David J %A Garcia, Melissa %A García-Giralt, Natalia %A Giroux, Sylvie %A Hallmans, Göran %A Hocking, Lynne J %A Husted, Lise Bjerre %A Jameson, Karen A %A Khusainova, Rita %A Kim, Ghi Su %A Kooperberg, Charles %A Koromila, Theodora %A Kruk, Marcin %A Laaksonen, Marika %A LaCroix, Andrea Z %A Lee, Seung Hun %A Leung, Ping C %A Lewis, Joshua R %A Masi, Laura %A Mencej-Bedrac, Simona %A Nguyen, Tuan V %A Nogues, Xavier %A Patel, Millan S %A Prezelj, Janez %A Rose, Lynda M %A Scollen, Serena %A Siggeirsdottir, Kristin %A Smith, Albert V %A Svensson, Olle %A Trompet, Stella %A Trummer, Olivia %A van Schoor, Natasja M %A Woo, Jean %A Zhu, Kun %A Balcells, Susana %A Brandi, Maria Luisa %A Buckley, Brendan M %A Cheng, Sulin %A Christiansen, Claus %A Cooper, Cyrus %A Dedoussis, George %A Ford, Ian %A Frost, Morten %A Goltzman, David %A González-Macías, Jesús %A Kähönen, Mika %A Karlsson, Magnus %A Khusnutdinova, Elza %A Koh, Jung-Min %A Kollia, Panagoula %A Langdahl, Bente Lomholt %A Leslie, William D %A Lips, Paul %A Ljunggren, Osten %A Lorenc, Roman S %A Marc, Janja %A Mellström, Dan %A Obermayer-Pietsch, Barbara %A Olmos, José M %A Pettersson-Kymmer, Ulrika %A Reid, David M %A Riancho, José A %A Ridker, Paul M %A Rousseau, François %A Slagboom, P Eline %A Tang, Nelson L S %A Urreizti, Roser %A Van Hul, Wim %A Viikari, Jorma %A Zarrabeitia, María T %A Aulchenko, Yurii S %A Castano-Betancourt, Martha %A Grundberg, Elin %A Herrera, Lizbeth %A Ingvarsson, Thorvaldur %A Johannsdottir, Hrefna %A Kwan, Tony %A Li, Rui %A Luben, Robert %A Medina-Gómez, Carolina %A Palsson, Stefan Th %A Reppe, Sjur %A Rotter, Jerome I %A Sigurdsson, Gunnar %A van Meurs, Joyce B J %A Verlaan, Dominique %A Williams, Frances M K %A Wood, Andrew R %A Zhou, Yanhua %A Gautvik, Kaare M %A Pastinen, Tomi %A Raychaudhuri, Soumya %A Cauley, Jane A %A Chasman, Daniel I %A Clark, Graeme R %A Cummings, Steven R %A Danoy, Patrick %A Dennison, Elaine M %A Eastell, Richard %A Eisman, John A %A Gudnason, Vilmundur %A Hofman, Albert %A Jackson, Rebecca D %A Jones, Graeme %A Jukema, J Wouter %A Khaw, Kay-Tee %A Lehtimäki, Terho %A Liu, Yongmei %A Lorentzon, Mattias %A McCloskey, Eugene %A Mitchell, Braxton D %A Nandakumar, Kannabiran %A Nicholson, Geoffrey C %A Oostra, Ben A %A Peacock, Munro %A Pols, Huibert A P %A Prince, Richard L %A Raitakari, Olli %A Reid, Ian R %A Robbins, John %A Sambrook, Philip N %A Sham, Pak Chung %A Shuldiner, Alan R %A Tylavsky, Frances A %A van Duijn, Cornelia M %A Wareham, Nick J %A Cupples, L Adrienne %A Econs, Michael J %A Evans, David M %A Harris, Tamara B %A Kung, Annie Wai Chee %A Psaty, Bruce M %A Reeve, Jonathan %A Spector, Timothy D %A Streeten, Elizabeth A %A Zillikens, M Carola %A Thorsteinsdottir, Unnur %A Ohlsson, Claes %A Karasik, David %A Richards, J Brent %A Brown, Matthew A %A Stefansson, Kari %A Uitterlinden, André G %A Ralston, Stuart H %A Ioannidis, John P A %A Kiel, Douglas P %A Rivadeneira, Fernando %K Bone Density %K Computational Biology %K European Continental Ancestry Group %K Extracellular Matrix Proteins %K Female %K Femur Neck %K Fractures, Bone %K Gene Expression Profiling %K Genetic Predisposition to Disease %K Genome-Wide Association Study %K Genotype %K Glycoproteins %K Humans %K Intercellular Signaling Peptides and Proteins %K Low Density Lipoprotein Receptor-Related Protein-5 %K Lumbar Vertebrae %K Male %K Mitochondrial Membrane Transport Proteins %K Osteoporosis %K Phosphoproteins %K Polymorphism, Single Nucleotide %K Quantitative Trait Loci %K Risk Factors %K Spectrin %X

