Advertisement

Diabetologia

, Volume 61, Issue 2, pp 317–330 | Cite as

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

  • Nicola M. McKeown
  • Hassan S. Dashti
  • Jiantao Ma
  • Danielle E. Haslam
  • Jessica C. Kiefte-de Jong
  • Caren E. Smith
  • Toshiko Tanaka
  • Mariaelisa Graff
  • Rozenn N. Lemaitre
  • Denis Rybin
  • Emily Sonestedt
  • Alexis C. Frazier-Wood
  • Dennis O. Mook-Kanamori
  • Yanping Li
  • Carol A. Wang
  • Elisabeth T. M. Leermakers
  • Vera Mikkilä
  • Kristin L. Young
  • Kenneth J. Mukamal
  • L. Adrienne Cupples
  • Christina-Alexandra Schulz
  • Tzu-An Chen
  • Ruifang Li-Gao
  • Tao Huang
  • Wendy H. Oddy
  • Olli Raitakari
  • Kenneth Rice
  • James B. Meigs
  • Ulrika Ericson
  • Lyn M. Steffen
  • Frits R. Rosendaal
  • Albert Hofman
  • Mika Kähönen
  • Bruce M. Psaty
  • Louise Brunkwall
  • Andre G. Uitterlinden
  • Jorma Viikari
  • David S. Siscovick
  • Ilkka Seppälä
  • Kari E. North
  • Dariush Mozaffarian
  • Josée Dupuis
  • Marju Orho-Melander
  • Stephen S. Rich
  • Renée de Mutsert
  • Lu Qi
  • Craig E. Pennell
  • Oscar H. Franco
  • Terho Lehtimäki
  • Mark A. Herman
Article

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 ± 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).

Keywords

Carbohydrate metabolism Epidemiology Genetics Meta-analysis Nutrition Type 2 diabetes 

Abbreviations

ARIC

Atherosclerosis Risk In Communities

CHARGE

Cohorts for Heart and Aging Research in Genomic Epidemiology

ChREBP

Carbohydrate responsive element-binding protein

CHS

Cardiovascular Health Study

FFQ

Food-frequency questionnaire

FGF21

Fibroblast growth factor 21

FHS

Framingham Heart Study

MAF

Minor allele frequency

MDC

Malmö Diet and Cancer

MESA

Multi-Ethnic Study of Atherosclerosis

NEO

Netherlands Epidemiology in Obesity Study

NHS

Nurses’ Health Study

RS1

Rotterdam Study I

RS2

Rotterdam Study II

SSB

Sugar-sweetened beverage

YFS

Cardiovascular Risk in Young Finns Study

Notes

Acknowledgements

Infrastructure for the CHARGE Consortium is supported in part by the National Heart, Lung, and Blood Institute grant HL105756. Cohort-specific sources of support and acknowledgements are presented in ESM Table 1. We thank J. C. Florez (Diabetes Unit, Massachusetts General Hospital, USA) for his help in the genesis of this project. Preliminary results were presented as an abstract at the ADA 75th Scientific Sessions in 2015.

Data availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

Funding

NMM received funding from the Boston Area Diabetes, Endocrinology Research Center Feasibility Program (P30 DK057521) to support part of this research, and she was funded in part by the US Department of Agriculture, under agreement No. 58-1950-0-014. MAH is supported by R01 DK100425. CES is supported by K08 HL112845. JBM is supported by K24DK080140 and U01DK078616. KLY is supported by KL2TR001109.

Duality of interest

BP serves on the Data and Safety Monitoring Board of a clinical trial funded by the manufacturer (Zoll LifeCor) and on the Steering Committee of the Yale Open Data Access Project funded by Johnson & Johnson. All other authors declare no conflict of interest.

