04864nas 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 a
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 ± 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/757604340nas a2200733 4500008004100000022001400041245013200055210006900187260001300256300001200269490000700281520227500288100001602563700002202579700002002601700002502621700002302646700001302669700002702682700001902709700001602728700002602744700001902770700001702789700002202806700002002828700001702848700002802865700002102893700002402914700001702938700002402955700001802979700002602997700002003023700002003043700002103063700002403084700002003108700002203128700002303150700002403173700002003197700001603217700001803233700001803251700001503269700002003284700002103304700002103325700001503346700001803361700001603379700002303395700001703418700001603435700001803451700002703469700001703496700002003513700002003533700001703553856003603570 2020 eng d a2574-830000aWhole Blood DNA Methylation Signatures of Diet Are Associated With Cardiovascular Disease Risk Factors and All-Cause Mortality.0 aWhole Blood DNA Methylation Signatures of Diet Are Associated Wi c2020 Aug ae0027660 v133 aBACKGROUND: DNA methylation patterns associated with habitual diet have not been well studied.
METHODS: Diet quality was characterized using a Mediterranean-style diet score and the Alternative Healthy Eating Index score. We conducted ethnicity-specific and trans-ethnic epigenome-wide association analyses for diet quality and leukocyte-derived DNA methylation at over 400 000 CpGs (cytosine-guanine dinucleotides) in 5 population-based cohorts including 6662 European ancestry, 2702 African ancestry, and 360 Hispanic ancestry participants. For diet-associated CpGs identified in epigenome-wide analyses, we conducted Mendelian randomization (MR) analysis to examine their relations to cardiovascular disease risk factors and examined their longitudinal associations with all-cause mortality.
RESULTS: We identified 30 CpGs associated with either Mediterranean-style diet score or Alternative Healthy Eating Index, or both, in European ancestry participants. Among these CpGs, 12 CpGs were significantly associated with all-cause mortality (Bonferroni corrected <1.6×10). Hypermethylation of cg18181703 () was associated with higher scores of both Mediterranean-style diet score and Alternative Healthy Eating Index and lower risk for all-cause mortality (=5.7×10). Ten additional diet-associated CpGs were nominally associated with all-cause mortality (<0.05). MR analysis revealed 8 putatively causal associations for 6 CpGs with 4 cardiovascular disease risk factors (body mass index, triglycerides, high-density lipoprotein cholesterol concentrations, and type 2 diabetes mellitus; Bonferroni corrected MR <4.5×10). For example, hypermethylation of cg11250194 () was associated with lower triglyceride concentrations (MR, =1.5×10).and hypermethylation of cg02079413 (; ) was associated with body mass index (corrected MR, =1×10).
CONCLUSIONS: Habitual diet quality was associated with differential peripheral leukocyte DNA methylation levels of 30 CpGs, most of which were also associated with multiple health outcomes, in European ancestry individuals. These findings demonstrate that integrative genomic analysis of dietary information may reveal molecular targets for disease prevention and treatment.
