@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 {6281, title = {Lipoprotein receptor-related protein 1 variants and dietary fatty acids: meta-analysis of European origin and African American studies.}, journal = {Int J Obes (Lond)}, volume = {37}, year = {2013}, month = {2013 Sep}, pages = {1211-20}, abstract = {

OBJECTIVE: Low-density lipoprotein-related receptor protein 1 (LRP1) is a multi-functional endocytic receptor and signaling molecule that is expressed in adipose and the hypothalamus. Evidence for a role of LRP1 in adiposity is accumulating from animal and in vitro models, but data from human studies are limited. The study objectives were to evaluate (i) relationships between LRP1 genotype and anthropometric traits, and (ii) whether these relationships were modified by dietary fatty acids.

DESIGN AND METHODS: We conducted race/ethnic-specific meta-analyses using data from 14 studies of US and European whites and 4 of African Americans to evaluate associations of dietary fatty acids and LRP1 genotypes with body mass index (BMI), waist circumference and hip circumference, as well as interactions between dietary fatty acids and LRP1 genotypes. Seven single-nucleotide polymorphisms (SNPs) of LRP1 were evaluated in whites (N up to 42 000) and twelve SNPs in African Americans (N up to 5800).

RESULTS: After adjustment for age, sex and population substructure if relevant, for each one unit greater intake of percentage of energy from saturated fat (SFA), BMI was 0.104 kg m(-2) greater, waist was 0.305 cm larger and hip was 0.168 cm larger (all P<0.0001). Other fatty acids were not associated with outcomes. The association of SFA with outcomes varied by genotype at rs2306692 (genotyped in four studies of whites), where the magnitude of the association of SFA intake with each outcome was greater per additional copy of the T allele: 0.107 kg m(-2) greater for BMI (interaction P=0.0001), 0.267 cm for waist (interaction P=0.001) and 0.21 cm for hip (interaction P=0.001). No other significant interactions were observed.

CONCLUSION: Dietary SFA and LRP1 genotype may interactively influence anthropometric traits. Further exploration of this, and other diet x genotype interactions, may improve understanding of interindividual variability in the relationships of dietary factors with anthropometric traits.

}, keywords = {Adipose Tissue, Adult, African Continental Ancestry Group, Aged, Aged, 80 and over, Body Mass Index, Europe, European Continental Ancestry Group, Fatty Acids, Female, Gene Frequency, Gene-Environment Interaction, Genetic Predisposition to Disease, Genotype, Humans, Low Density Lipoprotein Receptor-Related Protein-1, Male, Middle Aged, Obesity, Phenotype, Polymorphism, Single Nucleotide, Prevalence, United States}, issn = {1476-5497}, doi = {10.1038/ijo.2012.215}, author = {Smith, C E and Ngwa, J and Tanaka, T and Qi, Q and Wojczynski, M K and Lemaitre, R N and Anderson, J S and Manichaikul, A and Mikkil{\"a}, V and van Rooij, F J A and Ye, Z and Bandinelli, S and Frazier-Wood, A C and Houston, D K and Hu, F and Langenberg, C and McKeown, N M and Mozaffarian, D and North, K E and Viikari, J and Zillikens, M C and Djouss{\'e}, L and Hofman, A and K{\"a}h{\"o}nen, M and Kabagambe, E K and Loos, R J F and Saylor, G B and Forouhi, N G and Liu, Y and Mukamal, K J and Chen, Y-D I and Tsai, M Y and Uitterlinden, A G and Raitakari, O and van Duijn, C M and Arnett, D K and Borecki, I B and Cupples, L A and Ferrucci, L and Kritchevsky, S B and Lehtim{\"a}ki, T and Qi, Lu and Rotter, J I and Siscovick, D S and Wareham, N J and Witteman, J C M and Ordov{\'a}s, J M and Nettleton, J A} } @article {6292, title = {No evidence of interaction between known lipid-associated genetic variants and smoking in the multi-ethnic PAGE population.}, journal = {Hum Genet}, volume = {132}, year = {2013}, month = {2013 Dec}, pages = {1427-31}, abstract = {