Bone mineral density (BMD) is the most widely used predictor of fracture risk. We performed the largest meta-analysis to date on lumbar spine and femoral neck BMD, including 17 genome-wide association studies and 32,961 individuals of European and east Asian ancestry. We tested the top BMD-associated markers for replication in 50,933 independent subjects and for association with risk of low-trauma fracture in 31,016 individuals with a history of fracture (cases) and 102,444 controls. We identified 56 loci (32 new) associated with BMD at genome-wide significance (P < 5 × 10(-8)). Several of these factors cluster within the RANK-RANKL-OPG, mesenchymal stem cell differentiation, endochondral ossification and Wnt signaling pathways. However, we also discovered loci that were localized to genes not known to have a role in bone biology. Fourteen BMD-associated loci were also associated with fracture risk (P < 5 × 10(-4), Bonferroni corrected), of which six reached P < 5 × 10(-8), including at 18p11.21 (FAM210A), 7q21.3 (SLC25A13), 11q13.2 (LRP5), 4q22.1 (MEPE), 2p16.2 (SPTBN1) and 10q21.1 (DKK1). These findings shed light on the genetic architecture and pathophysiological mechanisms underlying BMD variation and fracture susceptibility.

%B Nat Genet %V 44 %P 491-501 %8 2012 Apr 15 %G eng %N 5 %R 10.1038/ng.2249 %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 Hum Mol Genet %D 2016 %T A meta-analysis of 120 246 individuals identifies 18 new loci for fibrinogen concentration. %A de Vries, Paul S %A Chasman, Daniel I %A Sabater-Lleal, Maria %A Chen, Ming-Huei %A Huffman, Jennifer E %A Steri, Maristella %A Tang, Weihong %A Teumer, Alexander %A Marioni, Riccardo E %A Grossmann, Vera %A Hottenga, Jouke J %A Trompet, Stella %A Müller-Nurasyid, Martina %A Zhao, Jing Hua %A Brody, Jennifer A %A Kleber, Marcus E %A Guo, Xiuqing %A Wang, Jie Jin %A Auer, Paul L %A Attia, John R %A Yanek, Lisa R %A Ahluwalia, Tarunveer S %A Lahti, Jari %A Venturini, Cristina %A Tanaka, Toshiko %A Bielak, Lawrence F %A Joshi, Peter K %A Rocanin-Arjo, Ares %A Kolcic, Ivana %A Navarro, Pau %A Rose, Lynda M %A Oldmeadow, Christopher %A Riess, Helene %A Mazur, Johanna %A Basu, Saonli %A Goel, Anuj %A Yang, Qiong %A Ghanbari, Mohsen %A Willemsen, Gonneke %A Rumley, Ann %A Fiorillo, Edoardo %A de Craen, Anton J M %A Grotevendt, Anne %A Scott, Robert %A Taylor, Kent D %A Delgado, Graciela E %A Yao, Jie %A Kifley, Annette %A Kooperberg, Charles %A Qayyum, Rehan %A Lopez, Lorna M %A Berentzen, Tina L %A Räikkönen, Katri %A Mangino, Massimo %A Bandinelli, Stefania %A Peyser, Patricia A %A Wild, Sarah %A Trégouët, David-Alexandre %A Wright, Alan F %A Marten, Jonathan %A Zemunik, Tatijana %A Morrison, Alanna C %A Sennblad, Bengt %A Tofler, Geoffrey %A de Maat, Moniek P M %A de Geus, Eco J C %A Lowe, Gordon D %A Zoledziewska, Magdalena %A Sattar, Naveed %A Binder, Harald %A Völker, Uwe %A Waldenberger, Melanie %A Khaw, Kay-Tee %A McKnight, Barbara %A Huang, Jie %A Jenny, Nancy S %A Holliday, Elizabeth G %A Qi, Lihong %A Mcevoy, Mark G %A Becker, Diane M %A Starr, John M %A Sarin, Antti-Pekka %A Hysi, Pirro G %A Hernandez, Dena G %A Jhun, Min A %A Campbell, Harry %A Hamsten, Anders %A Rivadeneira, Fernando %A McArdle, Wendy L %A Slagboom, P Eline %A Zeller, Tanja %A Koenig, Wolfgang %A Psaty, Bruce M %A Haritunians, Talin %A Liu, Jingmin %A Palotie, Aarno %A Uitterlinden, André G %A Stott, David J %A Hofman, Albert %A Franco, Oscar H %A Polasek, Ozren %A Rudan, Igor %A Morange, Pierre-Emmanuel %A Wilson, James F %A Kardia, Sharon L R %A Ferrucci, Luigi %A Spector, Tim D %A Eriksson, Johan G %A Hansen, Torben %A Deary, Ian J %A Becker, Lewis C %A Scott, Rodney J %A Mitchell, Paul %A März, Winfried %A Wareham, Nick J %A Peters, Annette %A Greinacher, Andreas %A Wild, Philipp S %A Jukema, J Wouter %A Boomsma, Dorret I %A Hayward, Caroline %A Cucca, Francesco %A Tracy, Russell %A Watkins, Hugh %A Reiner, Alex P %A Folsom, Aaron R %A Ridker, Paul M %A O'Donnell, Christopher J %A Smith, Nicholas L %A Strachan, David P %A Dehghan, Abbas %X

