@article {6163, title = {Genome-wide meta-analysis of observational studies shows common genetic variants associated with macronutrient intake.}, journal = {Am J Clin Nutr}, volume = {97}, year = {2013}, month = {2013 Jun}, pages = {1395-402}, abstract = {

BACKGROUND: Macronutrient intake varies substantially between individuals, and there is evidence that this variation is partly accounted for by genetic variants.

OBJECTIVE: The objective of the study was to identify common genetic variants that are associated with macronutrient intake.

DESIGN: We performed 2-stage genome-wide association (GWA) meta-analysis of macronutrient intake in populations of European descent. Macronutrients were assessed by using food-frequency questionnaires and analyzed as percentages of total energy consumption from total fat, protein, and carbohydrate. From the discovery GWA (n = 38,360), 35 independent loci associated with macronutrient intake at P < 5 {\texttimes} 10(-6) were identified and taken forward to replication in 3 additional cohorts (n = 33,533) from the DietGen Consortium. For one locus, fat mass obesity-associated protein (FTO), cohorts with Illumina MetaboChip genotype data (n = 7724) provided additional replication data.

RESULTS: A variant in the chromosome 19 locus (rs838145) was associated with higher carbohydrate (β {\textpm} SE: 0.25 {\textpm} 0.04\%; P = 1.68 {\texttimes} 10(-8)) and lower fat (β {\textpm} SE: -0.21 {\textpm} 0.04\%; P = 1.57 {\texttimes} 10(-9)) consumption. A candidate gene in this region, fibroblast growth factor 21 (FGF21), encodes a fibroblast growth factor involved in glucose and lipid metabolism. The variants in this locus were associated with circulating FGF21 protein concentrations (P < 0.05) but not mRNA concentrations in blood or brain. The body mass index (BMI)-increasing allele of the FTO variant (rs1421085) was associated with higher protein intake (β {\textpm} SE: 0.10 {\textpm} 0.02\%; P = 9.96 {\texttimes} 10(-10)), independent of BMI (after adjustment for BMI, β {\textpm} SE: 0.08 {\textpm} 0.02\%; P = 3.15 {\texttimes} 10(-7)).

CONCLUSION: Our results indicate that variants in genes involved in nutrient metabolism and obesity are associated with macronutrient consumption in humans. Trials related to this study were registered at clinicaltrials.gov as NCT00005131 (Atherosclerosis Risk in Communities), NCT00005133 (Cardiovascular Health Study), NCT00005136 (Family Heart Study), NCT00005121 (Framingham Heart Study), NCT00083369 (Genetic and Environmental Determinants of Triglycerides), NCT01331512 (InCHIANTI Study), and NCT00005487 (Multi-Ethnic Study of Atherosclerosis).

}, keywords = {Alleles, Atherosclerosis, Body Mass Index, Dietary Carbohydrates, Dietary Fats, Dietary Proteins, Energy Intake, European Continental Ancestry Group, Fibroblast Growth Factors, Follow-Up Studies, Gene-Environment Interaction, Genetic Predisposition to Disease, Genome-Wide Association Study, Genotype, Humans, Life Style, Obesity, Polymorphism, Single Nucleotide, Prospective Studies, Quantitative Trait Loci, Surveys and Questionnaires}, issn = {1938-3207}, doi = {10.3945/ajcn.112.052183}, author = {Tanaka, Toshiko and Ngwa, Julius S and van Rooij, Frank J A and Zillikens, M Carola and Wojczynski, Mary K and Frazier-Wood, Alexis C and Houston, Denise K and Kanoni, Stavroula and Lemaitre, Rozenn N and Luan, Jian{\textquoteright}an and Mikkil{\"a}, Vera and Renstrom, Frida and Sonestedt, Emily and Zhao, Jing Hua and Chu, Audrey Y and Qi, Lu and Chasman, Daniel I and de Oliveira Otto, Marcia C and Dhurandhar, Emily J and Feitosa, Mary F and Johansson, Ingegerd and Khaw, Kay-Tee and Lohman, Kurt K and Manichaikul, Ani and McKeown, Nicola M and Mozaffarian, Dariush and Singleton, Andrew and Stirrups, Kathleen and Viikari, Jorma and Ye, Zheng and Bandinelli, Stefania and Barroso, In{\^e}s and Deloukas, Panos and Forouhi, Nita G and Hofman, Albert and Liu, Yongmei and Lyytik{\"a}inen, Leo-Pekka and North, Kari E and Dimitriou, Maria and Hallmans, G{\"o}ran and K{\"a}h{\"o}nen, Mika and Langenberg, Claudia and Ordovas, Jose M and Uitterlinden, Andr{\'e} G and Hu, Frank B and Kalafati, Ioanna-Panagiota and Raitakari, Olli and Franco, Oscar H and Johnson, Andrew and Emilsson, Valur and Schrack, Jennifer A and Semba, Richard D and Siscovick, David S and Arnett, Donna K and Borecki, Ingrid B and Franks, Paul W and Kritchevsky, Stephen B and Lehtim{\"a}ki, Terho and Loos, Ruth J F and Orho-Melander, Marju and Rotter, Jerome I and Wareham, Nicholas J and Witteman, Jacqueline C M and Ferrucci, Luigi and Dedoussis, George and Cupples, L Adrienne and Nettleton, Jennifer A} } @article {5879, title = {Higher magnesium intake is associated with lower fasting glucose and insulin, with no evidence of interaction with select genetic loci, in a meta-analysis of 15 CHARGE Consortium Studies.}, journal = {J Nutr}, volume = {143}, year = {2013}, month = {2013 Mar}, pages = {345-53}, abstract = {

