@article {1222, title = {Interactions of dietary whole-grain intake with fasting glucose- and insulin-related genetic loci in individuals of European descent: a meta-analysis of 14 cohort studies.}, journal = {Diabetes Care}, volume = {33}, year = {2010}, month = {2010 Dec}, pages = {2684-91}, abstract = {

OBJECTIVE: Whole-grain foods are touted for multiple health benefits, including enhancing insulin sensitivity and reducing type 2 diabetes risk. Recent genome-wide association studies (GWAS) have identified several single nucleotide polymorphisms (SNPs) associated with fasting glucose and insulin concentrations in individuals free of diabetes. We tested the hypothesis that whole-grain food intake and genetic variation interact to influence concentrations of fasting glucose and insulin.

RESEARCH DESIGN AND METHODS: Via meta-analysis of data from 14 cohorts comprising \~{} 48,000 participants of European descent, we studied interactions of whole-grain intake with loci previously associated in GWAS with fasting glucose (16 loci) and/or insulin (2 loci) concentrations. For tests of interaction, we considered a P value <0.0028 (0.05 of 18 tests) as statistically significant.

RESULTS: Greater whole-grain food intake was associated with lower fasting glucose and insulin concentrations independent of demographics, other dietary and lifestyle factors, and BMI (β [95\% CI] per 1-serving-greater whole-grain intake: -0.009 mmol/l glucose [-0.013 to -0.005], P < 0.0001 and -0.011 pmol/l [ln] insulin [-0.015 to -0.007], P = 0.0003). No interactions met our multiple testing-adjusted statistical significance threshold. The strongest SNP interaction with whole-grain intake was rs780094 (GCKR) for fasting insulin (P = 0.006), where greater whole-grain intake was associated with a smaller reduction in fasting insulin concentrations in those with the insulin-raising allele.

CONCLUSIONS: Our results support the favorable association of whole-grain intake with fasting glucose and insulin and suggest a potential interaction between variation in GCKR and whole-grain intake in influencing fasting insulin concentrations.

}, keywords = {Adult, Aged, Blood Glucose, Edible Grain, European Continental Ancestry Group, Fasting, Female, Genetic Loci, Genome-Wide Association Study, Genotype, Humans, Insulin, Male, Middle Aged, Polymorphism, Single Nucleotide}, issn = {1935-5548}, doi = {10.2337/dc10-1150}, author = {Nettleton, Jennifer A and McKeown, Nicola M and Kanoni, Stavroula and Lemaitre, Rozenn N and Hivert, Marie-France and Ngwa, Julius and van Rooij, Frank J A and Sonestedt, Emily and Wojczynski, Mary K and Ye, Zheng and Tanaka, Tosh and Garcia, Melissa and Anderson, Jennifer S and Follis, Jack L and Djouss{\'e}, Luc and Mukamal, Kenneth and Papoutsakis, Constantina and Mozaffarian, Dariush and Zillikens, M Carola and Bandinelli, Stefania and Bennett, Amanda J and Borecki, Ingrid B and Feitosa, Mary F and Ferrucci, Luigi and Forouhi, Nita G and Groves, Christopher J and Hallmans, G{\"o}ran and Harris, Tamara and Hofman, Albert and Houston, Denise K and Hu, Frank B and Johansson, Ingegerd and Kritchevsky, Stephen B and Langenberg, Claudia and Launer, Lenore and Liu, Yongmei and Loos, Ruth J and Nalls, Michael and Orho-Melander, Marju and Renstrom, Frida and Rice, Kenneth and Riserus, Ulf and Rolandsson, Olov and Rotter, Jerome I and Saylor, Georgia and Sijbrands, Eric J G and Sjogren, Per and Smith, Albert and Steingr{\'\i}msd{\'o}ttir, Laufey and Uitterlinden, Andr{\'e} G and Wareham, Nicholas J and Prokopenko, Inga and Pankow, James S and van Duijn, Cornelia M and Florez, Jose C and Witteman, Jacqueline C M and Dupuis, Jos{\'e}e and Dedoussis, George V and Ordovas, Jose M and Ingelsson, Erik and Cupples, L Adrienne and Siscovick, David S and Franks, Paul W and Meigs, James B} } @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 {6687, title = {Dietary fatty acids modulate associations between genetic variants and circulating fatty acids in plasma and erythrocyte membranes: Meta-analysis of nine studies in the CHARGE consortium.}, journal = {Mol Nutr Food Res}, volume = {59}, year = {2015}, month = {2015 Jul}, pages = {1373-83}, abstract = {