Genome-wide association studies have previously identified 23 genetic loci associated with circulating fibrinogen concentration. These studies used HapMap imputation and did not examine the X-chromosome. 1000 Genomes imputation provides better coverage of uncommon variants, and includes indels. We conducted a genome-wide association analysis of 34 studies imputed to the 1000 Genomes Project reference panel and including ∼120 000 participants of European ancestry (95 806 participants with data on the X-chromosome). Approximately 10.7 million single-nucleotide polymorphisms and 1.2 million indels were examined. We identified 41 genome-wide significant fibrinogen loci; of which, 18 were newly identified. There were no genome-wide significant signals on the X-chromosome. The lead variants of five significant loci were indels. We further identified six additional independent signals, including three rare variants, at two previously characterized loci: FGB and IRF1. Together the 41 loci explain 3% of the variance in plasma fibrinogen concentration.

%B Hum Mol Genet %V 25 %P 358-70 %8 2016 Jan 15 %G eng %N 2 %1 http://www.ncbi.nlm.nih.gov/pubmed/26561523?dopt=Abstract %R 10.1093/hmg/ddv454 %0 Journal Article %J Nat Genet %D 2017 %T Exome-wide association study of plasma lipids in >300,000 individuals. %A Liu, Dajiang J %A Peloso, Gina M %A Yu, Haojie %A Butterworth, Adam S %A Wang, Xiao %A Mahajan, Anubha %A Saleheen, Danish %A Emdin, Connor %A Alam, Dewan %A Alves, Alexessander Couto %A Amouyel, Philippe %A Di Angelantonio, Emanuele %A Arveiler, Dominique %A Assimes, Themistocles L %A Auer, Paul L %A Baber, Usman %A Ballantyne, Christie M %A Bang, Lia E %A Benn, Marianne %A Bis, Joshua C %A Boehnke, Michael %A Boerwinkle, Eric %A Bork-Jensen, Jette %A Bottinger, Erwin P %A Brandslund, Ivan %A Brown, Morris %A Busonero, Fabio %A Caulfield, Mark J %A Chambers, John C %A Chasman, Daniel I %A Chen, Y Eugene %A Chen, Yii-Der Ida %A Chowdhury, Rajiv %A Christensen, Cramer %A Chu, Audrey Y %A Connell, John M %A Cucca, Francesco %A Cupples, L Adrienne %A Damrauer, Scott M %A Davies, Gail %A Deary, Ian J %A Dedoussis, George %A Denny, Joshua C %A Dominiczak, Anna %A Dubé, Marie-Pierre %A Ebeling, Tapani %A Eiriksdottir, Gudny %A Esko, Tõnu %A Farmaki, Aliki-Eleni %A Feitosa, Mary F %A Ferrario, Marco %A Ferrieres, Jean %A Ford, Ian %A Fornage, Myriam %A Franks, Paul W %A Frayling, Timothy M %A Frikke-Schmidt, Ruth %A Fritsche, Lars G %A Frossard, Philippe %A Fuster, Valentin %A Ganesh, Santhi K %A Gao, Wei %A Garcia, Melissa E %A Gieger, Christian %A Giulianini, Franco %A Goodarzi, Mark O %A Grallert, Harald %A Grarup, Niels %A Groop, Leif %A Grove, Megan L %A Gudnason, Vilmundur %A Hansen, Torben %A Harris, Tamara B %A Hayward, Caroline %A Hirschhorn, Joel N %A Holmen, Oddgeir L %A Huffman, Jennifer %A Huo, Yong %A Hveem, Kristian %A Jabeen, Sehrish %A Jackson, Anne U %A Jakobsdottir, Johanna %A Jarvelin, Marjo-Riitta %A Jensen, Gorm B %A Jørgensen, Marit E %A Jukema, J