@article {1308, title = {Total zinc intake may modify the glucose-raising effect of a zinc transporter (SLC30A8) variant: a 14-cohort meta-analysis.}, journal = {Diabetes}, volume = {60}, year = {2011}, month = {2011 Sep}, pages = {2407-16}, abstract = {

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 {\textpm} SE per 1 mg/day of zinc intake: -0.0012 {\textpm} 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 {\textpm} SE per A allele for 1 mg/day of greater total zinc intake: -0.0017 {\textpm} 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.

}, keywords = {Blood Glucose, Cation Transport Proteins, Cohort Studies, Humans, Polymorphism, Single Nucleotide, Zinc, Zinc Transporter 8}, issn = {1939-327X}, doi = {10.2337/db11-0176}, author = {Kanoni, Stavroula and Nettleton, Jennifer A and Hivert, Marie-France and Ye, Zheng and van Rooij, Frank J A and Shungin, Dmitry and Sonestedt, Emily and Ngwa, Julius S and Wojczynski, Mary K and Lemaitre, Rozenn N and Gustafsson, Stefan and Anderson, Jennifer S and Tanaka, Toshiko and Hindy, George and Saylor, Georgia and Renstrom, Frida and Bennett, Amanda J and van Duijn, Cornelia M and Florez, Jose C and Fox, Caroline S and Hofman, Albert and Hoogeveen, Ron C and Houston, Denise K and Hu, Frank B and Jacques, Paul F and Johansson, Ingegerd and Lind, Lars and Liu, Yongmei and McKeown, Nicola and Ordovas, Jose and Pankow, James S and Sijbrands, Eric J G and Syv{\"a}nen, Ann-Christine and Uitterlinden, Andr{\'e} G and Yannakoulia, Mary and Zillikens, M Carola and Wareham, Nick J and Prokopenko, Inga and Bandinelli, Stefania and Forouhi, Nita G and Cupples, L Adrienne and Loos, Ruth J and Hallmans, G{\"o}ran and Dupuis, Jos{\'e}e and Langenberg, Claudia and Ferrucci, Luigi and Kritchevsky, Stephen B and McCarthy, Mark I and Ingelsson, Erik and Borecki, Ingrid B and Witteman, Jacqueline C M and Orho-Melander, Marju and Siscovick, David S and Meigs, James B and Franks, Paul W and Dedoussis, George V} } @article {6579, title = {Sequence variation in TMEM18 in association with body mass index: Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium Targeted Sequencing Study.}, journal = {Circ Cardiovasc Genet}, volume = {7}, year = {2014}, month = {2014 Jun}, pages = {344-9}, abstract = {

BACKGROUND: Genome-wide association studies for body mass index (BMI) previously identified a locus near TMEM18. We conducted targeted sequencing of this region to investigate the role of common, low-frequency, and rare variants influencing BMI.

METHODS AND RESULTS: We sequenced TMEM18 and regions downstream of TMEM18 on chromosome 2 in 3976 individuals of European ancestry from 3 community-based cohorts (Atherosclerosis Risk in Communities, Cardiovascular Health Study, and Framingham Heart Study), including 200 adults selected for high BMI. We examined the association between BMI and variants identified in the region from nucleotide position 586 432 to 677 539 (hg18). Rare variants (minor allele frequency, <1\%) were analyzed using a burden test and the sequence kernel association test. Results from the 3 cohort studies were meta-analyzed. We estimate that mean BMI is 0.43 kg/m(2) higher for each copy of the G allele of single-nucleotide polymorphism rs7596758 (minor allele frequency, 29\%; P=3.46{\texttimes}10(-4)) using a Bonferroni threshold of P<4.6{\texttimes}10(-4). Analyses conditional on previous genome-wide association study single-nucleotide polymorphisms associated with BMI in the region led to attenuation of this signal and uncovered another independent (r(2)<0.2), statistically significant association, rs186019316 (P=2.11{\texttimes}10(-4)). Both rs186019316 and rs7596758 or proxies are located in transcription factor binding regions. No significant association with rare variants was found in either the exons of TMEM18 or the 3{\textquoteright} genome-wide association study region.

CONCLUSIONS: Targeted sequencing around TMEM18 identified 2 novel BMI variants with possible regulatory function.