SCOPE: Tissue concentrations of omega-3 fatty acids may reduce cardiovascular disease risk, and genetic variants are associated with circulating fatty acids concentrations. Whether dietary fatty acids interact with genetic variants to modify circulating omega-3 fatty acids is unclear. We evaluated interactions between genetic variants and fatty acid intakes for circulating alpha-linoleic acid, eicosapentaenoic acid, docosahexaenoic acid, and docosapentaenoic acid.

METHODS AND RESULTS: We conducted meta-analyses (N = 11~668) evaluating interactions between dietary fatty acids and genetic variants (rs174538 and rs174548 in FADS1 (fatty acid desaturase 1), rs7435 in AGPAT3 (1-acyl-sn-glycerol-3-phosphate), rs4985167 in PDXDC1 (pyridoxal-dependent decarboxylase domain-containing 1), rs780094 in GCKR (glucokinase regulatory protein), and rs3734398 in ELOVL2 (fatty acid elongase 2)). Stratification by measurement compartment (plasma versus erthyrocyte) revealed compartment-specific interactions between FADS1 rs174538 and rs174548 and dietary alpha-linolenic acid and linoleic acid for docosahexaenoic acid and docosapentaenoic acid.

CONCLUSION: Our findings reinforce earlier reports that genetically based differences in circulating fatty acids may be partially due to differences in the conversion of fatty acid precursors. Further, fatty acids measurement compartment may modify gene-diet relationships, and considering compartment may improve the detection of gene-fatty acids interactions for circulating fatty acid outcomes.

}, keywords = {Acetyltransferases, Acyltransferases, Adaptor Proteins, Signal Transducing, Carboxy-Lyases, Diet, Docosahexaenoic Acids, Eicosapentaenoic Acid, Erythrocyte Membrane, Fatty Acid Desaturases, Fatty Acids, Fatty Acids, Omega-3, Female, Humans, Male, Middle Aged, Polymorphism, Single Nucleotide}, issn = {1613-4133}, doi = {10.1002/mnfr.201400734}, author = {Smith, Caren E and Follis, Jack L and Nettleton, Jennifer A and Foy, Millennia and Wu, Jason H Y and Ma, Yiyi and Tanaka, Toshiko and Manichakul, Ani W and Wu, Hongyu and Chu, Audrey Y and Steffen, Lyn M and Fornage, Myriam and Mozaffarian, Dariush and Kabagambe, Edmond K and Ferruci, Luigi and Chen, Yii-Der Ida and Rich, Stephen S and Djouss{\'e}, Luc and Ridker, Paul M and Tang, Weihong and McKnight, Barbara and Tsai, Michael Y and Bandinelli, Stefania and Rotter, Jerome I and Hu, Frank B and Chasman, Daniel I and Psaty, Bruce M and Arnett, Donna K and King, Irena B and Sun, Qi and Wang, Lu and Lumley, Thomas and Chiuve, Stephanie E and Siscovick, David S and Ordovas, Jose M and Lemaitre, Rozenn N} } @article {6802, title = {Gene {\texttimes} dietary pattern interactions in obesity: analysis of up to 68 317 adults of European ancestry.}, journal = {Hum Mol Genet}, volume = {24}, year = {2015}, month = {2015 Aug 15}, pages = {4728-38}, abstract = {