Favorable associations between magnesium intake and glycemic traits, such as fasting glucose and insulin, are observed in observational and clinical studies, but whether genetic variation affects these associations is largely unknown. We hypothesized that single nucleotide polymorphisms (SNPs) associated with either glycemic traits or magnesium metabolism affect the association between magnesium intake and fasting glucose and insulin. Fifteen studies from the CHARGE (Cohorts for Heart and Aging Research in Genomic Epidemiology) Consortium provided data from up to 52,684 participants of European descent without known diabetes. In fixed-effects meta-analyses, we quantified 1) cross-sectional associations of dietary magnesium intake with fasting glucose (mmol/L) and insulin (ln-pmol/L) and 2) interactions between magnesium intake and SNPs related to fasting glucose (16 SNPs), insulin (2 SNPs), or magnesium (8 SNPs) on fasting glucose and insulin. After adjustment for age, sex, energy intake, BMI, and behavioral risk factors, magnesium (per 50-mg/d increment) was inversely associated with fasting glucose [β = -0.009 mmol/L (95\% CI: -0.013, -0.005), P < 0.0001] and insulin [-0.020 ln-pmol/L (95\% CI: -0.024, -0.017), P < 0.0001]. No magnesium-related SNP or interaction between any SNP and magnesium reached significance after correction for multiple testing. However, rs2274924 in magnesium transporter-encoding TRPM6 showed a nominal association (uncorrected P = 0.03) with glucose, and rs11558471 in SLC30A8 and rs3740393 near CNNM2 showed a nominal interaction (uncorrected, both P = 0.02) with magnesium on glucose. Consistent with other studies, a higher magnesium intake was associated with lower fasting glucose and insulin. Nominal evidence of TRPM6 influence and magnesium interaction with select loci suggests that further investigation is warranted.

}, keywords = {Blood Glucose, Female, Genetic Loci, Humans, Insulin, Magnesium, Male, Polymorphism, Single Nucleotide, Trace Elements, TRPM Cation Channels}, issn = {1541-6100}, doi = {10.3945/jn.112.172049}, author = {Hruby, Adela and Ngwa, Julius S and Renstrom, Frida and Wojczynski, Mary K and Ganna, Andrea and Hallmans, G{\"o}ran and Houston, Denise K and Jacques, Paul F and Kanoni, Stavroula and Lehtim{\"a}ki, Terho and Lemaitre, Rozenn N and Manichaikul, Ani and North, Kari E and Ntalla, Ioanna and Sonestedt, Emily and Tanaka, Toshiko and van Rooij, Frank J A and Bandinelli, Stefania and Djouss{\'e}, Luc and Grigoriou, Efi and Johansson, Ingegerd and Lohman, Kurt K and Pankow, James S and Raitakari, Olli T and Riserus, Ulf and Yannakoulia, Mary and Zillikens, M Carola and Hassanali, Neelam and Liu, Yongmei and Mozaffarian, Dariush and Papoutsakis, Constantina and Syv{\"a}nen, Ann-Christine and Uitterlinden, Andr{\'e} G and Viikari, Jorma and Groves, Christopher J and Hofman, Albert and Lind, Lars and McCarthy, Mark I and Mikkil{\"a}, Vera and Mukamal, Kenneth and Franco, Oscar H and Borecki, Ingrid B and Cupples, L Adrienne and Dedoussis, George V and Ferrucci, Luigi and Hu, Frank B and Ingelsson, Erik and K{\"a}h{\"o}nen, Mika and Kao, W H Linda and Kritchevsky, Stephen B and Orho-Melander, Marju and Prokopenko, Inga and Rotter, Jerome I and Siscovick, David S and Witteman, Jacqueline C M and Franks, Paul W and Meigs, James B and McKeown, Nicola M and Nettleton, Jennifer A} } @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 {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 {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} } @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 {7667, title = {Genome-wide association study in 79,366 European-ancestry individuals informs the genetic architecture of 25-hydroxyvitamin D levels.}, journal = {Nat Commun}, volume = {9}, year = {2018}, month = {2018 Jan 17}, pages = {260}, abstract = {