Genome-wide association studies (GWAS) have identified many variants that influence high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, and/or triglycerides. However, environmental modifiers, such as smoking, of these known genotype-phenotype associations are just recently emerging in the literature. We have tested for interactions between smoking and 49 GWAS-identified variants in over 41,000 racially/ethnically diverse samples with lipid levels from the Population Architecture Using Genomics and Epidemiology (PAGE) study. Despite their biological plausibility, we were unable to detect significant SNP {\texttimes} smoking interactions.

}, keywords = {Cholesterol, HDL, Cholesterol, LDL, Cohort Studies, Ethnic Groups, Female, Gene Frequency, Gene-Environment Interaction, Genetics, Population, Genome-Wide Association Study, Humans, Lipid Metabolism, Male, Polymorphism, Single Nucleotide, Prevalence, Smoking, Triglycerides, Young Adult}, issn = {1432-1203}, doi = {10.1007/s00439-013-1375-3}, author = {Dumitrescu, Logan and Carty, Cara L and Franceschini, Nora and Hindorff, Lucia A and Cole, Shelley A and B{\r u}zkov{\'a}, Petra and Schumacher, Fredrick R and Eaton, Charles B and Goodloe, Robert J and Duggan, David J and Haessler, Jeff and Cochran, Barbara and Henderson, Brian E and Cheng, Iona and Johnson, Karen C and Carlson, Chris S and Love, Shelly-Anne and Brown-Gentry, Kristin and Nato, Alejandro Q and Quibrera, Miguel and Shohet, Ralph V and Ambite, Jose Luis and Wilkens, Lynne R and Le Marchand, Lo{\"\i}c and Haiman, Christopher A and Buyske, Steven and Kooperberg, Charles and North, Kari E and Fornage, Myriam and Crawford, Dana C} } @article {5978, title = {Drug-gene interactions and the search for missing heritability: a cross-sectional pharmacogenomics study of the QT interval.}, journal = {Pharmacogenomics J}, volume = {14}, year = {2014}, month = {2014 Feb}, pages = {6-13}, abstract = {

Variability in response to drug use is common and heritable, suggesting that genome-wide pharmacogenomics studies may help explain the {\textquoteright}missing heritability{\textquoteright} of complex traits. Here, we describe four independent analyses in 33 781 participants of European ancestry from 10 cohorts that were designed to identify genetic variants modifying the effects of drugs on QT interval duration (QT). Each analysis cross-sectionally examined four therapeutic classes: thiazide diuretics (prevalence of use=13.0\%), tri/tetracyclic antidepressants (2.6\%), sulfonylurea hypoglycemic agents (2.9\%) and QT-prolonging drugs as classified by the University of Arizona Center for Education and Research on Therapeutics (4.4\%). Drug-gene interactions were estimated using covariable-adjusted linear regression and results were combined with fixed-effects meta-analysis. Although drug-single-nucleotide polymorphism (SNP) interactions were biologically plausible and variables were well-measured, findings from the four cross-sectional meta-analyses were null (Pinteraction>5.0 {\texttimes} 10(-8)). Simulations suggested that additional efforts, including longitudinal modeling to increase statistical power, are likely needed to identify potentially important pharmacogenomic effects.