Wouter %A Justesen, Johanne M %A Kamstrup, Pia R %A Kanoni, Stavroula %A Karpe, Fredrik %A Kee, Frank %A Khera, Amit V %A Klarin, Derek %A Koistinen, Heikki A %A Kooner, Jaspal S %A Kooperberg, Charles %A Kuulasmaa, Kari %A Kuusisto, Johanna %A Laakso, Markku %A Lakka, Timo %A Langenberg, Claudia %A Langsted, Anne %A Launer, Lenore J %A Lauritzen, Torsten %A Liewald, David C M %A Lin, Li An %A Linneberg, Allan %A Loos, Ruth J F %A Lu, Yingchang %A Lu, Xiangfeng %A Mägi, Reedik %A Mälarstig, Anders %A Manichaikul, Ani %A Manning, Alisa K %A Mäntyselkä, Pekka %A Marouli, Eirini %A Masca, Nicholas G D %A Maschio, Andrea %A Meigs, James B %A Melander, Olle %A Metspalu, Andres %A Morris, Andrew P %A Morrison, Alanna C %A Mulas, Antonella %A Müller-Nurasyid, Martina %A Munroe, Patricia B %A Neville, Matt J %A Nielsen, Jonas B %A Nielsen, Sune F %A Nordestgaard, Børge G %A Ordovas, Jose M %A Mehran, Roxana %A O'Donnell, Christoper J %A Orho-Melander, Marju %A Molony, Cliona M %A Muntendam, Pieter %A Padmanabhan, Sandosh %A Palmer, Colin N A %A Pasko, Dorota %A Patel, Aniruddh P %A Pedersen, Oluf %A Perola, Markus %A Peters, Annette %A Pisinger, Charlotta %A Pistis, Giorgio %A Polasek, Ozren %A Poulter, Neil %A Psaty, Bruce M %A Rader, Daniel J %A Rasheed, Asif %A Rauramaa, Rainer %A Reilly, Dermot F %A Reiner, Alex P %A Renstrom, Frida %A Rich, Stephen S %A Ridker, Paul M %A Rioux, John D %A Robertson, Neil R %A Roden, Dan M %A Rotter, Jerome I %A Rudan, Igor %A Salomaa, Veikko %A Samani, Nilesh J %A Sanna, Serena %A Sattar, Naveed %A Schmidt, Ellen M %A Scott, Robert A %A Sever, Peter %A Sevilla, Raquel S %A Shaffer, Christian M %A Sim, Xueling %A Sivapalaratnam, Suthesh %A Small, Kerrin S %A Smith, Albert V %A Smith, Blair H %A Somayajula, Sangeetha %A Southam, Lorraine %A Spector, Timothy D %A Speliotes, Elizabeth K %A Starr, John M %A Stirrups, Kathleen E %A Stitziel, Nathan %A Strauch, Konstantin %A Stringham, Heather M %A Surendran, Praveen %A Tada, Hayato %A Tall, Alan R %A Tang, Hua %A Tardif, Jean-Claude %A Taylor, Kent D %A Trompet, Stella %A Tsao, Philip S %A Tuomilehto, Jaakko %A Tybjaerg-Hansen, Anne %A van Zuydam, Natalie R %A Varbo, Anette %A Varga, Tibor V %A Virtamo, Jarmo %A Waldenberger, Melanie %A Wang, Nan %A Wareham, Nick J %A Warren, Helen R %A Weeke, Peter E %A Weinstock, Joshua %A Wessel, Jennifer %A Wilson, James G %A Wilson, Peter W F %A Xu, Ming %A Yaghootkar, Hanieh %A Young, Robin %A Zeggini, Eleftheria %A Zhang, He %A Zheng, Neil S %A Zhang, Weihua %A Zhang, Yan %A Zhou, Wei %A Zhou, Yanhua %A Zoledziewska, Magdalena %A Howson, Joanna M M %A Danesh, John %A McCarthy, Mark I %A Cowan, Chad A %A Abecasis, Goncalo %A Deloukas, Panos %A Musunuru, Kiran %A Willer, Cristen J %A Kathiresan, Sekar %K Coronary Artery Disease %K Diabetes Mellitus, Type 2 %K Exome %K Genetic Association Studies %K Genetic Predisposition to Disease %K Genetic Variation %K Genotype %K Humans %K Lipids %K Macular Degeneration %K Phenotype %K Risk Factors %X