1 aMa, Jiantao1 aRebholz, Casey, M1 aBraun, Kim, V E1 aReynolds, Lindsay, M1 aAslibekyan, Stella1 aXia, Rui1 aBiligowda, Niranjan, G1 aHuan, Tianxiao1 aLiu, Chunyu1 aMendelson, Michael, M1 aJoehanes, Roby1 aHu, Emily, A1 aVitolins, Mara, Z1 aWood, Alexis, C1 aLohman, Kurt1 aOchoa-Rosales, Carolina1 avan Meurs, Joyce1 aUitterlinden, Andre1 aLiu, Yongmei1 aElhadad, Mohamed, A1 aHeier, Margit1 aWaldenberger, Melanie1 aPeters, Annette1 aColicino, Elena1 aWhitsel, Eric, A1 aBaldassari, Antoine1 aGharib, Sina, A1 aSotoodehnia, Nona1 aBrody, Jennifer, A1 aSitlani, Colleen, M1 aTanaka, Toshiko1 aHill, David1 aCorley, Janie1 aDeary, Ian, J1 aZhang, Yan1 aSchöttker, Ben1 aBrenner, Hermann1 aWalker, Maura, E1 aYe, Shumao1 aNguyen, Steve1 aPankow, Jim1 aDemerath, Ellen, W1 aZheng, Yinan1 aHou, Lifang1 aLiang, Liming1 aLichtenstein, Alice, H1 aHu, Frank, B1 aFornage, Myriam1 aVoortman, Trudy1 aLevy, Daniel uhttps://chs-nhlbi.org/node/844603954nas a2200805 4500008004100000022001400041245007700055210006900132260001300201300001100214490000700225520175800232653001501990653002802005653002002033653002402053653001602077653001102093653000902104653001402113100001902127700001802146700002002164700002802184700001602212700002302228700002102251700001502272700002402287700002802311700002002339700001702359700002602376700002002402700001602422700001402438700001602452700002002468700001902488700002002507700001802527700002602545700002202571700002302593700002102616700002302637700002002660700001902680700002002699700002202719700002402741700001702765700002202782700002002804700002602824700001802850700002002868700001402888700002202902700002702924700001602951700002502967700002002992700002103012700002303033700001803056700002103074700001703095856003603112 2022 eng d a1474-972600aIntegrative analysis of clinical and epigenetic biomarkers of mortality.0 aIntegrative analysis of clinical and epigenetic biomarkers of mo c2022 Jun ae136080 v213 aDNA methylation (DNAm) has been reported to be associated with many diseases and with mortality. We hypothesized that the integration of DNAm with clinical risk factors would improve mortality prediction. We performed an epigenome-wide association study of whole blood DNAm in relation to mortality in 15 cohorts (n = 15,013). During a mean follow-up of 10 years, there were 4314 deaths from all causes including 1235 cardiovascular disease (CVD) deaths and 868 cancer deaths. Ancestry-stratified meta-analysis of all-cause mortality identified 163 CpGs in European ancestry (EA) and 17 in African ancestry (AA) participants at p < 1 × 10 , of which 41 (EA) and 16 (AA) were also associated with CVD death, and 15 (EA) and 9 (AA) with cancer death. We built DNAm-based prediction models for all-cause mortality that predicted mortality risk after adjusting for clinical risk factors. The mortality prediction model trained by integrating DNAm with clinical risk factors showed an improvement in prediction of cancer death with 5% increase in the C-index in a replication cohort, compared with the model including clinical risk factors alone. Mendelian randomization identified 15 putatively causal CpGs in relation to longevity, CVD, or cancer risk. For example, cg06885782 (in KCNQ4) was positively associated with risk for prostate cancer (Beta = 1.2, P = 4.1 × 10 ) and negatively associated with longevity (Beta = -1.9, P = 0.02). Pathway analysis revealed that genes associated with mortality-related CpGs are enriched for immune- and cancer-related pathways. We identified replicable DNAm signatures of mortality and demonstrated the potential utility of CpGs as informative biomarkers for prediction of mortality risk.