Obesity is highly heritable. Genetic variants showing robust associations with obesity traits have been identified through genome-wide association studies. We investigated whether a composite score representing healthy diet modifies associations of these variants with obesity traits. Totally, 32 body mass index (BMI)- and 14 waist-hip ratio (WHR)-associated single nucleotide polymorphisms were genotyped, and genetic risk scores (GRS) were calculated in 18 cohorts of European ancestry (n = 68 317). Diet score was calculated based on self-reported intakes of whole grains, fish, fruits, vegetables, nuts/seeds (favorable) and red/processed meats, sweets, sugar-sweetened beverages and fried potatoes (unfavorable). Multivariable adjusted, linear regression within each cohort followed by inverse variance-weighted, fixed-effects meta-analysis was used to characterize: (a) associations of each GRS with BMI and BMI-adjusted WHR~and (b) diet score modification of genetic associations with BMI and BMI-adjusted WHR. Nominally significant interactions (P = 0.006-0.04) were observed between the diet score and WHR-GRS (but not BMI-GRS), two WHR loci (GRB14 rs10195252; LYPLAL1 rs4846567) and two BMI loci (LRRN6C rs10968576; MTIF3 rs4771122), for the respective BMI-adjusted WHR or BMI outcomes. Although the magnitudes of these select interactions were small, our data indicated that associations between genetic predisposition and obesity traits were stronger with a healthier diet. Our findings generate interesting hypotheses; however, experimental and functional studies are needed to determine their clinical relevance.

}, keywords = {Adult, Body Mass Index, Case-Control Studies, Diet, Western, Epistasis, Genetic, European Continental Ancestry Group, Female, Genetic Loci, Genome-Wide Association Study, Humans, Male, Obesity, Polymorphism, Single Nucleotide}, issn = {1460-2083}, doi = {10.1093/hmg/ddv186}, author = {Nettleton, Jennifer A and Follis, Jack L and Ngwa, Julius S and Smith, Caren E and Ahmad, Shafqat and Tanaka, Toshiko and Wojczynski, Mary K and Voortman, Trudy and Lemaitre, Rozenn N and Kristiansson, Kati and Nuotio, Marja-Liisa and Houston, Denise K and Per{\"a}l{\"a}, Mia-Maria and Qi, Qibin and Sonestedt, Emily and Manichaikul, Ani and Kanoni, Stavroula and Ganna, Andrea and Mikkil{\"a}, Vera and North, Kari E and Siscovick, David S and Harald, Kennet and McKeown, Nicola M and Johansson, Ingegerd and Rissanen, Harri and Liu, Yongmei and Lahti, Jari and Hu, Frank B and Bandinelli, Stefania and Rukh, Gull and Rich, Stephen and Booij, Lisanne and Dmitriou, Maria and Ax, Erika and Raitakari, Olli and Mukamal, Kenneth and M{\"a}nnist{\"o}, Satu and Hallmans, G{\"o}ran and Jula, Antti and Ericson, Ulrika and Jacobs, David R and van Rooij, Frank J A and Deloukas, Panos and Sjogren, Per and K{\"a}h{\"o}nen, Mika and Djouss{\'e}, Luc and Perola, Markus and Barroso, In{\^e}s and Hofman, Albert and Stirrups, Kathleen and Viikari, Jorma and Uitterlinden, Andr{\'e} G and Kalafati, Ioanna P and Franco, Oscar H and Mozaffarian, Dariush and Salomaa, Veikko and Borecki, Ingrid B and Knekt, Paul and Kritchevsky, Stephen B and Eriksson, Johan G and Dedoussis, George V and Qi, Lu and Ferrucci, Luigi and Orho-Melander, Marju and Zillikens, M Carola and Ingelsson, Erik and Lehtim{\"a}ki, Terho and Renstrom, Frida and Cupples, L Adrienne and Loos, Ruth J F and Franks, Paul W} } @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 {6614, title = {Habitual sleep duration is associated with BMI and macronutrient intake and may be modified by CLOCK genetic variants.}, journal = {Am J Clin Nutr}, volume = {101}, year = {2015}, month = {2015 Jan}, pages = {135-43}, abstract = {

BACKGROUND: Short sleep duration has been associated with greater risks of obesity, hypertension, diabetes, and cardiovascular disease. Also, common genetic variants in the human Circadian Locomotor Output Cycles Kaput (CLOCK) show associations with ghrelin and total energy intake.