We screened variants on an exome-focused genotyping array in >300,000 participants (replication in >280,000 participants) and identified 444 independent variants in 250 loci significantly associated with total cholesterol (TC), high-density-lipoprotein cholesterol (HDL-C), low-density-lipoprotein cholesterol (LDL-C), and/or triglycerides (TG). At two loci (JAK2 and A1CF), experimental analysis in mice showed lipid changes consistent with the human data. We also found that: (i) beta-thalassemia trait carriers displayed lower TC and were protected from coronary artery disease (CAD); (ii) excluding the CETP locus, there was not a predictable relationship between plasma HDL-C and risk for age-related macular degeneration; (iii) only some mechanisms of lowering LDL-C appeared to increase risk for type 2 diabetes (T2D); and (iv) TG-lowering alleles involved in hepatic production of TG-rich lipoproteins (TM6SF2 and PNPLA3) tracked with higher liver fat, higher risk for T2D, and lower risk for CAD, whereas TG-lowering alleles involved in peripheral lipolysis (LPL and ANGPTL4) had no effect on liver fat but decreased risks for both T2D and CAD.

%B Nat Genet %V 49 %P 1758-1766 %8 2017 Dec %G eng %N 12 %R 10.1038/ng.3977 %0 Journal Article %J Nat Commun %D 2017 %T Large-scale GWAS identifies multiple loci for hand grip strength providing biological insights into muscular fitness. %A Willems, Sara M %A Wright, Daniel J %A Day, Felix R %A Trajanoska, Katerina %A Joshi, Peter K %A Morris, John A %A Matteini, Amy M %A Garton, Fleur C %A Grarup, Niels %A Oskolkov, Nikolay %A Thalamuthu, Anbupalam %A Mangino, Massimo %A Liu, Jun %A Demirkan, Ayse %A Lek, Monkol %A Xu, Liwen %A Wang, Guan %A Oldmeadow, Christopher %A Gaulton, Kyle J %A Lotta, Luca A %A Miyamoto-Mikami, Eri %A Rivas, Manuel A %A White, Tom %A Loh, Po-Ru %A Aadahl, Mette %A Amin, Najaf %A Attia, John R %A Austin, Krista %A Benyamin, Beben %A Brage, Søren %A Cheng, Yu-Ching %A Cięszczyk, Paweł %A Derave, Wim %A Eriksson, Karl-Fredrik %A Eynon, Nir %A Linneberg, Allan %A Lucia, Alejandro %A Massidda, Myosotis %A Mitchell, Braxton D %A Miyachi, Motohiko %A Murakami, Haruka %A Padmanabhan, Sandosh %A Pandey, Ashutosh %A Papadimitriou, Ioannis %A Rajpal, Deepak K %A Sale, Craig %A Schnurr, Theresia M %A Sessa, Francesco %A Shrine, Nick %A Tobin, Martin D %A Varley, Ian %A Wain, Louise V %A Wray, Naomi R %A Lindgren, Cecilia M %A MacArthur, Daniel G %A Waterworth, Dawn M %A McCarthy, Mark I %A Pedersen, Oluf %A Khaw, Kay-Tee %A Kiel, Douglas P %A Pitsiladis, Yannis %A Fuku, Noriyuki %A Franks, Paul W %A North, Kathryn N %A van Duijn, Cornelia M %A Mather, Karen A %A Hansen, Torben %A Hansson, Ola %A Spector, Tim %A Murabito, Joanne M %A Richards, J Brent %A Rivadeneira, Fernando %A Langenberg, Claudia %A Perry, John R B %A Wareham, Nick J %A Scott, Robert A %X

Hand grip strength is a widely used proxy of muscular fitness, a marker of frailty, and predictor of a range of morbidities and all-cause mortality. To investigate the genetic determinants of variation in grip strength, we perform a large-scale genetic discovery analysis in a combined sample of 195,180 individuals and identify 16 loci associated with grip strength (P<5 × 10) in combined analyses. A number of these loci contain genes implicated in structure and function of skeletal muscle fibres (ACTG1), neuronal maintenance and signal transduction (PEX14, TGFA, SYT1), or monogenic syndromes with involvement of psychomotor impairment (PEX14, LRPPRC and KANSL1). Mendelian randomization analyses are consistent with a causal effect of higher genetically predicted grip strength on lower fracture risk. In conclusion, our findings provide new biological insight into the mechanistic underpinnings of grip strength and the causal role of muscular strength in age-related morbidities and mortality.