10aBiomarkers10aCardiovascular Diseases10aDNA Methylation10aEpigenesis, Genetic10aEpigenomics10aHumans10aMale10aNeoplasms1 aHuan, Tianxiao1 aNguyen, Steve1 aColicino, Elena1 aOchoa-Rosales, Carolina1 aHill, David1 aBrody, Jennifer, A1 aSoerensen, Mette1 aZhang, Yan1 aBaldassari, Antoine1 aElhadad, Mohamed, Ahmed1 aToshiko, Tanaka1 aZheng, Yinan1 aDomingo-Relloso, Arce1 aLee, Dong, Heon1 aMa, Jiantao1 aYao, Chen1 aLiu, Chunyu1 aHwang, Shih-Jen1 aJoehanes, Roby1 aFornage, Myriam1 aBressler, Jan1 avan Meurs, Joyce, B J1 aDebrabant, Birgit1 aMengel-From, Jonas1 aHjelmborg, Jacob1 aChristensen, Kaare1 aVokonas, Pantel1 aSchwartz, Joel1 aGahrib, Sina, A1 aSotoodehnia, Nona1 aSitlani, Colleen, M1 aKunze, Sonja1 aGieger, Christian1 aPeters, Annette1 aWaldenberger, Melanie1 aDeary, Ian, J1 aFerrucci, Luigi1 aQu, Yishu1 aGreenland, Philip1 aLloyd-Jones, Donald, M1 aHou, Lifang1 aBandinelli, Stefania1 aVoortman, Trudy1 aHermann, Brenner1 aBaccarelli, Andrea1 aWhitsel, Eric1 aPankow, James, S1 aLevy, Daniel uhttps://chs-nhlbi.org/node/909403534nas a2200649 4500008004100000245010900041210006900150260001600219520168900235100002201924700001601946700002001962700002201982700001402004700002202018700001602040700002202056700002402078700002002102700002202122700001702144700002402161700001802185700002102203700002302224700002402247700001902271700002102290700002002311700001902331700002202350700002002372700001902392700002002411700001702431700001902448700001702467700002502484700002402509700001902533700002302552700001902575700002302594700002002617700002102637700001802658700002002676700002202696700002402718700002102742700002002763700001802783700001402801700001702815700001602832856003602848 2023 eng d00aGenome-Wide Interaction Analysis with DASH Diet Score Identified Novel Loci for Systolic Blood Pressure.0 aGenomeWide Interaction Analysis with DASH Diet Score Identified c2023 Nov 113 aOBJECTIVE: We examined interactions between genotype and a Dietary Approaches to Stop Hypertension (DASH) diet score in relation to systolic blood pressure (SBP).
METHODS: We analyzed up to 9,420,585 biallelic imputed single nucleotide polymorphisms (SNPs) in up to 127,282 individuals of six population groups (91% of European population) from the Cohorts for Heart and Aging Research in Genomic Epidemiology consortium (CHARGE; n=35,660) and UK Biobank (n=91,622) and performed European population-specific and cross-population meta-analyses.
RESULTS: We identified three loci in European-specific analyses and an additional four loci in cross-population analyses at P for interaction < 5e-8. We observed a consistent interaction between rs117878928 at 15q25.1 (minor allele frequency = 0.03) and the DASH diet score (P for interaction = 4e-8; P for heterogeneity = 0.35) in European population, where the interaction effect size was 0.42±0.09 mm Hg (P for interaction = 9.4e-7) and 0.20±0.06 mm Hg (P for interaction = 0.001) in CHARGE and the UK Biobank, respectively. The 1 Mb region surrounding rs117878928 was enriched with -expression quantitative trait loci (eQTL) variants (P = 4e-273) and -DNA methylation quantitative trait loci (mQTL) variants (P = 1e-300). While the closest gene for rs117878928 is , the highest narrow sense heritability accounted by SNPs potentially interacting with the DASH diet score in this locus was for gene at 15q25.1.
CONCLUSION: We demonstrated gene-DASH diet score interaction effects on SBP in several loci. Studies with larger diverse populations are needed to validate our findings.
1 aGuirette, Melanie1 aLan, Jessie1 aMcKeown, Nicola1 aBrown, Michael, R1 aChen, Han1 ade Vries, Paul, S1 aKim, Hyunju1 aRebholz, Casey, M1 aMorrison, Alanna, C1 aBartz, Traci, M1 aFretts, Amanda, M1 aGuo, Xiuqing1 aLemaitre, Rozenn, N1 aLiu, Ching-Ti1 aNoordam, Raymond1 ade Mutsert, Renée1 aRosendaal, Frits, R1 aWang, Carol, A1 aBeilin, Lawrence1 aMori, Trevor, A1 aOddy, Wendy, H1 aPennell, Craig, E1 aChai, Jin, Fang1 aWhitton, Clare1 avan Dam, Rob, M1 aLiu, Jianjun1 aTai, Shyong, E1 aSim, Xueling1 aNeuhouser, Marian, L1 aKooperberg, Charles1 aTinker, Lesley1 aFranceschini, Nora1 aHuan, Tianxiao1 aWinkler, Thomas, W1 aBentley, Amy, R1 aGauderman, James1 aHeerkens, Luc1 aTanaka, Toshiko1 avan Rooij, Jeroen1 aMunroe, Patricia, B1 aWarren, Helen, R1 aVoortman, Trudy1 aChen, Honglei1 aRao, D, C1 aLevy, Daniel1 aMa, Jiantao uhttps://chs-nhlbi.org/node/9583