Contribution statement

The authors’ responsibilities were as follows: NMM, HSD, JM and MAH: designed the study; NMM, HSD, JM, DEH, JCK-dJ, CES, TT, MG, RNL, DR, ES, ACF-W, DOM-K, YL, CAW, ETML, VM, KLY, KJM, LAC, C-AS, T-AC, RL-G, TH, WHO, OR, KR, JBM, UE, LMS, FRR, AH, MK, BMP, LB, AGU, JV, DSS, IS, KEN, DM, JD, MO-M, SSR, RdM, LQ, CEP, OHF, TL and MAH: played a role in acquisition of the data and critical revision of the manuscript for important intellectual content; NMM, HSD, JM, DEH, JCK-dJ, CES, TT, MG, RNL, DR, ES, ACF-W, DOM-K, YL, CAW, ETML, VM and MAH: contributed to statistical analyses; NMM, HSD, JM, DEH, JCK-dJ, CES, TT and MAH: interpreted data; NMM, HSD, JM, DEH, JCK-dJ, CES, TT, MG, RNL, DR, ES, ACF-W, DOM-K, YL, CAW, ETML, VM, JBM and MAH: contributed to writing of the manuscript; all authors read and approved the final version of the manuscript. NMM and HSD (joint co-first authors) are the guarantors of this work and, as such, had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Supplementary material

125_2017_4475_MOESM1_ESM.pdf (551 kb)
ESM (PDF 550 kb)