OBJECTIVES: We examined associations between habitual sleep duration, body mass index (BMI), and macronutrient intake and assessed whether CLOCK variants modify these associations.

DESIGN: We conducted inverse-variance weighted, fixed-effect meta-analyses of results of adjusted associations of sleep duration and BMI and macronutrient intake as percentages of total energy as well as interactions with CLOCK variants from 9 cohort studies including up to 14,906 participants of European descent from the Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium.

RESULTS: We observed a significant association between sleep duration and lower BMI (β {\textpm} SE = 0.16 {\textpm} 0.04, P < 0.0001) in the overall sample; however, associations between sleep duration and relative macronutrient intake were evident in age- and sex-stratified analyses only. We observed a significant association between sleep duration and lower saturated fatty acid intake in younger (aged 20-64 y) adults (men: 0.11 {\textpm} 0.06\%, P = 0.03; women: 0.10 {\textpm} 0.05\%, P = 0.04) and with lower carbohydrate (-0.31 {\textpm} 0.12\%, P < 0.01), higher total fat (0.18 {\textpm} 0.09\%, P = 0.05), and higher PUFA (0.05 {\textpm} 0.02\%, P = 0.02) intakes in older (aged 65-80 y) women. In addition, the following 2 nominally significant interactions were observed: between sleep duration and rs12649507 on PUFA intake and between sleep duration and rs6858749 on protein intake.

CONCLUSIONS: Our results indicate that longer habitual sleep duration is associated with lower BMI and age- and sex-specific favorable dietary behaviors. Differences in the relative intake of specific macronutrients associated with short sleep duration could, at least in part, explain previously reported associations between short sleep duration and chronic metabolic abnormalities. In addition, the influence of obesity-associated CLOCK variants on the association between sleep duration and macronutrient intake suggests that longer habitual sleep duration could ameliorate the genetic predisposition to obesity via a favorable dietary profile.

}, keywords = {Adult, Body Mass Index, CLOCK Proteins, Cohort Studies, Cross-Sectional Studies, Diet, Dietary Proteins, Energy Intake, European Continental Ancestry Group, Fatty Acids, Unsaturated, Female, Gene-Environment Interaction, Genetic Predisposition to Disease, Humans, Male, Middle Aged, Obesity, Polymorphism, Single Nucleotide, Sleep, Young Adult}, issn = {1938-3207}, doi = {10.3945/ajcn.114.095026}, author = {Dashti, Hassan S and Follis, Jack L and Smith, Caren E and Tanaka, Toshiko and Cade, Brian E and Gottlieb, Daniel J and Hruby, Adela and Jacques, Paul F and Lamon-Fava, Stefania and Richardson, Kris and Saxena, Richa and Scheer, Frank A J L and Kovanen, Leena and Bartz, Traci M and Per{\"a}l{\"a}, Mia-Maria and Jonsson, Anna and Frazier-Wood, Alexis C and Kalafati, Ioanna-Panagiota and Mikkil{\"a}, Vera and Partonen, Timo and Lemaitre, Rozenn N and Lahti, Jari and Hernandez, Dena G and Toft, Ulla and Johnson, W Craig and Kanoni, Stavroula and Raitakari, Olli T and Perola, Markus and Psaty, Bruce M and Ferrucci, Luigi and Grarup, Niels and Highland, Heather M and Rallidis, Loukianos and K{\"a}h{\"o}nen, Mika and Havulinna, Aki S and Siscovick, David S and R{\"a}ikk{\"o}nen, Katri and J{\o}rgensen, Torben and Rotter, Jerome I and Deloukas, Panos and Viikari, Jorma S A and Mozaffarian, Dariush and Linneberg, Allan and Sepp{\"a}l{\"a}, Ilkka and Hansen, Torben and Salomaa, Veikko and Gharib, Sina A and Eriksson, Johan G and Bandinelli, Stefania and Pedersen, Oluf and Rich, Stephen S and Dedoussis, George and Lehtim{\"a}ki, Terho and Ordovas, Jose M} } @article {6951, title = {Interaction of methylation-related genetic variants with circulating fatty acids on plasma lipids: a meta-analysis of 7 studies and methylation analysis of 3 studies in the Cohorts for Heart and Aging Research in Genomic Epidemiology consortium.}, journal = {Am J Clin Nutr}, volume = {103}, year = {2016}, month = {2016 Feb}, pages = {567-78}, abstract = {

BACKGROUND: DNA methylation is influenced by diet and single nucleotide polymorphisms (SNPs), and methylation modulates gene expression.

