05848nas a2201069 4500008004100000022001400041245018600055210006900241260001300310300001200323490000800335520268400343653001803027653001903045653003203064653003803096653003403134653001103168653001803179653002003197653001203217653002303229653002803252653000903280653001803289653001603307653003603323653001703359100002203376700002003398700002703418700002403445700002003469700002403489700003203513700002003545700002803565700002303593700001603616700002003632700002903652700001903681700003203700700002503732700001803757700002003775700001903795700002303814700002103837700002203858700002003880700002303900700002403923700002103947700002103968700002603989700002104015700002004036700002104056700001804077700002504095700001704120700002004137700002304157700002404180700002204204700001904226700001604245700002004261700002104281700002404302700002104326700001704347700002104364700002804385700002004413700002104433700002004454700002504474700002304499700002004522700002104542700001904563700002304582700002504605700002104630700002204651700002504673700002004698700002404718856003604742 2015 eng d a1938-320700aConsumption 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.0 aConsumption of meat is associated with higher fasting glucose an c2015 Nov a1266-780 v1023 a
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).
10aBlood Glucose10aCohort Studies10aGenetic Association Studies10aGenetic Predisposition to Disease10aGenome-Wide Association Study10aHumans10aHyperglycemia10aHyperinsulinism10aInsulin10aInsulin Resistance10aInsulin-Secreting Cells10aMeat10aMeat Products10aMiddle Aged10aPolymorphism, Single Nucleotide10aRisk Factors1 aFretts, Amanda, M1 aFollis, Jack, L1 aNettleton, Jennifer, A1 aLemaitre, Rozenn, N1 aNgwa, Julius, S1 aWojczynski, Mary, K1 aKalafati, Ioanna, Panagiota1 aVarga, Tibor, V1 aFrazier-Wood, Alexis, C1 aHouston, Denise, K1 aLahti, Jari1 aEricson, Ulrika1 avan den Hooven, Edith, H1 aMikkilä, Vera1 ade Jong, Jessica, C Kiefte-1 aMozaffarian, Dariush1 aRice, Kenneth1 aRenstrom, Frida1 aNorth, Kari, E1 aMcKeown, Nicola, M1 aFeitosa, Mary, F1 aKanoni, Stavroula1 aSmith, Caren, E1 aGarcia, Melissa, E1 aTiainen, Anna-Maija1 aSonestedt, Emily1 aManichaikul, Ani1 avan Rooij, Frank, J A1 aDimitriou, Maria1 aRaitakari, Olli1 aPankow, James, S1 aDjoussé, Luc1 aProvince, Michael, A1 aHu, Frank, B1 aLai, Chao-Qiang1 aKeller, Margaux, F1 aPerälä, Mia-Maria1 aRotter, Jerome, I1 aHofman, Albert1 aGraff, Misa1 aKähönen, Mika1 aMukamal, Kenneth1 aJohansson, Ingegerd1 aOrdovas, Jose, M1 aLiu, Yongmei1 aMännistö, Satu1 aUitterlinden, André, G1 aDeloukas, Panos1 aSeppälä, Ilkka1 aPsaty, Bruce, M1 aCupples, Adrienne, L1 aBorecki, Ingrid, B1 aFranks, Paul, W1 aArnett, Donna, K1 aNalls, Mike, A1 aEriksson, Johan, G1 aOrho-Melander, Marju1 aFranco, Oscar, H1 aLehtimäki, Terho1 aDedoussis, George, V1 aMeigs, James, B1 aSiscovick, David, S uhttps://chs-nhlbi.org/node/684404354nas a2200661 4500008004100000022001400041245008900055210006900144260001300213300001200226490000700238520241500245653001002660653001202670653001802682653005302700653001902753653003002772653002502802653004002827653001202867653001102879653003302890653001102923653002302934653000902957653001602966653003302982653003503015653001403050653003603064653001003100653002403110100002203134700002003156700002003176700002003196700002003216700002403236700001703260700002103277700003203298700002503330700002503355700002003380700001903400700002403419700002803443700002803471700002403499700001803523700001803541700002403559700001903583700002103602710003303623856003603656 2015 eng d a1935-554800aGene-Environment Interactions of Circadian-Related Genes for Cardiometabolic Traits.0 aGeneEnvironment Interactions of CircadianRelated Genes for Cardi c2015 Aug a1456-660 v383 aOBJECTIVE: 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—specifically higher carbohydrate and lower fat composition—and normal sleep duration should continue to be emphasized among individuals with the investigated circadian-related gene variants.