References

  1. 1.
    Ma J, McKeown NM, Hwang S-J et al (2016) Sugar-sweetened beverage consumption is associated with change of visceral adipose tissue over 6 years of follow-up. Circulation 133:370–377CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    Malik VS, Popkin BM, Bray GA et al (2010) Sugar-sweetened beverages and risk of metabolic syndrome and type 2 diabetes: a meta-analysis. Diabetes Care 33:2477–2483CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Imamura F, O’Connor L, Ye Z et al (2015) Consumption of sugar sweetened beverages, artificially sweetened beverages, and fruit juice and incidence of type 2 diabetes: systematic review, meta-analysis, and estimation of population attributable fraction. BMJ.  https://doi.org/10.1136/bmj.h3576
  4. 4.
    Welsh JA, Sharma A, Cunningham SA, Vos MB (2011) Consumption of added sugars and indicators of cardiovascular disease risk among US adolescents. Circulation 123:249–257CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Stanhope KL, Schwarz JM, Keim NL et al (2009) Consuming fructose-sweetened, not glucose-sweetened, beverages increases visceral adiposity and lipids and decreases insulin sensitivity in overweight/obese humans. J Clin Invest 119:1322–1334CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Aeberli I, Hochuli M, Gerber PA et al (2013) Moderate amounts of fructose consumption impair insulin sensitivity in healthy young men. Diabetes Care 36:150–156CrossRefPubMedGoogle Scholar
  7. 7.
    Kuzma JN, Cromer G, Hagman DK et al (2016) No differential effect of beverages sweetened with fructose, high-fructose corn syrup, or glucose on systemic or adipose tissue inflammation in normal-weight to obese adults: a randomized controlled trial. Am J Clin Nutr 104:306–314CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Silbernagel G, Machann J, Häring H-U et al (2014) Plasminogen activator inhibitor-1, monocyte chemoattractant protein-1, e-selectin and C-reactive protein levels in response to 4-week very-high-fructose or -glucose diets. Eur J Clin Nutr 68:97–100CrossRefPubMedGoogle Scholar
  9. 9.
    Centers for Disease Control 2017. Centers for Disease Control National Diabetes Statistics Report. www.cdc.gov/diabetes/data/statistics/statistics-report.html. Accessed 17 October 2017
  10. 10.
    Lana A, Rodríguez-Artalejo F, Lopez-Garcia E (2014) Consumption of sugar-sweetened beverages is positively related to insulin resistance and higher plasma leptin concentrations in men and nonoverweight women. J Nutr 144:1099–1105CrossRefPubMedGoogle Scholar
  11. 11.
    Yoshida M, McKeown NM, Rogers G et al (2007) Surrogate markers of insulin resistance are associated with consumption of sugar-sweetened drinks and fruit juice in middle and older-aged adults. J Nutr 137:2121–2127PubMedGoogle Scholar
  12. 12.
    Ogawa Y, Kurosu H, Yamamoto M et al (2007) β-Klotho is required for metabolic activity of fibroblast growth factor 21. Proc Natl Acad Sci 104:7432–7437CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Uyeda K, Repa JJ (2006) Carbohydrate response element binding protein, ChREBP, a transcription factor coupling hepatic glucose utilization and lipid synthesis. Cell Metab 4:107–110CrossRefPubMedGoogle Scholar
  14. 14.
    Iizuka K, Takeda J, Horikawa Y (2009) Glucose induces FGF21 mRNA expression through ChREBP activation in rat hepatocytes. FEBS Lett 583:2882–2886CrossRefPubMedGoogle Scholar
  15. 15.
    Koo H-Y, Miyashita M, Simon Cho BH, Nakamura MT (2009) Replacing dietary glucose with fructose increases ChREBP activity and SREBP-1 protein in rat liver nucleus. Biochem Biophys Res Commun 390:285–289CrossRefPubMedGoogle Scholar
  16. 16.
    Erion DM, Popov V, Hsiao JJ et al (2013) The role of the carbohydrate response element-binding protein in male fructose-fed rats. Endocrinology 154:36–44CrossRefPubMedGoogle Scholar
  17. 17.