OBJECTIVE: We aimed to explore whether the gene-by-diet interactions on blood lipids act through DNA methylation.

DESIGN: We selected 7 SNPs on the basis of predicted relations in fatty acids, methylation, and lipids. We conducted a meta-analysis and a methylation and mediation analysis with the use of data from the CHARGE (Cohorts for Heart and Aging Research in Genomic Epidemiology) consortium and the ENCODE (Encyclopedia of DNA Elements) consortium.

RESULTS: On the basis of the meta-analysis of 7 cohorts in the CHARGE consortium, higher plasma HDL cholesterol was associated with fewer C alleles at ATP-binding cassette subfamily A member 1 (ABCA1) rs2246293 (β = -0.6 mg/dL, P = 0.015) and higher circulating eicosapentaenoic acid (EPA) (β = 3.87 mg/dL, P = 5.62 {\texttimes} 10(21)). The difference in HDL cholesterol associated with higher circulating EPA was dependent on genotypes at rs2246293, and it was greater for each additional C allele (β = 1.69 mg/dL, P = 0.006). In the GOLDN (Genetics of Lipid Lowering Drugs and Diet Network) study, higher ABCA1 promoter cg14019050 methylation was associated with more C alleles at rs2246293 (β = 8.84\%, P = 3.51 {\texttimes} 10(18)) and lower circulating EPA (β = -1.46\%, P = 0.009), and the mean difference in methylation of cg14019050 that was associated with higher EPA was smaller with each additional C allele of rs2246293 (β = -2.83\%, P = 0.007). Higher ABCA1 cg14019050 methylation was correlated with lower ABCA1 expression (r = -0.61, P = 0.009) in the ENCODE consortium and lower plasma HDL cholesterol in the GOLDN study (r = -0.12, P = 0.0002). An additional mediation analysis was meta-analyzed across the GOLDN study, Cardiovascular Health Study, and the Multi-Ethnic Study of Atherosclerosis. Compared with the model without the adjustment of cg14019050 methylation, the model with such adjustment provided smaller estimates of the mean plasma HDL cholesterol concentration in association with both the rs2246293 C allele and EPA and a smaller difference by rs2246293 genotypes in the EPA-associated HDL cholesterol. However, the differences between 2 nested models were NS (P > 0.05).

CONCLUSION: We obtained little evidence that the gene-by-fatty acid interactions on blood lipids act through DNA methylation.

}, keywords = {Apolipoproteins E, ATP Binding Cassette Transporter 1, Cholesterol, HDL, Cohort Studies, Diet, DNA Methylation, Eicosapentaenoic Acid, Epigenesis, Genetic, Fatty Acids, Gene Expression Regulation, Humans, Lipids, Polymorphism, Single Nucleotide, Promoter Regions, Genetic, Triglycerides}, issn = {1938-3207}, doi = {10.3945/ajcn.115.112987}, author = {Ma, Yiyi and Follis, Jack L and Smith, Caren E and Tanaka, Toshiko and Manichaikul, Ani W and Chu, Audrey Y and Samieri, Cecilia and Zhou, Xia and Guan, Weihua and Wang, Lu and Biggs, Mary L and Chen, Yii-der I and Hernandez, Dena G and Borecki, Ingrid and Chasman, Daniel I and Rich, Stephen S and Ferrucci, Luigi and Irvin, Marguerite Ryan and Aslibekyan, Stella and Zhi, Degui and Tiwari, Hemant K and Claas, Steven A and Sha, Jin and Kabagambe, Edmond K and Lai, Chao-Qiang and Parnell, Laurence D and Lee, Yu-Chi and Amouyel, Philippe and Lambert, Jean-Charles and Psaty, Bruce M and King, Irena B and Mozaffarian, Dariush and McKnight, Barbara and Bandinelli, Stefania and Tsai, Michael Y and Ridker, Paul M and Ding, Jingzhong and Mstat, Kurt Lohmant and Liu, Yongmei and Sotoodehnia, Nona and Barberger-Gateau, Pascale and Steffen, Lyn M and Siscovick, David S and Absher, Devin and Arnett, Donna K and Ordovas, Jose M and Lemaitre, Rozenn N} } @article {7588, title = {Genome-Wide Interactions with Dairy Intake for Body Mass Index in Adults of European Descent.}, journal = {Mol Nutr Food Res}, year = {2017}, month = {2017 Sep 21}, abstract = {