}, keywords = {Adult, Aged, Aging, Body Mass Index, Cohort Studies, Female, Genetic Association Studies, Genetic Variation, Genome-Wide Association Study, Genomics, Heart Diseases, Humans, Male, Membrane Proteins, Middle Aged, Polymorphism, Single Nucleotide, Sequence Analysis, DNA, Young Adult}, issn = {1942-3268}, doi = {10.1161/CIRCGENETICS.13.000067}, author = {Liu, Ching-Ti and Young, Kristin L and Brody, Jennifer A and Olden, Matthias and Wojczynski, Mary K and Heard-Costa, Nancy and Li, Guo and Morrison, Alanna C and Muzny, Donna and Gibbs, Richard A and Reid, Jeffrey G and Shao, Yaming and Zhou, Yanhua and Boerwinkle, Eric and Heiss, Gerardo and Wagenknecht, Lynne and McKnight, Barbara and Borecki, Ingrid B and Fox, Caroline S and North, Kari E and Cupples, L Adrienne} } @article {6844, title = {Consumption of meat is associated with higher fasting glucose and insulin concentrations regardless of glucose and insulin genetic risk scores: a meta-analysis of 50,345 Caucasians.}, journal = {Am J Clin Nutr}, volume = {102}, year = {2015}, month = {2015 Nov}, pages = {1266-78}, abstract = {

BACKGROUND: Recent studies suggest that meat intake is associated with diabetes-related phenotypes. However, whether the associations of meat intake and glucose and insulin homeostasis are modified by genes related to glucose and insulin is unknown.

OBJECTIVE: We investigated the associations of meat intake and the interaction of meat with genotype on fasting glucose and insulin concentrations in Caucasians free of diabetes mellitus.

DESIGN: Fourteen studies that are part of the Cohorts for Heart and Aging Research in Genomic Epidemiology consortium participated in the analysis. Data were provided for up to 50,345 participants. Using linear regression within studies and a fixed-effects meta-analysis across studies, we examined 1) the associations of processed meat and unprocessed red meat intake with fasting glucose and insulin concentrations; and 2) the interactions of processed meat and unprocessed red meat with genetic risk score related to fasting glucose or insulin resistance on fasting glucose and insulin concentrations.

RESULTS: Processed meat was associated with higher fasting glucose, and unprocessed red meat was associated with both higher fasting glucose and fasting insulin concentrations after adjustment for potential confounders [not including body mass index (BMI)]. For every additional 50-g serving of processed meat per day, fasting glucose was 0.021 mmol/L (95\% CI: 0.011, 0.030 mmol/L) higher. Every additional 100-g serving of unprocessed red meat per day was associated with a 0.037-mmol/L (95\% CI: 0.023, 0.051-mmol/L) higher fasting glucose concentration and a 0.049-ln-pmol/L (95\% CI: 0.035, 0.063-ln-pmol/L) higher fasting insulin concentration. After additional adjustment for BMI, observed associations were attenuated and no longer statistically significant. The association of processed meat and fasting insulin did not reach statistical significance after correction for multiple comparisons. Observed associations were not modified by genetic loci known to influence fasting glucose or insulin resistance.

CONCLUSION: The association of higher fasting glucose and insulin concentrations with meat consumption was not modified by an index of glucose- and insulin-related single-nucleotide polymorphisms. Six of the participating studies are registered at clinicaltrials.gov as NCT0000513 (Atherosclerosis Risk in Communities), NCT00149435 (Cardiovascular Health Study), NCT00005136 (Family Heart Study), NCT00005121 (Framingham Heart Study), NCT00083369 (Genetics of Lipid Lowering Drugs and Diet Network), and NCT00005487 (Multi-Ethnic Study of Atherosclerosis).