%B Nat Commun %V 8 %P 16015 %8 2017 Jul 12 %G eng %R 10.1038/ncomms16015 %0 Journal Article %J PLoS Med %D 2018 %T Fatty acid biomarkers of dairy fat consumption and incidence of type 2 diabetes: A pooled analysis of prospective cohort studies. %A Imamura, Fumiaki %A Fretts, Amanda %A Marklund, Matti %A Ardisson Korat, Andres V %A Yang, Wei-Sin %A Lankinen, Maria %A Qureshi, Waqas %A Helmer, Catherine %A Chen, Tzu-An %A Wong, Kerry %A Bassett, Julie K %A Murphy, Rachel %A Tintle, Nathan %A Yu, Chaoyu Ian %A Brouwer, Ingeborg A %A Chien, Kuo-Liong %A Frazier-Wood, Alexis C %A Del Gobbo, Liana C %A Djoussé, Luc %A Geleijnse, Johanna M %A Giles, Graham G %A de Goede, Janette %A Gudnason, Vilmundur %A Harris, William S %A Hodge, Allison %A Hu, Frank %A Koulman, Albert %A Laakso, Markku %A Lind, Lars %A Lin, Hung-Ju %A McKnight, Barbara %A Rajaobelina, Kalina %A Riserus, Ulf %A Robinson, Jennifer G %A Samieri, Cecilia %A Siscovick, David S %A Soedamah-Muthu, Sabita S %A Sotoodehnia, Nona %A Sun, Qi %A Tsai, Michael Y %A Uusitupa, Matti %A Wagenknecht, Lynne E %A Wareham, Nick J %A Wu, Jason HY %A Micha, Renata %A Forouhi, Nita G %A Lemaitre, Rozenn N %A Mozaffarian, Dariush %K Aged %K Australia %K Biomarkers %K Dairy Products %K Diabetes Mellitus, Type 2 %K Dietary Fats %K Europe %K Fatty Acids %K Fatty Acids, Monounsaturated %K Female %K Humans %K Incidence %K Male %K Middle Aged %K Prospective Studies %K Sex Factors %K Taiwan %K United States %X

BACKGROUND: We aimed to investigate prospective associations of circulating or adipose tissue odd-chain fatty acids 15:0 and 17:0 and trans-palmitoleic acid, t16:1n-7, as potential biomarkers of dairy fat intake, with incident type 2 diabetes (T2D).

METHODS AND FINDINGS: Sixteen prospective cohorts from 12 countries (7 from the United States, 7 from Europe, 1 from Australia, 1 from Taiwan) performed new harmonised individual-level analysis for the prospective associations according to a standardised plan. In total, 63,682 participants with a broad range of baseline ages and BMIs and 15,180 incident cases of T2D over the average of 9 years of follow-up were evaluated. Study-specific results were pooled using inverse-variance-weighted meta-analysis. Prespecified interactions by age, sex, BMI, and race/ethnicity were explored in each cohort and were meta-analysed. Potential heterogeneity by cohort-specific characteristics (regions, lipid compartments used for fatty acid assays) was assessed with metaregression. After adjustment for potential confounders, including measures of adiposity (BMI, waist circumference) and lipogenesis (levels of palmitate, triglycerides), higher levels of 15:0, 17:0, and t16:1n-7 were associated with lower incidence of T2D. In the most adjusted model, the hazard ratio (95% CI) for incident T2D per cohort-specific 10th to 90th percentile range of 15:0 was 0.80 (0.73-0.87); of 17:0, 0.65 (0.59-0.72); of t16:1n7, 0.82 (0.70-0.96); and of their sum, 0.71 (0.63-0.79). In exploratory analyses, similar associations for 15:0, 17:0, and the sum of all three fatty acids were present in both genders but stronger in women than in men (pinteraction < 0.001). Whereas studying associations with biomarkers has several advantages, as limitations, the biomarkers do not distinguish between different food sources of dairy fat (e.g., cheese, yogurt, milk), and residual confounding by unmeasured or imprecisely measured confounders may exist.

CONCLUSIONS: In a large meta-analysis that pooled the findings from 16 prospective cohort studies, higher levels of 15:0, 17:0, and t16:1n-7 were associated with a lower risk of T2D.