    Kim M-S, Krawczyk SA, Doridot L et al (2016) ChREBP regulates fructose-induced glucose production independently of insulin signaling. J Clin Invest 126:4372–4386CrossRefPubMedPubMedCentralGoogle Scholar
  18. 18.
    Kooner JS, Chambers JC, Aguilar-Salinas CA et al (2008) Genome-wide scan identifies variation in MLXIPL associated with plasma triglycerides. Nat Genet 40:149–151CrossRefPubMedGoogle Scholar
  19. 19.
    Kathiresan S, Melander O, Guiducci C et al (2008) Six new loci associated with blood low-density lipoprotein cholesterol, high-density lipoprotein cholesterol or triglycerides in humans. Nat Genet 40:189–197CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Dushay JR, Toschi E, Mitten EK et al (2015) Fructose ingestion acutely stimulates circulating FGF21 levels in humans. Mol Metab 4:51–57CrossRefPubMedGoogle Scholar
  21. 21.
    Fisher FM, Kim M, Doridot L et al (2017) A critical role for ChREBP-mediated FGF21 secretion in hepatic fructose metabolism. Mol Metab 6:14–21CrossRefPubMedGoogle Scholar
  22. 22.
    Emanuelli B, Vienberg SG, Smyth G et al (2014) Interplay between FGF21 and insulin action in the liver regulates metabolism. J Clin Invest 124:515–527CrossRefPubMedPubMedCentralGoogle Scholar
  23. 23.
    Gimeno RE, Moller DE (2014) FGF21-based pharmacotherapy—potential utility for metabolic disorders. Trends Endocrinol Metab 25:303–311CrossRefPubMedGoogle Scholar
  24. 24.
    Tanaka T, Ngwa JS, van Rooij FJ et al (2013) Genome-wide meta-analysis of observational studies shows common genetic variants associated with macronutrient intake. Am J Clin Nutr 97:1395–1402CrossRefPubMedPubMedCentralGoogle Scholar
  25. 25.
    Chu AY, Workalemahu T, Paynter NP et al (2013) Novel locus including FGF21 is associated with dietary macronutrient intake. Hum Mol Genet 22:1895–1902CrossRefPubMedPubMedCentralGoogle Scholar
  26. 26.
    Grundy SM (1999) Hypertriglyceridemia, insulin resistance, and the metabolic syndrome. Am J Cardiol 83:25–29CrossRefGoogle Scholar
  27. 27.
    Olefsky JM, Farquhar JW, Reaven GM (1974) Reappraisal of the role of insulin in hypertriglyceridemia. Am J Med 57:551–560CrossRefPubMedGoogle Scholar
  28. 28.
    Santer R, Rischewski J, von Weihe M et al (2005) The spectrum of aldolase B (ALDOB) mutations and the prevalence of hereditary fructose intolerance in Central Europe. Hum Mutat 25:594Google Scholar
  29. 29.
    van Schaftingen E (1989) A protein from rat liver confers to glucokinase the property of being antagonistically regulated by fructose 6-phosphate and fructose 1-phosphate. Eur J Biochem 179:179–184CrossRefPubMedGoogle Scholar
  30. 30.
    Agius L (2008) Glucokinase and molecular aspects of liver glycogen metabolism. Biochem J 414:1–18CrossRefPubMedGoogle Scholar
  31. 31.
    Helliwell PA, Richardson M, Affleck J, Kellett GL (2000) Stimulation of fructose transport across the intestinal brush-border membrane by PMA is mediated by GLUT2 and dynamically regulated by protein kinase C. Biochem J 350:149–154CrossRefPubMedPubMedCentralGoogle Scholar
  32. 32.
    Bonthron DT, Brady N, Donaldson IA, Steinmann B (1994) Molecular basis of essential fructosuria: molecular cloning and mutational analysis of human ketohexokinase (fructokinase). Hum Mol Genet 3:1627–1631CrossRefPubMedGoogle Scholar
  33. 33.
    Corpe CP, Basaleh MM, Affleck J et al (1996) The regulation of GLUT5 and GLUT2 activity in the adaptation of intestinal brush-border fructose transport in diabetes. Pflugers Arch 432:192–201CrossRefPubMedGoogle Scholar
  34. 34.
    Burant CF, Takeda J, Brot-Laroche E et al (1992) Fructose transporter in human spermatozoa and small intestine is GLUT5. J Biol Chem 267:14523–14526PubMedGoogle Scholar
  35. 35.
    Dentin R, Pégorier J-P, Benhamed F et al (2004) Hepatic glucokinase is required for the synergistic action of ChREBP and SREBP-1c on glycolytic and lipogenic gene expression. J Biol Chem 279:20314–20326CrossRefPubMedGoogle Scholar