}, keywords = {Blood Glucose, Cohort Studies, Genetic Association Studies, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Hyperglycemia, Hyperinsulinism, Insulin, Insulin Resistance, Insulin-Secreting Cells, Meat, Meat Products, Middle Aged, Polymorphism, Single Nucleotide, Risk Factors}, issn = {1938-3207}, doi = {10.3945/ajcn.114.101238}, author = {Fretts, Amanda M and Follis, Jack L and Nettleton, Jennifer A and Lemaitre, Rozenn N and Ngwa, Julius S and Wojczynski, Mary K and Kalafati, Ioanna Panagiota and Varga, Tibor V and Frazier-Wood, Alexis C and Houston, Denise K and Lahti, Jari and Ericson, Ulrika and van den Hooven, Edith H and Mikkil{\"a}, Vera and Kiefte-de Jong, Jessica C and Mozaffarian, Dariush and Rice, Kenneth and Renstrom, Frida and North, Kari E and McKeown, Nicola M and Feitosa, Mary F and Kanoni, Stavroula and Smith, Caren E and Garcia, Melissa E and Tiainen, Anna-Maija and Sonestedt, Emily and Manichaikul, Ani and van Rooij, Frank J A and Dimitriou, Maria and Raitakari, Olli and Pankow, James S and Djouss{\'e}, Luc and Province, Michael A and Hu, Frank B and Lai, Chao-Qiang and Keller, Margaux F and Per{\"a}l{\"a}, Mia-Maria and Rotter, Jerome I and Hofman, Albert and Graff, Misa and K{\"a}h{\"o}nen, Mika and Mukamal, Kenneth and Johansson, Ingegerd and Ordovas, Jose M and Liu, Yongmei and M{\"a}nnist{\"o}, Satu and Uitterlinden, Andr{\'e} G and Deloukas, Panos and Sepp{\"a}l{\"a}, Ilkka and Psaty, Bruce M and Cupples, L Adrienne and Borecki, Ingrid B and Franks, Paul W and Arnett, Donna K and Nalls, Mike A and Eriksson, Johan G and Orho-Melander, Marju and Franco, Oscar H and Lehtim{\"a}ki, Terho and Dedoussis, George V and Meigs, James B and Siscovick, David S} } @article {6927, title = {Gene-Environment Interactions of Circadian-Related Genes for Cardiometabolic Traits.}, journal = {Diabetes Care}, volume = {38}, year = {2015}, month = {2015 Aug}, pages = {1456-66}, abstract = {

OBJECTIVE: Common circadian-related gene variants associate with increased risk for metabolic alterations including type 2 diabetes. However, little is known about whether diet and sleep could modify associations between circadian-related variants (CLOCK-rs1801260, CRY2-rs11605924, MTNR1B-rs1387153, MTNR1B-rs10830963, NR1D1-rs2314339) and cardiometabolic traits (fasting glucose [FG], HOMA-insulin resistance, BMI, waist circumference, and HDL-cholesterol) to facilitate personalized recommendations.

RESEARCH DESIGN AND METHODS: We conducted inverse-variance weighted, fixed-effect meta-analyses of results of adjusted associations and interactions between dietary intake/sleep duration and selected variants on cardiometabolic traits from 15 cohort studies including up to 28,190 participants of European descent from the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium.

RESULTS: We observed significant associations between relative macronutrient intakes and glycemic traits and short sleep duration (<7 h) and higher FG and replicated known MTNR1B associations with glycemic traits. No interactions were evident after accounting for multiple comparisons. However, we observed nominally significant interactions (all P < 0.01) between carbohydrate intake and MTNR1B-rs1387153 for FG with a 0.003 mmol/L higher FG with each additional 1\% carbohydrate intake in the presence of the T allele, between sleep duration and CRY2-rs11605924 for HDL-cholesterol with a 0.010 mmol/L higher HDL-cholesterol with each additional hour of sleep in the presence of the A allele, and between long sleep duration (>=9 h) and MTNR1B-rs1387153 for BMI with a 0.60 kg/m(2) higher BMI with long sleep duration in the presence of the T allele relative to normal sleep duration (>=7 to <9 h).

CONCLUSIONS: Our results suggest that lower carbohydrate intake and normal sleep duration may ameliorate cardiometabolic abnormalities conferred by common circadian-related genetic variants. Until further mechanistic examination of the nominally significant interactions is conducted, recommendations applicable to the general population regarding diet{\textemdash}specifically higher carbohydrate and lower fat composition{\textemdash}and normal sleep duration should continue to be emphasized among individuals with the investigated circadian-related gene variants.

}, keywords = {Adult, Alleles, Blood Glucose, Circadian Rhythm Signaling Peptides and Proteins, Cohort Studies, Diabetes Mellitus, Type 2, Diet, Fat-Restricted, European Continental Ancestry Group, Fasting, Female, Gene-Environment Interaction, Humans, Insulin Resistance, Male, Middle Aged, Multicenter Studies as Topic, Observational Studies as Topic, Phenotype, Polymorphism, Single Nucleotide, Sleep, Waist Circumference}, issn = {1935-5548}, doi = {10.2337/dc14-2709}, author = {Dashti, Hassan S and Follis, Jack L and Smith, Caren E and Tanaka, Toshiko and Garaulet, Marta and Gottlieb, Daniel J and Hruby, Adela and Jacques, Paul F and Kiefte-de Jong, Jessica C and Lamon-Fava, Stefania and Scheer, Frank A J L and Bartz, Traci M and Kovanen, Leena and Wojczynski, Mary K and Frazier-Wood, Alexis C and Ahluwalia, Tarunveer S and Per{\"a}l{\"a}, Mia-Maria and Jonsson, Anna and Muka, Taulant and Kalafati, Ioanna P and Mikkil{\"a}, Vera and Ordovas, Jose M} } @article {7578, title = {Genome-wide association meta-analysis of fish and EPA+DHA consumption in 17 US and European cohorts.}, journal = {PLoS One}, volume = {12}, year = {2017}, month = {2017}, pages = {e0186456}, abstract = {

BACKGROUND: Regular fish and omega-3 consumption may have several health benefits and are recommended by major dietary guidelines. Yet, their intakes remain remarkably variable both within and across populations, which could partly owe to genetic influences.

