Diabetologia

pp 1–12 | Cite as

Plasma branched chain/aromatic amino acids, enriched Mediterranean diet and risk of type 2 diabetes: case-cohort study within the PREDIMED Trial

  • Miguel Ruiz-Canela
  • Marta Guasch-Ferré
  • Estefanía Toledo
  • Clary B. Clish
  • Cristina Razquin
  • Liming Liang
  • Dong D. Wang
  • Dolores Corella
  • Ramón Estruch
  • Álvaro Hernáez
  • Edward Yu
  • Enrique Gómez-Gracia
  • Yan Zheng
  • Fernando Arós
  • Dora Romaguera
  • Courtney Dennis
  • Emilio Ros
  • José Lapetra
  • Lluis Serra-Majem
  • Christopher Papandreou
  • Olga Portoles
  • Montserrat Fitó
  • Jordi Salas-Salvadó
  • Frank B. Hu
  • Miguel A. Martínez-González
Article

Abstract

Aims/hypothesis

Branched-chain amino acids (BCAAs) and aromatic amino acids (AAAs) are associated with type 2 diabetes. However, repeated measurements of BCAA/AAA and their interactions with dietary interventions have not been evaluated. We investigated the associations between baseline and changes at 1 year in BCAA/AAA with type 2 diabetes in the context of a Mediterranean diet (MedDiet) trial.

Methods

We included 251 participants with incident type 2 diabetes and a random sample of 694 participants (641 participants without type 2 diabetes and 53 overlapping cases) in a case-cohort study nested within the PREvención con DIeta MEDiterránea (PREDIMED) trial. Participants were randomised to a MedDiet+extra-virgin olive oil (n = 273), a MedDiet+nuts (n = 324) or a control diet (n = 295). We used LC-MS/MS to measure plasma levels of amino acids. Type 2 diabetes was a pre-specified secondary outcome of the PREDIMED trial.

Results

Elevated plasma levels of individual BCAAs/AAAs were associated with higher type 2 diabetes risk after a median follow-up of 3.8 years: multivariable HR for the highest vs lowest quartile ranged from 1.32 for phenylalanine ([95% CI 0.90, 1.92], p for trend = 0.015) to 3.29 for leucine ([95% CI 2.03, 5.34], p for trend<0.001). Increases in BCAA score at 1 year were associated with higher type 2 diabetes risk in the control group with HR per SD = 1.61 (95% CI 1.02, 2.54), but not in the MedDiet groups (p for interaction <0.001). The MedDiet+extra-virgin olive oil significantly reduced BCAA levels after 1 year of intervention (p = 0.005 vs the control group).

Conclusions/interpretation

Our results support that higher baseline BCAAs and their increases at 1 year were associated with higher type 2 diabetes risk. A Mediterranean diet rich in extra-virgin olive oil significantly reduced the levels of BCAA and attenuated the positive association between plasma BCAA levels and type 2 diabetes incidence.

Clinical trial number: SRCTN35739639 (www.controlled-trials.com)

Keywords

Aromatic amino acids Branched-chain amino acids Mediterranean diet Type 2 diabetes 

Abbreviations

AAA

Aromatic amino acid

BCAA

Branched-chain amino acid

EVOO

Extra-virgin olive oil

MedDiet

Mediterranean diet (trial intervention)

MET

Metabolic equivalent task

mTOR

Mammalian target of rapamycin

PREDIMED

PREvención con DIeta MEDiterránea

Notes

Acknowledgements

We are very grateful to all the participants for their enthusiastic collaboration, the PREDIMED personnel for their excellent assistance, and the personnel of all affiliated primary care centres. CIBEROBN is an initiative of Instituto de Salud Carlos III, Spain.

Contribution statement

MR-C and MAM-G conducted the statistical analyses and drafted the article. MR-C, FBH, ET, CBC, LL, JS-S, and MAM-G made substantial contributions to the conception and design of the work. All authors contributed substantially in the acquisition of data or analysis and interpretation of data. All authors revised the article critically for important intellectual content. All authors approved the version to be published.

Duality of interest

ER has received honoraria for lectures and grants for research through his institution from the California Walnut Commission and is a nonpaid member of its Scientific Advisory Committee. JS-S has received grants for research through his institution from the International Nut and Dried Fruit Council and is a nonpaid member of its Scientific Advisory Committee. The rest of the authors declare that there is no duality of interest associated with this manuscript.