%B PLoS Med %V 15 %P e1002670 %8 2018 10 %G eng %N 10 %R 10.1371/journal.pmed.1002670 %0 Journal Article %J Nat Genet %D 2018 %T Multiancestry association study identifies new asthma risk loci that colocalize with immune-cell enhancer marks. %A Demenais, Florence %A Margaritte-Jeannin, Patricia %A Barnes, Kathleen C %A Cookson, William O C %A Altmüller, Janine %A Ang, Wei %A Barr, R Graham %A Beaty, Terri H %A Becker, Allan B %A Beilby, John %A Bisgaard, Hans %A Bjornsdottir, Unnur Steina %A Bleecker, Eugene %A Bønnelykke, Klaus %A Boomsma, Dorret I %A Bouzigon, Emmanuelle %A Brightling, Christopher E %A Brossard, Myriam %A Brusselle, Guy G %A Burchard, Esteban %A Burkart, Kristin M %A Bush, Andrew %A Chan-Yeung, Moira %A Chung, Kian Fan %A Couto Alves, Alexessander %A Curtin, John A %A Custovic, Adnan %A Daley, Denise %A de Jongste, Johan C %A Del-Rio-Navarro, Blanca E %A Donohue, Kathleen M %A Duijts, Liesbeth %A Eng, Celeste %A Eriksson, Johan G %A Farrall, Martin %A Fedorova, Yuliya %A Feenstra, Bjarke %A Ferreira, Manuel A %A Freidin, Maxim B %A Gajdos, Zofia %A Gauderman, Jim %A Gehring, Ulrike %A Geller, Frank %A Genuneit, Jon %A Gharib, Sina A %A Gilliland, Frank %A Granell, Raquel %A Graves, Penelope E %A Gudbjartsson, Daniel F %A Haahtela, Tari %A Heckbert, Susan R %A Heederik, Dick %A Heinrich, Joachim %A Heliövaara, Markku %A Henderson, John %A Himes, Blanca E %A Hirose, Hiroshi %A Hirschhorn, Joel N %A Hofman, Albert %A Holt, Patrick %A Hottenga, Jouke %A Hudson, Thomas J %A Hui, Jennie %A Imboden, Medea %A Ivanov, Vladimir %A Jaddoe, Vincent W V %A James, Alan %A Janson, Christer %A Jarvelin, Marjo-Riitta %A Jarvis, Deborah %A Jones, Graham %A Jonsdottir, Ingileif %A Jousilahti, Pekka %A Kabesch, Michael %A Kähönen, Mika %A Kantor, David B %A Karunas, Alexandra S %A Khusnutdinova, Elza %A Koppelman, Gerard H %A Kozyrskyj, Anita L %A Kreiner, Eskil %A Kubo, Michiaki %A Kumar, Rajesh %A Kumar, Ashish %A Kuokkanen, Mikko %A Lahousse, Lies %A Laitinen, Tarja %A Laprise, Catherine %A Lathrop, Mark %A Lau, Susanne %A Lee, Young-Ae %A Lehtimäki, Terho %A Letort, Sébastien %A Levin, Albert M %A Li, Guo %A Liang, Liming %A Loehr, Laura R %A London, Stephanie J %A Loth, Daan W %A Manichaikul, Ani %A Marenholz, Ingo %A Martinez, Fernando J %A Matheson, Melanie C %A Mathias, Rasika A %A Matsumoto, Kenji %A Mbarek, Hamdi %A McArdle, Wendy L %A Melbye, Mads %A Melén, Erik %A Meyers, Deborah %A Michel, Sven %A Mohamdi, Hamida %A Musk, Arthur W %A Myers, Rachel A %A Nieuwenhuis, Maartje A E %A Noguchi, Emiko %A O'Connor, George T %A Ogorodova, Ludmila M %A Palmer, Cameron D %A Palotie, Aarno %A Park, Julie E %A Pennell, Craig E %A Pershagen, Göran %A Polonikov, Alexey %A Postma, Dirkje S %A Probst-Hensch, Nicole %A Puzyrev, Valery P %A Raby, Benjamin A %A Raitakari, Olli T %A Ramasamy, Adaikalavan %A Rich, Stephen S %A Robertson, Colin F %A Romieu, Isabelle %A Salam, Muhammad T %A Salomaa, Veikko %A Schlünssen, Vivi %A Scott, Robert %A Selivanova, Polina A %A Sigsgaard, Torben %A Simpson, Angela %A Siroux, Valérie %A Smith, Lewis J %A Solodilova, Maria %A Standl, Marie %A Stefansson, Kari %A Strachan, David P %A Stricker, Bruno H %A Takahashi, Atsushi %A Thompson, Philip J %A Thorleifsson, Gudmar %A Thorsteinsdottir, Unnur %A Tiesler, Carla M T %A Torgerson, Dara G %A Tsunoda, Tatsuhiko %A Uitterlinden, André G %A van der Valk, Ralf J P %A Vaysse, Amaury %A Vedantam, Sailaja %A von Berg, Andrea %A von Mutius, Erika %A Vonk, Judith M %A Waage, Johannes %A Wareham, Nick J %A Weiss, Scott T %A White, Wendy B %A Wickman, Magnus %A Widen, Elisabeth %A Willemsen, Gonneke %A Williams, L Keoki %A Wouters, Inge M %A Yang, James J %A Zhao, Jing Hua %A Moffatt, Miriam F %A Ober, Carole %A Nicolae, Dan L %X

We examined common variation in asthma risk by conducting a meta-analysis of worldwide asthma genome-wide association studies (23,948 asthma cases, 118,538 controls) of individuals from ethnically diverse populations. We identified five new asthma loci, found two new associations at two known asthma loci, established asthma associations at two loci previously implicated in the comorbidity of asthma plus hay fever, and confirmed nine known loci. Investigation of pleiotropy showed large overlaps in genetic variants with autoimmune and inflammatory diseases. The enrichment in enhancer marks at asthma risk loci, especially in immune cells, suggested a major role of these loci in the regulation of immunologically related mechanisms.