  36. 36.
    Iizuka K, Bruick RK, Liang G et al (2004) Deficiency of carbohydrate response element-binding protein (ChREBP) reduces lipogenesis as well as glycolysis. Proc Natl Acad Sci U S A 101:7281–7286CrossRefPubMedPubMedCentralGoogle Scholar
  37. 37.
    Dentin R, Benhamed F, Pégorier J-P et al (2005) Polyunsaturated fatty acids suppress glycolytic and lipogenic genes through the inhibition of ChREBP nuclear protein translocation. J Clin Invest 115:2843–2854CrossRefPubMedPubMedCentralGoogle Scholar
  38. 38.
    Tanaka T, Shen J, Abecasis GR et al (2009) Genome-wide association study of plasma polyunsaturated fatty acids in the InCHIANTI study. PLoS Genet 5:e1000338CrossRefPubMedPubMedCentralGoogle Scholar
  39. 39.
    Chambers JC, Zhang W, Sehmi J et al (2011) Genome-wide association study identifies loci influencing concentrations of liver enzymes in plasma. Nat Genet 43:1131–1138CrossRefPubMedPubMedCentralGoogle Scholar
  40. 40.
    Ishizuka Y, Nakayama K, Ogawa A et al (2014) TRIB1 downregulates hepatic lipogenesis and glycogenesis via multiple molecular interactions. J Mol Endocrinol 52:145–158CrossRefPubMedGoogle Scholar
  41. 41.
    Jump DB (2011) Fatty acid regulation of hepatic lipid metabolism. Curr Opin Clin Nutr Metab Care 14:115–120CrossRefPubMedPubMedCentralGoogle Scholar
  42. 42.
    Talukdar S, Owen BM, Song P et al (2016) FGF21 regulates sweet and alcohol preference. Cell Metab 23:344–349CrossRefPubMedGoogle Scholar
  43. 43.
    Adams AC, Cheng CC, Coskun T, Kharitonenkov A (2012) FGF21 requires βklotho to act in vivo. PLoS One 7:e49977CrossRefPubMedPubMedCentralGoogle Scholar
  44. 44.
    Global Lipids Genetics Consortium (2013) Discovery and refinement of loci associated with lipid levels. Nat Genet 45:1274–1283CrossRefGoogle Scholar
  45. 45.
    Ranawana DV, Henry CJK (2010) Are caloric beverages compensated for in the short-term by young adults? An investigation with particular focus on gender differences. Appetite 55:137–146CrossRefPubMedGoogle Scholar
  46. 46.
    Gadah NS, Kyle LA, Rogers PJ (2012) Gender differences in the satiety effects of sugar-containing drinks. Appetite 59:626CrossRefGoogle Scholar
  47. 47.
    Dupuis J, Langenberg C, Prokopenko I et al (2010) New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk. Nat Genet 42:105–116CrossRefPubMedPubMedCentralGoogle Scholar
  48. 48.
    Saxena R, Hivert M-F, Langenberg C et al (2010) Genetic variation in GIPR influences the glucose and insulin responses to an oral glucose challenge. Nat Genet 42:142–148CrossRefPubMedPubMedCentralGoogle Scholar
  49. 49.
    Wang J, Light K, Henderson M et al (2014) Consumption of added sugars from liquid but not solid sources predicts impaired glucose homeostasis and insulin resistance among youth at risk of obesity. J Nutr 144:81–86CrossRefPubMedGoogle Scholar
  50. 50.
    Bel-Serrat S, Mouratidou T, Santaliestra-Pasías AM et al (2013) Clustering of multiple lifestyle behaviours and its association to cardiovascular risk factors in children: the IDEFICS study. Eur J Clin Nutr 67:848–854CrossRefPubMedGoogle Scholar
  51. 51.
    Bremer AA, Auinger P, Byrd RS (2009) Relationship between insulin resistance-associated metabolic parameters and anthropometric measurements with sugar-sweetened beverage intake and physical activity levels in US adolescents: findings from the 1999–2004 National Health and Nutrition Examination Survey. Arch Pediatr Adolesc Med 163:328–335CrossRefPubMedPubMedCentralGoogle Scholar
  52. 52.
    Rezvani R, Cianflone K, McGahan JP et al (2013) Effects of sugar-sweetened beverages on plasma acylation stimulating protein, leptin and adiponectin: relationships with metabolic outcomes. Obesity 21:2471–2480CrossRefPubMedPubMedCentralGoogle Scholar
  53. 53.