10aAdult10aAlleles10aBlood Glucose10aCircadian Rhythm Signaling Peptides and Proteins10aCohort Studies10aDiabetes Mellitus, Type 210aDiet, Fat-Restricted10aEuropean Continental Ancestry Group10aFasting10aFemale10aGene-Environment Interaction10aHumans10aInsulin Resistance10aMale10aMiddle Aged10aMulticenter Studies as Topic10aObservational Studies as Topic10aPhenotype10aPolymorphism, Single Nucleotide10aSleep10aWaist Circumference1 aDashti, Hassan, S1 aFollis, Jack, L1 aSmith, Caren, E1 aTanaka, Toshiko1 aGaraulet, Marta1 aGottlieb, Daniel, J1 aHruby, Adela1 aJacques, Paul, F1 ade Jong, Jessica, C Kiefte-1 aLamon-Fava, Stefania1 aScheer, Frank, A J L1 aBartz, Traci, M1 aKovanen, Leena1 aWojczynski, Mary, K1 aFrazier-Wood, Alexis, C1 aAhluwalia, Tarunveer, S1 aPerälä, Mia-Maria1 aJonsson, Anna1 aMuka, Taulant1 aKalafati, Ioanna, P1 aMikkilä, Vera1 aOrdovas, Jose, M1 aCHARGE Nutrition Study Group uhttps://chs-nhlbi.org/node/692705076nas a2201081 4500008004100000022001400041245010500055210006900160260000900229300001300238490000700251520207300258653001002331653000902341653001902350653002602369653002602395653001102421653004002432653001102472653003402483653001102517653000902528653001602537653001202553653001802565100002502583700002202608700002402630700001902654700002702673700002102700700002302721700001802744700001602762700002302778700002502801700002602826700002102852700002202873700001902895700002002914700002902934700001102963700001702974700001802991700002203009700001903031700001903050700002003069700001603089700002003105700002103125700002103146700002003167700001603187700002103203700002403224700001903248700001703267700001903284700002003303700002003323700002103343700002103364700003203385700001903417700002803436700002403464700002003488700001903508700002103527700002203548700002003570700001903590700002103609700002003630700001603650700002103666700002303687700002503710700002303735700002803758700002503786700002103811700002103832700002003853700002203873700002303895700002003918700002003938856003603958 2017 eng d a1932-620300aGenome-wide association meta-analysis of fish and EPA+DHA consumption in 17 US and European cohorts.0 aGenomewide association metaanalysis of fish and EPADHA consumpti c2017 ae01864560 v123 aBACKGROUND: 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.
10aAdult10aAged10aCohort Studies10aDocosahexaenoic Acids10aEicosapentaenoic Acid10aEurope10aEuropean Continental Ancestry Group10aFemale10aGenome-Wide Association Study10aHumans10aMale10aMiddle Aged10aSeafood10aUnited States1 aMozaffarian, Dariush1 aDashti, Hassan, S1 aWojczynski, Mary, K1 aChu, Audrey, Y1 aNettleton, Jennifer, A1 aMännistö, Satu1 aKristiansson, Kati1 aReedik, Mägi1 aLahti, Jari1 aHouston, Denise, K1 aCornelis, Marilyn, C1 avan Rooij, Frank, J A1 aDimitriou, Maria1 aKanoni, Stavroula1 aMikkilä, Vera1 aSteffen, Lyn, M1 aOtto, Marcia, C de Olive1 aQi, Lu1 aPsaty, Bruce1 aDjoussé, Luc1 aRotter, Jerome, I1 aHarald, Kennet1 aPerola, Markus1 aRissanen, Harri1 aJula, Antti1 aKrista, Fischer1 aMihailov, Evelin1 aFeitosa, Mary, F1 aNgwa, Julius, S1 aXue, Luting1 aJacques, Paul, F1 aPerälä, Mia-Maria1 aPalotie, Aarno1 aLiu, Yongmei1 aNalls, Nike, A1 aFerrucci, Luigi1 aHernandez, Dena1 aManichaikul, Ani1 aTsai, Michael, Y1 ade Jong, Jessica, C Kiefte-1 aHofman, Albert1 aUitterlinden, André, G1 aRallidis, Loukianos1 aRidker, Paul, M1 aRose, Lynda, M1 aBuring, Julie, E1 aLehtimäki, Terho1 aKähönen, Mika1 aViikari, Jorma1 aLemaitre, Rozenn1 aSalomaa, Veikko1 aKnekt, Paul1 aMetspalu, Andres1 aBorecki, Ingrid, B1 aCupples, Adrienne, L1 aEriksson, Johan, G1 aKritchevsky, Stephen, B1 aBandinelli, Stefania1 aSiscovick, David1 aFranco, Oscar, H1 aDeloukas, Panos1 aDedoussis, George1 aChasman, Daniel, I1 aRaitakari, Olli1 aTanaka, Toshiko uhttps://chs-nhlbi.org/node/757804016nas a2200865 