OBJECTIVE: To identify common genetic variants that influence fish and dietary eicosapentaenoic acid plus docosahexaenoic acid (EPA+DHA) consumption.

DESIGN: We conducted genome-wide association (GWA) meta-analysis of fish (n = 86,467) and EPA+DHA (n = 62,265) consumption in 17 cohorts of European descent from the CHARGE (Cohorts for Heart and Aging Research in Genomic Epidemiology) Consortium Nutrition Working Group. Results from cohort-specific GWA analyses (additive model) for fish and EPA+DHA consumption were adjusted for age, sex, energy intake, and population stratification, and meta-analyzed separately using fixed-effect meta-analysis with inverse variance weights (METAL software). Additionally, heritability was estimated in 2 cohorts.

RESULTS: Heritability estimates for fish and EPA+DHA consumption ranged from 0.13-0.24 and 0.12-0.22, respectively. A significant GWA for fish intake was observed for rs9502823 on chromosome 6: each copy of the minor allele (FreqA = 0.015) was associated with 0.029 servings/day (~1 serving/month) lower fish consumption (P = 1.96x10-8). No significant association was observed for EPA+DHA, although rs7206790 in the obesity-associated FTO gene was among top hits (P = 8.18x10-7). Post-hoc calculations demonstrated 95\% statistical power to detect a genetic variant associated with effect size of 0.05\% for fish and 0.08\% for EPA+DHA.

CONCLUSIONS: These novel findings suggest that non-genetic personal and environmental factors are principal determinants of the remarkable variation in fish consumption, representing modifiable targets for increasing intakes among all individuals. Genes underlying the signal at rs72838923 and mechanisms for the association warrant further investigation.

}, keywords = {Adult, Aged, Cohort Studies, Docosahexaenoic Acids, Eicosapentaenoic Acid, Europe, European Continental Ancestry Group, Female, Genome-Wide Association Study, Humans, Male, Middle Aged, Seafood, United States}, issn = {1932-6203}, doi = {10.1371/journal.pone.0186456}, author = {Mozaffarian, Dariush and Dashti, Hassan S and Wojczynski, Mary K and Chu, Audrey Y and Nettleton, Jennifer A and M{\"a}nnist{\"o}, Satu and Kristiansson, Kati and Reedik, M{\"a}gi and Lahti, Jari and Houston, Denise K and Cornelis, Marilyn C and van Rooij, Frank J A and Dimitriou, Maria and Kanoni, Stavroula and Mikkil{\"a}, Vera and Steffen, Lyn M and de Oliveira Otto, Marcia C and Qi, Lu and Psaty, Bruce and Djouss{\'e}, Luc and Rotter, Jerome I and Harald, Kennet and Perola, Markus and Rissanen, Harri and Jula, Antti and Krista, Fischer and Mihailov, Evelin and Feitosa, Mary F and Ngwa, Julius S and Xue, Luting and Jacques, Paul F and Per{\"a}l{\"a}, Mia-Maria and Palotie, Aarno and Liu, Yongmei and Nalls, Nike A and Ferrucci, Luigi and Hernandez, Dena and Manichaikul, Ani and Tsai, Michael Y and Kiefte-de Jong, Jessica C and Hofman, Albert and Uitterlinden, Andr{\'e} G and Rallidis, Loukianos and Ridker, Paul M and Rose, Lynda M and Buring, Julie E and Lehtim{\"a}ki, Terho and K{\"a}h{\"o}nen, Mika and Viikari, Jorma and Lemaitre, Rozenn and Salomaa, Veikko and Knekt, Paul and Metspalu, Andres and Borecki, Ingrid B and Cupples, L Adrienne and Eriksson, Johan G and Kritchevsky, Stephen B and Bandinelli, Stefania and Siscovick, David and Franco, Oscar H and Deloukas, Panos and Dedoussis, George and Chasman, Daniel I and Raitakari, Olli and Tanaka, Toshiko} }