@article {6938, title = {FTO genetic variants, dietary intake and body mass index: insights from 177,330 individuals.}, journal = {Hum Mol Genet}, volume = {23}, year = {2014}, month = {2014 Dec 20}, pages = {6961-72}, abstract = {

FTO is the strongest known genetic susceptibility locus for obesity. Experimental studies in animals suggest the potential roles of FTO in regulating food intake. The interactive relation among FTO variants, dietary intake and body mass index (BMI) is complex and results from previous often small-scale studies in humans are highly inconsistent. We performed large-scale analyses based on data from 177,330 adults (154 439 Whites, 5776 African Americans and 17 115 Asians) from 40 studies to examine: (i) the association between the FTO-rs9939609 variant (or a proxy single-nucleotide polymorphism) and total energy and macronutrient intake and (ii) the interaction between the FTO variant and dietary intake on BMI. The minor allele (A-allele) of the FTO-rs9939609 variant was associated with higher BMI in Whites (effect per allele = 0.34 [0.31, 0.37] kg/m(2), P = 1.9 {\texttimes} 10(-105)), and all participants (0.30 [0.30, 0.35] kg/m(2), P = 3.6 {\texttimes} 10(-107)). The BMI-increasing allele of the FTO variant showed a significant association with higher dietary protein intake (effect per allele = 0.08 [0.06, 0.10] \%, P = 2.4 {\texttimes} 10(-16)), and relative weak associations with lower total energy intake (-6.4 [-10.1, -2.6] kcal/day, P = 0.001) and lower dietary carbohydrate intake (-0.07 [-0.11, -0.02] \%, P = 0.004). The associations with protein (P = 7.5 {\texttimes} 10(-9)) and total energy (P = 0.002) were attenuated but remained significant after adjustment for BMI. We did not find significant interactions between the FTO variant and dietary intake of total energy, protein, carbohydrate or fat on BMI. Our findings suggest a positive association between the BMI-increasing allele of FTO variant and higher dietary protein intake and offer insight into potential link between FTO, dietary protein intake and adiposity.

}, keywords = {Adult, African Americans, Aged, Alleles, Asian Continental Ancestry Group, Body Mass Index, Dietary Carbohydrates, Dietary Fats, Dietary Proteins, Energy Intake, European Continental Ancestry Group, Female, Gene Frequency, Humans, Male, Middle Aged, Obesity, Polymorphism, Single Nucleotide, Proteins}, issn = {1460-2083}, doi = {10.1093/hmg/ddu411}, author = {Qi, Qibin and Kilpel{\"a}inen, Tuomas O and Downer, Mary K and Tanaka, Toshiko and Smith, Caren E and Sluijs, Ivonne and Sonestedt, Emily and Chu, Audrey Y and Renstrom, Frida and Lin, Xiaochen and {\"A}ngquist, Lars H and Huang, Jinyan and Liu, Zhonghua and Li, Yanping and Asif Ali, Muhammad and Xu, Min and Ahluwalia, Tarunveer Singh and Boer, Jolanda M A and Chen, Peng and Daimon, Makoto and Eriksson, Johan and Perola, Markus and Friedlander, Yechiel and Gao, Yu-Tang and Heppe, Denise H M and Holloway, John W and Houston, Denise K and Kanoni, Stavroula and Kim, Yu-Mi and Laaksonen, Maarit A and J{\"a}{\"a}skel{\"a}inen, Tiina and Lee, Nanette R and Lehtim{\"a}ki, Terho and Lemaitre, Rozenn N and Lu, Wei and Luben, Robert N and Manichaikul, Ani and M{\"a}nnist{\"o}, Satu and Marques-Vidal, Pedro and Monda, Keri L and Ngwa, Julius S and Perusse, Louis and van Rooij, Frank J A and Xiang, Yong-Bing and Wen, Wanqing and Wojczynski, Mary K and Zhu, Jingwen and Borecki, Ingrid B and Bouchard, Claude and Cai, Qiuyin and Cooper, Cyrus and Dedoussis, George V and Deloukas, Panos and Ferrucci, Luigi and Forouhi, Nita G and Hansen, Torben and Christiansen, Lene and Hofman, Albert and Johansson, Ingegerd and J{\o}rgensen, Torben and Karasawa, Shigeru and Khaw, Kay-Tee and Kim, Mi-Kyung and Kristiansson, Kati and Li, Huaixing and Lin, Xu and Liu, Yongmei and Lohman, Kurt K and Long, Jirong and Mikkil{\"a}, Vera and Mozaffarian, Dariush and North, Kari and Pedersen, Oluf and Raitakari, Olli and Rissanen, Harri and Tuomilehto, Jaakko and van der Schouw, Yvonne T and Uitterlinden, Andr{\'e} G and Zillikens, M Carola and Franco, Oscar H and Shyong Tai, E and Ou Shu, Xiao and Siscovick, David S and Toft, Ulla and Verschuren, W M Monique and Vollenweider, Peter and Wareham, Nicholas J and Witteman, Jacqueline C M and Zheng, Wei and Ridker, Paul M and Kang, Jae H and Liang, Liming and Jensen, Majken K and Curhan, Gary C and Pasquale, Louis R and Hunter, David J and Mohlke, Karen L and Uusitupa, Matti and Cupples, L Adrienne and Rankinen, Tuomo and Orho-Melander, Marju and Wang, Tao and Chasman, Daniel I and Franks, Paul W and S{\o}rensen, Thorkild I A and Hu, Frank B and Loos, Ruth J F and Nettleton, Jennifer A and Qi, Lu} } @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} } @article {7791, title = {Genome-wide association meta-analysis of circulating odd-numbered chain saturated fatty acids: Results from the CHARGE Consortium.}, journal = {PLoS One}, volume = {13}, year = {2018}, month = {2018}, pages = {e0196951}, abstract = {