}, keywords = {Computer Simulation, Cross-Sectional Studies, Drug-Related Side Effects and Adverse Reactions, Electrocardiography, European Continental Ancestry Group, Gene-Environment Interaction, Genome-Wide Association Study, Humans, Linear Models, Long QT Syndrome, Markov Chains, Pharmacogenetics, Polymorphism, Single Nucleotide, Quantitative Trait, Heritable}, issn = {1473-1150}, doi = {10.1038/tpj.2013.4}, author = {Avery, C L and Sitlani, C M and Arking, D E and Arnett, D K and Bis, J C and Boerwinkle, E and Buckley, B M and Ida Chen, Y-D and de Craen, A J M and Eijgelsheim, M and Enquobahrie, D and Evans, D S and Ford, I and Garcia, M E and Gudnason, V and Harris, T B and Heckbert, S R and Hochner, H and Hofman, A and Hsueh, W-C and Isaacs, A and Jukema, J W and Knekt, P and Kors, J A and Krijthe, B P and Kristiansson, K and Laaksonen, M and Liu, Y and Li, X and Macfarlane, P W and Newton-Cheh, C and Nieminen, M S and Oostra, B A and Peloso, G M and Porthan, K and Rice, K and Rivadeneira, F F and Rotter, J I and Salomaa, V and Sattar, N and Siscovick, D S and Slagboom, P E and Smith, A V and Sotoodehnia, N and Stott, D J and Stricker, B H and St{\"u}rmer, T and Trompet, S and Uitterlinden, A G and van Duijn, C and Westendorp, R G J and Witteman, J C and Whitsel, E A and Psaty, B M} } @article {6667, title = {No evidence for genome-wide interactions on plasma fibrinogen by smoking, alcohol consumption and body mass index: results from meta-analyses of 80,607 subjects.}, journal = {PLoS One}, volume = {9}, year = {2014}, month = {2014}, pages = {e111156}, abstract = {

Plasma fibrinogen is an acute phase protein playing an important role in the blood coagulation cascade having strong associations with smoking, alcohol consumption and body mass index (BMI). Genome-wide association studies (GWAS) have identified a variety of gene regions associated with elevated plasma fibrinogen concentrations. However, little is yet known about how associations between environmental factors and fibrinogen might be modified by genetic variation. Therefore, we conducted large-scale meta-analyses of genome-wide interaction studies to identify possible interactions of genetic variants and smoking status, alcohol consumption or BMI on fibrinogen concentration. The present study included 80,607 subjects of European ancestry from 22 studies. Genome-wide interaction analyses were performed separately in each study for about 2.6 million single nucleotide polymorphisms (SNPs) across the 22 autosomal chromosomes. For each SNP and risk factor, we performed a linear regression under an additive genetic model including an interaction term between SNP and risk factor. Interaction estimates were meta-analysed using a fixed-effects model. No genome-wide significant interaction with smoking status, alcohol consumption or BMI was observed in the meta-analyses. The most suggestive interaction was found for smoking and rs10519203, located in the LOC123688 region on chromosome 15, with a p value of 6.2 {\texttimes} 10(-8). This large genome-wide interaction study including 80,607 participants found no strong evidence of interaction between genetic variants and smoking status, alcohol consumption or BMI on fibrinogen concentrations. Further studies are needed to yield deeper insight in the interplay between environmental factors and gene variants on the regulation of fibrinogen concentrations.