    Angelopoulos TJ, Lowndes J, Sinnett S, Rippe JM (2016) Fructose containing sugars at normal levels of consumption do not effect adversely components of the metabolic syndrome and risk factors for cardiovascular disease. Nutrients 8:179CrossRefPubMedPubMedCentralGoogle Scholar
  54. 54.
    Black RNA, Spence M, McMahon RO et al (2006) Effect of eucaloric high- and low-sucrose diets with identical macronutrient profile on insulin resistance and vascular risk. Diabetes 55:3566–3572CrossRefPubMedGoogle Scholar
  55. 55.
    Wang M, Yu M, Fang L, Hu R-Y (2015) Association between sugar-sweetened beverages and type 2 diabetes: a meta-analysis. J Diabetes Investig 6:360–366CrossRefPubMedGoogle Scholar
  56. 56.
    Qi Q, Chu AY, Kang JH et al (2012) Sugar-sweetened beverages and genetic risk of obesity. N Engl J Med 367:1387–1396CrossRefPubMedPubMedCentralGoogle Scholar
  57. 57.
    Brunkwall L, Chen Y, Hindy G et al (2016) Sugar-sweetened beverage consumption and genetic predisposition to obesity in 2 Swedish cohorts. Am J Clin Nutr 104:809–815CrossRefPubMedPubMedCentralGoogle Scholar
  58. 58.
    Olsen NJ, Ängquist L, Larsen SC et al (2016) Interactions between genetic variants associated with adiposity traits and soft drinks in relation to longitudinal changes in body weight and waist circumference. Am J Clin Nutr 104:816–826CrossRefPubMedGoogle Scholar
  59. 59.
    Zheng Y, Li Y, Huang T et al (2016) Sugar-sweetened beverage intake, chromosome 9p21 variants, and risk of myocardial infarction in Hispanics. Am J Clin Nutr 103:1179–1184CrossRefPubMedPubMedCentralGoogle Scholar
  60. 60.
    Davis JN, Lê K-A, Walker RW et al (2010) Increased hepatic fat in overweight Hispanic youth influenced by interaction between genetic variation in PNPLA3 and high dietary carbohydrate and sugar consumption. Am J Clin Nutr 92:1522–1527CrossRefPubMedPubMedCentralGoogle Scholar
  61. 61.
    Horton TJ, Gayles EC, Prach PA et al (1997) Female rats do not develop sucrose-induced insulin resistance. Am J Phys 272:R1571–R1576Google Scholar
  62. 62.
    Reaven GM (1988) Role of insulin resistance in human disease. Diabetes 37:1595–1607CrossRefPubMedGoogle Scholar
  63. 63.
    Haffner SM, Stern MP, Hazuda HP et al (1990) Cardiovascular risk factors in confirmed prediabetic individuals: does the clock for coronary heart disease start ticking before the onset of clinical diabetes? JAMA 263:2893–2898CrossRefPubMedGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  • Nicola M. McKeown
    • 1
  • Hassan S. Dashti
    • 2
    • 3
    • 4
  • Jiantao Ma
    • 5
  • Danielle E. Haslam
    • 1
  • Jessica C. Kiefte-de Jong
    • 6
    • 7
  • Caren E. Smith
    • 2
  • Toshiko Tanaka
    • 8
  • Mariaelisa Graff
    • 9
  • Rozenn N. Lemaitre
    • 10
  • Denis Rybin
    • 11
  • Emily Sonestedt
    • 12
  • Alexis C. Frazier-Wood
    • 13
  • Dennis O. Mook-Kanamori
    • 14
    • 15
  • Yanping Li
    • 16
  • Carol A. Wang
    • 17
  • Elisabeth T. M. Leermakers
    • 6
  • Vera Mikkilä
    • 18
    • 19
  • Kristin L. Young
    • 9
  • Kenneth J. Mukamal
    • 20
  • L. Adrienne Cupples
    • 5
    • 21
  • Christina-Alexandra Schulz
    • 12
  • Tzu-An Chen
    • 13
  • Ruifang Li-Gao
    • 14
  • Tao Huang
    • 16
  • Wendy H. Oddy
    • 22
    • 23
  • Olli Raitakari
    • 18
    • 24
  • Kenneth Rice
    • 25
  • James B. Meigs
    • 4
    • 26
    • 27
  • Ulrika Ericson
    • 12
  • Lyn M. Steffen
    • 28
  • Frits R. Rosendaal
    • 14
  • Albert Hofman
    • 6
  • Mika Kähönen
    • 29
  • Bruce M. Psaty
    • 10
    • 30
    • 31
    • 32
  • Louise Brunkwall
    • 12
  • Andre G. Uitterlinden
    • 6
  • Jorma Viikari
    • 33
    • 34
  • David S. Siscovick
    • 35
  • Ilkka Seppälä
    • 36
  • Kari E. North
    • 9
  • Dariush Mozaffarian
    • 37
  • Josée Dupuis
    • 5
    • 21
  • Marju Orho-Melander
    • 12
  • Stephen S. Rich