BACKGROUND: Odd-numbered chain saturated fatty acids (OCSFA) have been associated with potential health benefits. Although some OCSFA (e.g., C15:0 and C17:0) are found in meats and dairy products, sources and metabolism of C19:0 and C23:0 are relatively unknown, and the influence of non-dietary determinants, including genetic factors, on circulating levels of OCSFA is not established.

OBJECTIVE: To elucidate the biological processes that influence circulating levels of OCSFA by investigating associations between genetic variation and OCSFA.

DESIGN: We performed a meta-analysis of genome-wide association studies (GWAS) of plasma phospholipid/erythrocyte levels of C15:0, C17:0, C19:0, and C23:0 among 11,494 individuals of European descent. We also investigated relationships between specific single nucleotide polymorphisms (SNPs) in the lactase (LCT) gene, associated with adult-onset lactase intolerance, with circulating levels of dairy-derived OCSFA, and evaluated associations of candidate sphingolipid genes with C23:0 levels.

RESULTS: We found no genome-wide significant evidence that common genetic variation is associated with circulating levels of C15:0 or C23:0. In two cohorts with available data, we identified one intronic SNP (rs13361131) in myosin X gene (MYO10) associated with C17:0 level (P = 1.37{\texttimes}10-8), and two intronic SNP (rs12874278 and rs17363566) in deleted in lymphocytic leukemia 1 (DLEU1) region associated with C19:0 level (P = 7.07{\texttimes}10-9). In contrast, when using a candidate-gene approach, we found evidence that three SNPs in LCT (rs11884924, rs16832067, and rs3816088) are associated with circulating C17:0 level (adjusted P = 4{\texttimes}10-2). In addition, nine SNPs in the ceramide synthase 4 (CERS4) region were associated with circulating C23:0 levels (adjusted P<5{\texttimes}10-2).

CONCLUSIONS: Our findings suggest that circulating levels of OCSFA may be predominantly influenced by non-genetic factors. SNPs associated with C17:0 level in the LCT gene may reflect genetic influence in dairy consumption or in metabolism of dairy foods. SNPs associated with C23:0 may reflect a role of genetic factors in the synthesis of sphingomyelin.

}, keywords = {Fatty Acids, Genome-Wide Association Study, Humans, Introns, Lactase, Myosins, Polymorphism, Single Nucleotide, Sphingomyelins, Sphingosine N-Acyltransferase, Tumor Suppressor Proteins}, issn = {1932-6203}, doi = {10.1371/journal.pone.0196951}, author = {de Oliveira Otto, Marcia C and Lemaitre, Rozenn N and Sun, Qi and King, Irena B and Wu, Jason H Y and Manichaikul, Ani and Rich, Stephen S and Tsai, Michael Y and Chen, Y D and Fornage, Myriam and Weihua, Guan and Aslibekyan, Stella and Irvin, Marguerite R and Kabagambe, Edmond K and Arnett, Donna K and Jensen, Majken K and McKnight, Barbara and Psaty, Bruce M and Steffen, Lyn M and Smith, Caren E and Riserus, Ulf and Lind, Lars and Hu, Frank B and Rimm, Eric B and Siscovick, David S and Mozaffarian, Dariush} } @article {7576, title = {Sugar-sweetened beverage intake associations with fasting glucose and insulin concentrations are not modified by selected genetic variants in a ChREBP-FGF21 pathway: a meta-analysis.}, journal = {Diabetologia}, volume = {61}, year = {2018}, month = {2018 Feb}, pages = {317-330}, abstract = {

AIMS/HYPOTHESIS: Sugar-sweetened beverages (SSBs) are a major dietary contributor to fructose intake. A molecular pathway involving the carbohydrate responsive element-binding protein (ChREBP) and the metabolic hormone fibroblast growth factor 21 (FGF21) may influence sugar metabolism and, thereby, contribute to fructose-induced metabolic disease. We hypothesise that common variants in 11 genes involved in fructose metabolism and the ChREBP-FGF21 pathway may interact with SSB intake to exacerbate positive associations between higher SSB intake and glycaemic traits.

