@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 {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 {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 {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 {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} }