Supplementary material

125_2018_4611_MOESM1_ESM.pdf (264 kb)
ESM (PDF 263 kb)

References

  1. 1.
    Yoon M-S (2016) The emerging role of branched-chain amino acids in insulin resistance and metabolism. Nutrients 8:405.  https://doi.org/10.3390/nu8070405 CrossRefPubMedCentralGoogle Scholar
  2. 2.
    Roberts LD, Koulman A, Griffin JL (2014) Towards metabolic biomarkers of insulin resistance and type 2 diabetes: progress from the metabolome. Lancet Diabetes Endocrinol 2:65–75CrossRefPubMedGoogle Scholar
  3. 3.
    Newgard CB, An J, Bain JR et al (2009) A branched-chain amino acid-related metabolic signature that differentiates obese and lean humans and contributes to insulin resistance. Cell Metab 9:311–326CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Wang TJ, Larson MG, Vasan RS et al (2011) Metabolite profiles and the risk of developing diabetes. Nat Med 17:448–453CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Qiu G, Zheng Y, Wang H et al (2016) Plasma metabolomics identified novel metabolites associated with risk of type 2 diabetes in two prospective cohorts of Chinese adults. Int J Epidemiol 45:1507–1516CrossRefPubMedGoogle Scholar
  6. 6.
    Guasch-Ferré M, Hruby A, Toledo E et al (2016) Metabolomics in prediabetes and diabetes: a systematic review and meta-analysis. Diabetes Care 39:833–846CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Tricò D, Prinsen H, Giannini C et al (2017) Elevated α-hydroxybutyrate and BCAA levels predict deterioration of glycemic control in adolescents. J Clin Endocrinol Metab 102:2473–2481CrossRefPubMedGoogle Scholar
  8. 8.
    Connelly MA, Wolak-Dinsmore J, Dullaart RPF (2017) Branched chain amino acids are associated with insulin resistance independent of leptin and adiponectin in subjects with varying degrees of glucose tolerance. Metab Syndr Relat Disord 15:183–186CrossRefPubMedGoogle Scholar
  9. 9.
    Yu D, Moore SC, Matthews CE et al (2016) Plasma metabolomic profiles in association with type 2 diabetes risk and prevalence in Chinese adults. Metabolomics 12:3CrossRefPubMedGoogle Scholar
  10. 10.
    Tulipani S, Palau-Rodriguez M, Miñarro Alonso A et al (2016) Biomarkers of morbid obesity and prediabetes by metabolomic profiling of human discordant phenotypes. Clin Chim Acta 463:53–61CrossRefPubMedGoogle Scholar
  11. 11.
    Menni C, Migaud M, Glastonbury CA et al (2016) Metabolomic profiling to dissect the role of visceral fat in cardiometabolic health. Obesity 24:1380–1388CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Wiklund P, Zhang X, Pekkala S et al (2016) Insulin resistance is associated with altered amino acid metabolism and adipose tissue dysfunction in normoglycemic women. Sci Rep 6:24540CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Lu Y, Wang Y, Ong C-N et al (2016) Metabolic signatures and risk of type 2 diabetes in a Chinese population: an untargeted metabolomics study using both LC-MS and GC-MS. Diabetologia 59:2349–2359CrossRefPubMedGoogle Scholar
  14. 14.
    Stancáková A, Civelek M, Saleem NK et al (2012) Hyperglycemia and a common variant of GCKR are associated with the levels of eight amino acids in 9,369 Finnish men. Diabetes 61:1895–1902CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    Estruch R, Ros E, Salas-Salvadó J et al (2013) Primary prevention of cardiovascular disease with a Mediterranean diet. N Engl J Med 368:1279–1290CrossRefPubMedGoogle Scholar
  16. 16.
    Salas-Salvadó J, Bulló M, Estruch R et al (2014) Prevention of diabetes with Mediterranean diets. Ann Intern Med 160:1–10CrossRefPubMedGoogle Scholar
  17. 17.
    Elosua R, Marrugat J, Molina L et al (1994) Validation of the Minnesota Leisure Time Physical Activity questionnaire in Spanish men. The MARATHOM investigators. Am J Epidemiol 139:1197–1209CrossRefPubMedGoogle Scholar