4500008004100000022001400041245009800055210006900153260001600222520147500238100002001713700002001733700002201753700002001775700002201795700002201817700002801839700002401867700002101891700001901912700003201931700001701963700002801980700002402008700001602032700002202048700001902070700001902089700002002108700001902128700002102147700002002168700002302188700001902211700002002230700001402250700002302264700001902287700002502306700002102331700002102352700002402373700002902397700002502426700002502451700002302476700002502499700002502524700002102549700002402570700002202594700003202616700002002648700002202668700001902690700002302709700002202732700002902754700002502783700002302808700001902831700001702850700002102867700001902888700002202907700002202929700002802951700002602979700002103005700001803026700002403044700002503068700002103093856003603114 2017 eng d a1613-413300aGenome-Wide Interactions with Dairy Intake for Body Mass Index in Adults of European Descent.0 aGenomeWide Interactions with Dairy Intake for Body Mass Index in c2017 Sep 213 aSCOPE: 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' of LINC00333) was replicated (p-interaction = 0.004). In the discovery cohorts, rs9635058 interacted with dairy (p-interaction = 7.36 × 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.
1 aSmith, Caren, E1 aFollis, Jack, L1 aDashti, Hassan, S1 aTanaka, Toshiko1 aGraff, Mariaelisa1 aFretts, Amanda, M1 aKilpeläinen, Tuomas, O1 aWojczynski, Mary, K1 aRichardson, Kris1 aNalls, Mike, A1 aSchulz, Christina-Alexandra1 aLiu, Yongmei1 aFrazier-Wood, Alexis, C1 avan Eekelen, Esther1 aWang, Carol1 ade Vries, Paul, S1 aMikkilä, Vera1 aRohde, Rebecca1 aPsaty, Bruce, M1 aHansen, Torben1 aFeitosa, Mary, F1 aLai, Chao-Qiang1 aHouston, Denise, K1 aFerruci, Luigi1 aEricson, Ulrika1 aWang, Zhe1 ade Mutsert, Renée1 aOddy, Wendy, H1 ade Jonge, Ester, A L1 aSeppälä, Ilkka1 aJustice, Anne, E1 aLemaitre, Rozenn, N1 aSørensen, Thorkild, I A1 aProvince, Michael, A1 aParnell, Laurence, D1 aGarcia, Melissa, E1 aBandinelli, Stefania1 aOrho-Melander, Marju1 aRich, Stephen, S1 aRosendaal, Frits, R1 aPennell, Craig, E1 ade Jong, Jessica, C Kiefte-1 aKähönen, Mika1 aYoung, Kristin, L1 aPedersen, Oluf1 aAslibekyan, Stella1 aRotter, Jerome, I1 aMook-Kanamori, Dennis, O1 aZillikens, Carola, M1 aRaitakari, Olli, T1 aNorth, Kari, E1 aOvervad, Kim1 aArnett, Donna, K1 aHofman, Albert1 aLehtimäki, Terho1 aTjønneland, Anne1 aUitterlinden, André, G1 aRivadeneira, Fernando1 aFranco, Oscar, H1 aGerman, Bruce1 aSiscovick, David, S1 aCupples, Adrienne, L1 aOrdovas, Jose, M uhttps://chs-nhlbi.org/node/758804864nas a2200733 4500008004100000022001400041245018700055210006900242260001300311300001200324490000700336520267100343100002303014700002203037700001603059700002403075700003203099700002003131700002003151700002203171700002403193700001703217700002103234700002803255700002903283700001603312700001903328700003103347700001903378700002203397700002403419700002503443700003203468700001703500700002003517700001503537700001903552700002003571700001803591700002003609700002003629700002003649700002403669700001903693700002003712700002003732700002203752700002803774700001903802700002403821700002103845700001903866700002503885700001903910700002503929700002103954700002303975700001103998700002204009700002104031700002204052700002004074856003604094 2018 eng d a1432-042800aSugar-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.0 aSugarsweetened beverage intake associations with fasting glucose c2018 Feb a317-3300 v613 aAIMS/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 ± SE 0.014 ± 0.004 [mmol/l], p = 1.5 × 10-3) and higher fasting insulin (0.030 ± 0.005 [log e pmol/l], p = 2.0 × 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 ± 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' Health Study).