METHODS: Data from 11 cohorts (six discovery and five replication) in the CHARGE (Cohorts for Heart and Aging Research in Genomic Epidemiology) Consortium provided association and interaction results from 34,748 adults of European descent. SSB intake (soft drinks, fruit punches, lemonades or other fruit drinks) was derived from food-frequency questionnaires and food diaries. In fixed-effects meta-analyses, we quantified: (1) the associations between SSBs and glycaemic traits (fasting glucose and fasting insulin); and (2) the interactions between SSBs and 18 independent SNPs related to the ChREBP-FGF21 pathway.

RESULTS: In our combined meta-analyses of discovery and replication cohorts, after adjustment for age, sex, energy intake, BMI and other dietary covariates, each additional serving of SSB intake was associated with higher fasting glucose (β~{\textpm}~SE 0.014~{\textpm}~0.004 [mmol/l], p~=~1.5~{\texttimes}~10-3) and higher fasting insulin (0.030~{\textpm}~0.005 [log e pmol/l], p~=~2.0~{\texttimes}~10-10). No significant interactions on glycaemic traits were observed between SSB intake and selected SNPs. While a suggestive interaction was observed in the discovery cohorts with a SNP (rs1542423) in the β-Klotho (KLB) locus on fasting insulin (0.030~{\textpm}~0.011 log e pmol/l, uncorrected p~=~0.006), results in the replication cohorts and combined meta-analyses were non-significant.

CONCLUSIONS/INTERPRETATION: In this large meta-analysis, we observed that SSB intake was associated with higher fasting glucose and insulin. Although a suggestive interaction with a genetic variant in the ChREBP-FGF21 pathway was observed in the discovery cohorts, this observation was not confirmed in the replication analysis.

TRIAL REGISTRATION: Trials related to this study were registered at clinicaltrials.gov as NCT00005131 (Atherosclerosis Risk in Communities), NCT00005133 (Cardiovascular Health Study), NCT00005121 (Framingham Offspring Study), NCT00005487 (Multi-Ethnic Study of Atherosclerosis) and NCT00005152 (Nurses{\textquoteright} Health Study).

}, issn = {1432-0428}, doi = {10.1007/s00125-017-4475-0}, author = {McKeown, Nicola M and Dashti, Hassan S and Ma, Jiantao and Haslam, Danielle E and Kiefte-de Jong, Jessica C and Smith, Caren E and Tanaka, Toshiko and Graff, Mariaelisa and Lemaitre, Rozenn N and Rybin, Denis and Sonestedt, Emily and Frazier-Wood, Alexis C and Mook-Kanamori, Dennis O and Li, Yanping and Wang, Carol A and Leermakers, Elisabeth T M and Mikkil{\"a}, Vera and Young, Kristin L and Mukamal, Kenneth J and Cupples, L Adrienne and Schulz, Christina-Alexandra and Chen, Tzu-An and Li-Gao, Ruifang and Huang, Tao and Oddy, Wendy H and Raitakari, Olli and Rice, Kenneth and Meigs, James B and Ericson, Ulrika and Steffen, Lyn M and Rosendaal, Frits R and Hofman, Albert and K{\"a}h{\"o}nen, Mika and Psaty, Bruce M and Brunkwall, Louise and Uitterlinden, Andr{\'e} G and Viikari, Jorma and Siscovick, David S and Sepp{\"a}l{\"a}, Ilkka and North, Kari E and Mozaffarian, Dariush and Dupuis, Jos{\'e}e and Orho-Melander, Marju and Rich, Stephen S and de Mutsert, Ren{\'e}e and Qi, Lu and Pennell, Craig E and Franco, Oscar H and Lehtim{\"a}ki, Terho and Herman, Mark A} } @article {8099, title = {Genome-wide association study of breakfast skipping links clock regulation with food timing.}, journal = {Am J Clin Nutr}, year = {2019}, month = {2019 Jun 13}, abstract = {

BACKGROUND: Little is known about the contribution of genetic variation to food timing, and breakfast has been determined to exhibit the most heritable meal timing. As breakfast timing and skipping are not routinely measured in large cohort studies, alternative approaches include analyses of correlated traits.