    • 38
  • Renée de Mutsert
    • 14
  • Lu Qi
    • 16
  • Craig E. Pennell
    • 17
  • Oscar H. Franco
    • 6
  • Terho Lehtimäki
    • 37
  • Mark A. Herman
    • 39
  1. 1.Nutritional Epidemiology Program, Jean Mayer US Department of Agriculture Human Nutrition Research Center on AgingTufts UniversityBostonUSA
  2. 2.Nutrition & Genomics Laboratory, Jean Mayer US Department of Agriculture Human Nutrition Research Center on AgingTufts UniversityBostonUSA
  3. 3.Center for Genomic MedicineMassachusetts General HospitalBostonUSA
  4. 4.Program in Medical and Population GeneticsBroad InstituteCambridgeUSA
  5. 5.National Heart, Lung, and Blood Institute’s Framingham Heart Study and Population Sciences BranchFraminghamUSA
  6. 6.Department of EpidemiologyErasmus MC University Medical CenterRotterdamthe Netherlands
  7. 7.Global Public HealthLeiden University CollegeThe Haguethe Netherlands
  8. 8.Translational Gerontology BranchNational Institute on AgingBaltimoreUSA
  9. 9.Department of EpidemiologyUniversity of North CarolinaChapel HillUSA
  10. 10.Department of MedicineUniversity of WashingtonSeattleUSA
  11. 11.Boston University Data Coordinating CenterBoston UniversityBostonUSA
  12. 12.Department of Clinical Sciences MalmöLund UniversityMalmöSweden
  13. 13.USDA/ARS Children’s Nutrition Research Center, Department of PediatricsBaylor College of MedicineHoustonUSA
  14. 14.Department of Clinical EpidemiologyLeiden University Medical CenterLeidenthe Netherlands
  15. 15.Department of Public Health and Primary CareLeiden University Medical CenterLeidenthe Netherlands
  16. 16.Department of Nutrition, Harvard T. H. Chan School of Public HealthHarvard UniversityBostonUSA
  17. 17.School of Women’s and Infants’ HealthThe University of Western AustraliaCrawleyAustralia
  18. 18.Research Centre of Applied and Preventive Cardiovascular MedicineUniversity of TurkuTurkuFinland
  19. 19.Department of Food and Environmental SciencesUniversity of HelsinkiHelsinkiFinland
  20. 20.Division of General Medicine and Primary CareHarvard Medical School and Beth Israel Deaconess Medical CenterBostonUSA
  21. 21.Department of BiostatisticsBoston University School of Public HealthBostonUSA
  22. 22.Telethon Kids InstituteSubiacoAustralia
  23. 23.Menzies Institute for Medical ResearchUniversity of TasmaniaHobartAustralia
  24. 24.Department of Clinical Physiology and Nuclear MedicineTurku University HospitalTurkuFinland
  25. 25.Department of BiostatisticsUniversity of WashingtonSeattleUSA
  26. 26.Division of General Internal MedicineMassachusetts General HospitalBostonUSA
  27. 27.Department of MedicineHarvard Medical SchoolBostonUSA
  28. 28.Division of Epidemiology and Community HealthUniversity of MinnesotaMinneapolisUSA
  29. 29.Department of Clinical Physiology, Tampere University Hospital, and Finnish Cardiovascular Research Center – Tampere, Faculty of Medicine and Life SciencesUniversity of TampereTampereFinland
  30. 30.Department of EpidemiologyUniversity of WashingtonSeattleUSA
  31. 31.Department of Health ServicesUniversity of WashingtonSeattleUSA
  32. 32.Group Health Research InstituteGroup Health CooperativeSeattleUSA
  33. 33.Department of MedicineUniversity of TurkuTurkuFinland
  34. 34.Division of MedicineTurku University HospitalTurkuFinland
  35. 35.The New York Academy of MedicineNew YorkUSA
  36. 36.Department of Clinical Chemistry, Fimlab Laboratories, and Finnish Cardiovascular Research Center – Tampere, Faculty of Medicine and Life SciencesUniversity of TampereTampereFinland
  37. 37.Friedman School of Nutrition Science and PolicyTufts UniversityBostonUSA
  38. 38.Center for Public Health GenomicsUniversity of VirginiaCharlottesvilleUSA
  39. 39.Division Of Endocrinology, Metabolism, and Nutrition, Department of MedicineDuke University School of MedicineDurhamUSA

Personalised recommendations