%B Nat Genet %V 50 %P 42-53 %8 2018 Jan %G eng %N 1 %R 10.1038/s41588-017-0014-7 %0 Journal Article %J Circ Genom Precis Med %D 2021 %T Sugar-Sweetened Beverage Consumption May Modify Associations Between Genetic Variants in the CHREBP (Carbohydrate Responsive Element Binding Protein) Locus and HDL-C (High-Density Lipoprotein Cholesterol) and Triglyceride Concentrations. %A Haslam, Danielle E %A Peloso, Gina M %A Guirette, Melanie %A Imamura, Fumiaki %A Bartz, Traci M %A Pitsillides, Achilleas N %A Wang, Carol A %A Li-Gao, Ruifang %A Westra, Jason M %A Pitkänen, Niina %A Young, Kristin L %A Graff, Mariaelisa %A Wood, Alexis C %A Braun, Kim V E %A Luan, Jian'an %A Kähönen, Mika %A Kiefte-de Jong, Jessica C %A Ghanbari, Mohsen %A Tintle, Nathan %A Lemaitre, Rozenn N %A Mook-Kanamori, Dennis O %A North, Kari %A Helminen, Mika %A Mossavar-Rahmani, Yasmin %A Snetselaar, Linda %A Martin, Lisa W %A Viikari, Jorma S %A Oddy, Wendy H %A Pennell, Craig E %A Rosendall, Frits R %A Ikram, M Arfan %A Uitterlinden, André G %A Psaty, Bruce M %A Mozaffarian, Dariush %A Rotter, Jerome I %A Taylor, Kent D %A Lehtimäki, Terho %A Raitakari, Olli T %A Livingston, Kara A %A Voortman, Trudy %A Forouhi, Nita G %A Wareham, Nick J %A de Mutsert, Renée %A Rich, Steven S %A Manson, JoAnn E %A Mora, Samia %A Ridker, Paul M %A Merino, Jordi %A Meigs, James B %A Dashti, Hassan S %A Chasman, Daniel I %A Lichtenstein, Alice H %A Smith, Caren E %A Dupuis, Josée %A Herman, Mark A %A McKeown, Nicola M %X

BACKGROUND: ChREBP (carbohydrate responsive element binding protein) is a transcription factor that responds to sugar consumption. Sugar-sweetened beverage (SSB) consumption and genetic variants in the locus have separately been linked to HDL-C (high-density lipoprotein cholesterol) and triglyceride concentrations. We hypothesized that SSB consumption would modify the association between genetic variants in the locus and dyslipidemia.

METHODS: Data from 11 cohorts from the Cohorts for Heart and Aging Research in Genomic Epidemiology consortium (N=63 599) and the UK Biobank (N=59 220) were used to quantify associations of SSB consumption, genetic variants, and their interaction on HDL-C and triglyceride concentrations using linear regression models. A total of 1606 single nucleotide polymorphisms within or near were considered. SSB consumption was estimated from validated questionnaires, and participants were grouped by their estimated intake.

RESULTS: In a meta-analysis, rs71556729 was significantly associated with higher HDL-C concentrations only among the highest SSB consumers (β, 2.12 [95% CI, 1.16-3.07] mg/dL per allele; <0.0001), but not significantly among the lowest SSB consumers (=0.81; <0.0001). Similar results were observed for 2 additional variants (rs35709627 and rs71556736). For triglyceride, rs55673514 was positively associated with triglyceride concentrations only among the highest SSB consumers (β, 0.06 [95% CI, 0.02-0.09] ln-mg/dL per allele, =0.001) but not the lowest SSB consumers (=0.84; =0.0005).

CONCLUSIONS: Our results identified genetic variants in the locus that may protect against SSB-associated reductions in HDL-C and other variants that may exacerbate SSB-associated increases in triglyceride concentrations. Registration: URL: https://www.clinicaltrials.gov; Unique identifier: NCT00005133, NCT00005121, NCT00005487, and NCT00000479.

%B Circ Genom Precis Med %V 14 %P e003288 %8 2021 Aug %G eng %N 4 %R 10.1161/CIRCGEN.120.003288