}, keywords = {Alcohol Drinking, Body Mass Index, Fibrinogen, Gene-Environment Interaction, Genomics, Humans, Smoking}, issn = {1932-6203}, doi = {10.1371/journal.pone.0111156}, author = {Baumert, Jens and Huang, Jie and McKnight, Barbara and Sabater-Lleal, Maria and Steri, Maristella and Chu, Audrey Y and Trompet, Stella and Lopez, Lorna M and Fornage, Myriam and Teumer, Alexander and Tang, Weihong and Rudnicka, Alicja R and M{\"a}larstig, Anders and Hottenga, Jouke-Jan and Kavousi, Maryam and Lahti, Jari and Tanaka, Toshiko and Hayward, Caroline and Huffman, Jennifer E and Morange, Pierre-Emmanuel and Rose, Lynda M and Basu, Saonli and Rumley, Ann and Stott, David J and Buckley, Brendan M and de Craen, Anton J M and Sanna, Serena and Masala, Marco and Biffar, Reiner and Homuth, Georg and Silveira, Angela and Sennblad, Bengt and Goel, Anuj and Watkins, Hugh and M{\"u}ller-Nurasyid, Martina and R{\"u}ckerl, Regina and Taylor, Kent and Chen, Ming-Huei and de Geus, Eco J C and Hofman, Albert and Witteman, Jacqueline C M and de Maat, Moniek P M and Palotie, Aarno and Davies, Gail and Siscovick, David S and Kolcic, Ivana and Wild, Sarah H and Song, Jaejoon and McArdle, Wendy L and Ford, Ian and Sattar, Naveed and Schlessinger, David and Grotevendt, Anne and Franzosi, Maria Grazia and Illig, Thomas and Waldenberger, Melanie and Lumley, Thomas and Tofler, Geoffrey H and Willemsen, Gonneke and Uitterlinden, Andr{\'e} G and Rivadeneira, Fernando and R{\"a}ikk{\"o}nen, Katri and Chasman, Daniel I and Folsom, Aaron R and Lowe, Gordon D and Westendorp, Rudi G J and Slagboom, P Eline and Cucca, Francesco and Wallaschofski, Henri and Strawbridge, Rona J and Seedorf, Udo and Koenig, Wolfgang and Bis, Joshua C and Mukamal, Kenneth J and van Dongen, Jenny and Widen, Elisabeth and Franco, Oscar H and Starr, John M and Liu, Kiang and Ferrucci, Luigi and Polasek, Ozren and Wilson, James F and Oudot-Mellakh, Tiphaine and Campbell, Harry and Navarro, Pau and Bandinelli, Stefania and Eriksson, Johan and Boomsma, Dorret I and Dehghan, Abbas and Clarke, Robert and Hamsten, Anders and Boerwinkle, Eric and Jukema, J Wouter and Naitza, Silvia and Ridker, Paul M and V{\"o}lzke, Henry and Deary, Ian J and Reiner, Alexander P and Tr{\'e}gou{\"e}t, David-Alexandre and O{\textquoteright}Donnell, Christopher J and Strachan, David P and Peters, Annette and Smith, Nicholas L} } @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 {6602, title = {Generalized estimating equations for genome-wide association studies using longitudinal phenotype data.}, journal = {Stat Med}, volume = {34}, year = {2015}, month = {2015 Jan 15}, pages = {118-30}, abstract = {

Many longitudinal cohort studies have both genome-wide measures of genetic variation and repeated measures of phenotypes and environmental exposures. Genome-wide association study analyses have typically used only cross-sectional data to evaluate quantitative phenotypes and binary traits. Incorporation of repeated measures may increase power to detect associations, but also requires specialized analysis methods. Here, we discuss one such method-generalized estimating equations (GEE)-in the contexts of analysis of main effects of rare genetic variants and analysis of gene-environment interactions. We illustrate the potential for increased power using GEE analyses instead of cross-sectional analyses. We also address challenges that arise, such as the need for small-sample corrections when the minor allele frequency of a genetic variant and/or the prevalence of an environmental exposure is low. To illustrate methods for detection of gene-drug interactions on a genome-wide scale, using repeated measures data, we conduct single-study analyses and meta-analyses across studies in three large cohort studies participating in the Cohorts for Heart and Aging Research in Genomic Epidemiology consortium-the Atherosclerosis Risk in Communities study, the Cardiovascular Health Study, and the Rotterdam Study.

}, keywords = {Aged, Aging, Cardiovascular Diseases, Cohort Studies, Computer Simulation, Cross-Sectional Studies, Epidemiologic Research Design, Gene-Environment Interaction, Genetic Variation, Genome, Human, Genome-Wide Association Study, Humans, Longitudinal Studies, Meta-Analysis as Topic, Models, Genetic, Pharmacogenetics, Risk Assessment, United States}, issn = {1097-0258}, doi = {10.1002/sim.6323}, author = {Sitlani, Colleen M and Rice, Kenneth M and Lumley, Thomas and McKnight, Barbara and Cupples, L Adrienne and Avery, Christy L and Noordam, Raymond and Stricker, Bruno H C and Whitsel, Eric A and Psaty, Bruce 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} }