OBJECTIVES: The aim of this study was to elucidate breakfast skipping genetic variants through a proxy-phenotype genome-wide association study (GWAS) for breakfast cereal skipping, a commonly assessed correlated trait.

METHODS: We leveraged the statistical power of the UK Biobank (n~=~193,860) to identify genetic variants related to breakfast cereal skipping as a proxy-phenotype for breakfast skipping and applied several in silico approaches to investigate mechanistic functions and links to traits/diseases. Next, we attempted validation of our approach in smaller breakfast skipping GWAS from the TwinUK (n~=~2,006) and the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium (n~=~11,963).

RESULTS: In the UK Biobank, we identified 6 independent GWAS variants, including those implicated for caffeine (ARID3B/CYP1A1), carbohydrate metabolism (FGF21), schizophrenia (ZNF804A), and encoding enzymes important for N6-methyladenosine RNA transmethylation (METTL4, YWHAB, and YTHDF3), which regulates the pace of the circadian clock. Expression of identified genes was enriched in the cerebellum. Genome-wide correlation analyses indicated positive correlations with anthropometric traits. Through Mendelian randomization (MR), we observed causal links between genetically determined breakfast skipping and higher body mass index, more depressive symptoms, and smoking. In bidirectional MR, we demonstrated a causal link between being an evening person and skipping breakfast, but not vice versa. We observed association of our signals in an independent breakfast skipping GWAS in another British cohort (P~=~0.032), TwinUK, but not in a meta-analysis of non-British cohorts from the CHARGE consortium (P~=~0.095).

CONCLUSIONS: Our proxy-phenotype GWAS identified 6 genetic variants for breakfast skipping, linking clock regulation with food timing and suggesting a possible beneficial role of regular breakfast intake as part of a healthy lifestyle.

}, issn = {1938-3207}, doi = {10.1093/ajcn/nqz076}, author = {Dashti, Hassan S and Merino, Jordi and Lane, Jacqueline M and Song, Yanwei and Smith, Caren E and Tanaka, Toshiko and McKeown, Nicola M and Tucker, Chandler and Sun, Dianjianyi and Bartz, Traci M and Li-Gao, Ruifang and Nisa, Hoirun and Reutrakul, Sirimon and Lemaitre, Rozenn N and Alshehri, Tahani M and de Mutsert, Ren{\'e}e and Bazzano, Lydia and Qi, Lu and Knutson, Kristen L and Psaty, Bruce M and Mook-Kanamori, Dennis O and Perica, Vesna Boraska and Neuhouser, Marian L and Scheer, Frank A J L and Rutter, Martin K and Garaulet, Marta and Saxena, Richa} } @article {8830, title = {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.}, journal = {Circ Genom Precis Med}, volume = {14}, year = {2021}, month = {2021 Aug}, pages = {e003288}, abstract = {

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.

}, issn = {2574-8300}, doi = {10.1161/CIRCGEN.120.003288}, author = {Haslam, Danielle E and Peloso, Gina M and Guirette, Melanie and Imamura, Fumiaki and Bartz, Traci M and Pitsillides, Achilleas N and Wang, Carol A and Li-Gao, Ruifang and Westra, Jason M and Pitk{\"a}nen, Niina and Young, Kristin L and Graff, Mariaelisa and Wood, Alexis C and Braun, Kim V E and Luan, Jian{\textquoteright}an and K{\"a}h{\"o}nen, Mika and Kiefte-de Jong, Jessica C and Ghanbari, Mohsen and Tintle, Nathan and Lemaitre, Rozenn N and Mook-Kanamori, Dennis O and North, Kari and Helminen, Mika and Mossavar-Rahmani, Yasmin and Snetselaar, Linda and Martin, Lisa W and Viikari, Jorma S and Oddy, Wendy H and Pennell, Craig E and Rosendall, Frits R and Ikram, M Arfan and Uitterlinden, Andr{\'e} G and Psaty, Bruce M and Mozaffarian, Dariush and Rotter, Jerome I and Taylor, Kent D and Lehtim{\"a}ki, Terho and Raitakari, Olli T and Livingston, Kara A and Voortman, Trudy and Forouhi, Nita G and Wareham, Nick J and de Mutsert, Ren{\'e}e and Rich, Steven S and Manson, JoAnn E and Mora, Samia and Ridker, Paul M and Merino, Jordi and Meigs, James B and Dashti, Hassan S and Chasman, Daniel I and Lichtenstein, Alice H and Smith, Caren E and Dupuis, Jos{\'e}e and Herman, Mark A and McKeown, Nicola M} }