SCOPE: Body weight responds variably to the intake of dairy foods. Genetic variation may contribute to inter-individual variability in associations between body weight and dairy consumption.

METHODS AND RESULTS: A genome-wide interaction study to discover genetic variants that account for variation in BMI in the context of low-fat, high-fat and total dairy intake in cross-sectional analysis was conducted. Data from nine discovery studies (up to 25 513 European descent individuals) were meta-analyzed. Twenty-six genetic variants reached the selected significance threshold (p-interaction <10-7) , and six independent variants (LINC01512-rs7751666, PALM2/AKAP2-rs914359, ACTA2-rs1388, PPP1R12A-rs7961195, LINC00333-rs9635058, AC098847.1-rs1791355) were evaluated meta-analytically for replication of interaction in up to 17 675 individuals. Variant rs9635058 (128 kb 3{\textquoteright} of LINC00333) was replicated (p-interaction = 0.004). In the discovery cohorts, rs9635058 interacted with dairy (p-interaction = 7.36 {\texttimes} 10-8) such that each serving of low-fat dairy was associated with 0.225 kg m-2 lower BMI per each additional copy of the effect allele (A). A second genetic variant (ACTA2-rs1388) approached interaction replication significance for low-fat dairy exposure.

CONCLUSION: Body weight responses to dairy intake may be modified by genotype, in that greater dairy intake may protect a genetic subgroup from higher body weight.

}, issn = {1613-4133}, doi = {10.1002/mnfr.201700347}, author = {Smith, Caren E and Follis, Jack L and Dashti, Hassan S and Tanaka, Toshiko and Graff, Mariaelisa and Fretts, Amanda M and Kilpel{\"a}inen, Tuomas O and Wojczynski, Mary K and Richardson, Kris and Nalls, Mike A and Schulz, Christina-Alexandra and Liu, Yongmei and Frazier-Wood, Alexis C and van Eekelen, Esther and Wang, Carol and de Vries, Paul S and Mikkil{\"a}, Vera and Rohde, Rebecca and Psaty, Bruce M and Hansen, Torben and Feitosa, Mary F and Lai, Chao-Qiang and Houston, Denise K and Ferruci, Luigi and Ericson, Ulrika and Wang, Zhe and de Mutsert, Ren{\'e}e and Oddy, Wendy H and de Jonge, Ester A L and Sepp{\"a}l{\"a}, Ilkka and Justice, Anne E and Lemaitre, Rozenn N and S{\o}rensen, Thorkild I A and Province, Michael A and Parnell, Laurence D and Garcia, Melissa E and Bandinelli, Stefania and Orho-Melander, Marju and Rich, Stephen S and Rosendaal, Frits R and Pennell, Craig E and Kiefte-de Jong, Jessica C and K{\"a}h{\"o}nen, Mika and Young, Kristin L and Pedersen, Oluf and Aslibekyan, Stella and Rotter, Jerome I and Mook-Kanamori, Dennis O and Zillikens, M Carola and Raitakari, Olli T and North, Kari E and Overvad, Kim and Arnett, Donna K and Hofman, Albert and Lehtim{\"a}ki, Terho and Tj{\o}nneland, Anne and Uitterlinden, Andr{\'e} G and Rivadeneira, Fernando and Franco, Oscar H and German, J Bruce and Siscovick, David S and Cupples, L Adrienne and Ordovas, Jose M} }