Vitamin D is a steroid hormone precursor that is associated with a range of human traits and diseases. Previous GWAS of serum 25-hydroxyvitamin D concentrations have identified four genome-wide significant loci (GC, NADSYN1/DHCR7, CYP2R1, CYP24A1). In this study, we expand the previous SUNLIGHT Consortium GWAS discovery sample size from 16,125 to 79,366 (all European descent). This larger GWAS yields two additional loci harboring genome-wide significant variants (P = 4.7{\texttimes}10 at rs8018720 in SEC23A, and P = 1.9{\texttimes}10 at rs10745742 in AMDHD1). The overall estimate of heritability of 25-hydroxyvitamin D serum concentrations attributable to GWAS common SNPs is 7.5\%, with statistically significant loci explaining 38\% of this total. Further investigation identifies signal enrichment in immune and hematopoietic tissues, and clustering with autoimmune diseases in cell-type-specific analysis. Larger studies are required to identify additional common SNPs, and to explore the role of rare or structural variants and gene-gene interactions in the heritability of circulating 25-hydroxyvitamin D levels.

}, issn = {2041-1723}, doi = {10.1038/s41467-017-02662-2}, author = {Jiang, Xia and O{\textquoteright}Reilly, Paul F and Aschard, Hugues and Hsu, Yi-Hsiang and Richards, J Brent and Dupuis, Jos{\'e}e and Ingelsson, Erik and Karasik, David and Pilz, Stefan and Berry, Diane and Kestenbaum, Bryan and Zheng, Jusheng and Luan, Jianan and Sofianopoulou, Eleni and Streeten, Elizabeth A and Albanes, Demetrius and Lutsey, Pamela L and Yao, Lu and Tang, Weihong and Econs, Michael J and Wallaschofski, Henri and V{\"o}lzke, Henry and Zhou, Ang and Power, Chris and McCarthy, Mark I and Michos, Erin D and Boerwinkle, Eric and Weinstein, Stephanie J and Freedman, Neal D and Huang, Wen-Yi and van Schoor, Natasja M and van der Velde, Nathalie and Groot, Lisette C P G M de and Enneman, Anke and Cupples, L Adrienne and Booth, Sarah L and Vasan, Ramachandran S and Liu, Ching-Ti and Zhou, Yanhua and Ripatti, Samuli and Ohlsson, Claes and Vandenput, Liesbeth and Lorentzon, Mattias and Eriksson, Johan G and Shea, M Kyla and Houston, Denise K and Kritchevsky, Stephen B and Liu, Yongmei and Lohman, Kurt K and Ferrucci, Luigi and Peacock, Munro and Gieger, Christian and Beekman, Marian and Slagboom, Eline and Deelen, Joris and Heemst, Diana van and Kleber, Marcus E and M{\"a}rz, Winfried and de Boer, Ian H and Wood, Alexis C and Rotter, Jerome I and Rich, Stephen S and Robinson-Cohen, Cassianne and den Heijer, Martin and Jarvelin, Marjo-Riitta and Cavadino, Alana and Joshi, Peter K and Wilson, James F and Hayward, Caroline and Lind, Lars and Micha{\"e}lsson, Karl and Trompet, Stella and Zillikens, M Carola and Uitterlinden, Andr{\'e} G and Rivadeneira, Fernando and Broer, Linda and Zgaga, Lina and Campbell, Harry and Theodoratou, Evropi and Farrington, Susan M and Timofeeva, Maria and Dunlop, Malcolm G and Valdes, Ana M and Tikkanen, Emmi and Lehtim{\"a}ki, Terho and Lyytik{\"a}inen, Leo-Pekka and K{\"a}h{\"o}nen, Mika and Raitakari, Olli T and Mikkil{\"a}, Vera and Ikram, M Arfan and Sattar, Naveed and Jukema, J Wouter and Wareham, Nicholas J and Langenberg, Claudia and Forouhi, Nita G and Gundersen, Thomas E and Khaw, Kay-Tee and Butterworth, Adam S and Danesh, John and Spector, Timothy and Wang, Thomas J and Hypp{\"o}nen, Elina and Kraft, Peter and Kiel, Douglas P} } @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} }