@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 {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 {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 {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} }