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 CupplesChristina 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

Research output: Contribution to journalArticlepeer-review

12 Scopus citations

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 [loge 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 loge 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).

Original languageEnglish (US)
Pages (from-to)317-330
Number of pages14
JournalDiabetologia
Volume61
Issue number2
DOIs
StatePublished - Feb 1 2018

Bibliographical note

Funding Information:
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.

Funding Information:
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.

Funding Information:
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. 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.

Keywords

  • Carbohydrate metabolism
  • Epidemiology
  • Genetics
  • Meta-analysis
  • Nutrition
  • Type 2 diabetes

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