1 aMcKeown, Nicola, M1 aDashti, Hassan, S1 aMa, Jiantao1 aHaslam, Danielle, E1 ade Jong, Jessica, C Kiefte-1 aSmith, Caren, E1 aTanaka, Toshiko1 aGraff, Mariaelisa1 aLemaitre, Rozenn, N1 aRybin, Denis1 aSonestedt, Emily1 aFrazier-Wood, Alexis, C1 aMook-Kanamori, Dennis, O1 aLi, Yanping1 aWang, Carol, A1 aLeermakers, Elisabeth, T M1 aMikkilä, Vera1 aYoung, Kristin, L1 aMukamal, Kenneth, J1 aCupples, Adrienne, L1 aSchulz, Christina-Alexandra1 aChen, Tzu-An1 aLi-Gao, Ruifang1 aHuang, Tao1 aOddy, Wendy, H1 aRaitakari, Olli1 aRice, Kenneth1 aMeigs, James, B1 aEricson, Ulrika1 aSteffen, Lyn, M1 aRosendaal, Frits, R1 aHofman, Albert1 aKähönen, Mika1 aPsaty, Bruce, M1 aBrunkwall, Louise1 aUitterlinden, André, G1 aViikari, Jorma1 aSiscovick, David, S1 aSeppälä, Ilkka1 aNorth, Kari, E1 aMozaffarian, Dariush1 aDupuis, Josée1 aOrho-Melander, Marju1 aRich, Stephen, S1 ade Mutsert, Renée1 aQi, Lu1 aPennell, Craig, E1 aFranco, Oscar, H1 aLehtimäki, Terho1 aHerman, Mark, A uhttps://chs-nhlbi.org/node/757604415nas a2200805 4500008004100000022001400041245024200055210006900297260001300366300001200379490000700391520195900398100002402357700002002381700002202401700002102423700002002444700003002464700001902494700002002513700002102533700002102554700002202575700002202597700002002619700002002639700001802659700002002677700003202697700002102729700001902750700002402769700002902793700001602822700001902838700002902857700002202886700002002908700002202928700001902950700002202969700002402991700002003015700002803035700002003063700002503083700002203108700002003130700002203150700002303172700002403195700002003219700002103239700002103260700002303281700002003304700002103324700001603345700002003361700001803381700002003399700002203419700002303441700002703464700002003491700001903511700002003530700002303550856003603573 2021 eng d a2574-830000aSugar-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.0 aSugarSweetened Beverage Consumption May Modify Associations Betw c2021 Aug ae0032880 v143 aBACKGROUND: 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.
1 aHaslam, Danielle, E1 aPeloso, Gina, M1 aGuirette, Melanie1 aImamura, Fumiaki1 aBartz, Traci, M1 aPitsillides, Achilleas, N1 aWang, Carol, A1 aLi-Gao, Ruifang1 aWestra, Jason, M1 aPitkänen, Niina1 aYoung, Kristin, L1 aGraff, Mariaelisa1 aWood, Alexis, C1 aBraun, Kim, V E1 aLuan, Jian'an1 aKähönen, Mika1 ade Jong, Jessica, C Kiefte-1 aGhanbari, Mohsen1 aTintle, Nathan1 aLemaitre, Rozenn, N1 aMook-Kanamori, Dennis, O1 aNorth, Kari1 aHelminen, Mika1 aMossavar-Rahmani, Yasmin1 aSnetselaar, Linda1 aMartin, Lisa, W1 aViikari, Jorma, S1 aOddy, Wendy, H1 aPennell, Craig, E1 aRosendall, Frits, R1 aIkram, Arfan, M1 aUitterlinden, André, G1 aPsaty, Bruce, M1 aMozaffarian, Dariush1 aRotter, Jerome, I1 aTaylor, Kent, D1 aLehtimäki, Terho1 aRaitakari, Olli, T1 aLivingston, Kara, A1 aVoortman, Trudy1 aForouhi, Nita, G1 aWareham, Nick, J1 ade Mutsert, Renée1 aRich, Steven, S1 aManson, JoAnn, E1 aMora, Samia1 aRidker, Paul, M1 aMerino, Jordi1 aMeigs, James, B1 aDashti, Hassan, S1 aChasman, Daniel, I1 aLichtenstein, Alice, H1 aSmith, Caren, E1 aDupuis, Josée1 aHerman, Mark, A1 aMcKeown, Nicola, M uhttps://chs-nhlbi.org/node/8830