  18. 18.
    Mascanfroni ID, Takenaka MC, Yeste A et al (2015) Metabolic control of type 1 regulatory T cell differentiation by AHR and HIF1-α. Nat Med 21:638–646CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    O’Sullivan JF, Morningstar JE, Yang Q et al (2017) Dimethylguanidino valeric acid is a marker of liver fat and predicts diabetes. J Clin Invest 127:4394–4402CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Rowan S, Jiang S, Korem T et al (2017) Involvement of a gut-retina axis in protection against dietary glycemia-induced age-related macular degeneration. Proc Natl Acad Sci U S A 114:E4472–E4481CrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    American Diabetes Association (2015) 2. Classification and diagnosis of diabetes. Diabetes Care 38:S8–S16CrossRefGoogle Scholar
  22. 22.
    Blom G (1958) Statistical estimates and transformed beta-variables. Wiley, New YorkGoogle Scholar
  23. 23.
    Barlow WE, Ichikawa L, Rosner D, Izumi S (1999) Analysis of case-cohort designs. 52:1165–1172Google Scholar
  24. 24.
    Carpenter J, Kenward M (2013) Multiple imputation and its application. Wiley, LondonCrossRefGoogle Scholar
  25. 25.
    Lotta LA, Scott RA, Sharp SJ et al (2016) Genetic predisposition to an impaired metabolism of the branched-chain amino acids and risk of type 2 diabetes: a Mendelian randomisation analysis. PLoS Med 13:e1002179CrossRefPubMedPubMedCentralGoogle Scholar
  26. 26.
    Ruiz-Canela M, Toledo E, Clish CB et al (2016) Plasma branched-chain amino acids and incident cardiovascular disease in the PREDIMED trial. Clin Chem 62:582–592CrossRefPubMedPubMedCentralGoogle Scholar
  27. 27.
    Zhenyukh O, Civantos E, Ruiz-Ortega M et al (2017) High concentration of branched-chain amino acids promotes oxidative stress, inflammation and migration of human peripheral blood mononuclear cells via mTORC1 activation. Free Radic Biol Med 104:165–177CrossRefPubMedGoogle Scholar
  28. 28.
    Mahendran Y, Jonsson A, Have CT et al (2017) Genetic evidence of a causal effect of insulin resistance on branched-chain amino acid levels. Diabetologia 60:873–878CrossRefPubMedGoogle Scholar
  29. 29.
    Tremblay F, Krebs M, Dombrowski L et al (2005) Overactivation of S6 kinase 1 as a cause of human insulin resistance during increased amino acid availability. Diabetes 54:2674–2684CrossRefPubMedGoogle Scholar
  30. 30.
    Jang C, Oh SF, Wada S et al (2016) A branched-chain amino acid metabolite drives vascular fatty acid transport and causes insulin resistance. Nat Med 22:421–426CrossRefPubMedPubMedCentralGoogle Scholar
  31. 31.
    Zheng Y, Li Y, Qi Q et al (2016) Cumulative consumption of branched-chain amino acids and incidence of type 2 diabetes. Int J Epidemiol 45:1482–1492CrossRefPubMedPubMedCentralGoogle Scholar
  32. 32.
    Zheng Y, Ceglarek U, Huang T et al (2016) Weight-loss diets and 2-y changes in circulating amino acids in 2 randomized intervention trials. Am J Clin Nutr 103:505–511CrossRefPubMedPubMedCentralGoogle Scholar
  33. 33.
    Zhao X, Gang X, Liu Y et al (2016) Using metabolomic profiles as biomarkers for insulin resistance in childhood obesity: a systematic review. J Diabetes Res 2016:1–12Google Scholar
  34. 34.
    Kujala UM, Peltonen M, Laine MK et al (2016) Branched-chain amino acid levels are related with surrogates of disturbed lipid metabolism among older men. Front Med 3:57CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Miguel Ruiz-Canela
    • 1
    • 2
    • 3
  • Marta Guasch-Ferré
    • 3
    • 4
    • 5
    • 6
  • Estefanía Toledo
    • 1
    • 2
    • 3
  • Clary B. Clish
    • 7
  • Cristina Razquin
    • 1
    • 2
    • 3
  • Liming Liang
    • 8
  • Dong D. Wang
    • 4
  • Dolores Corella
    • 3
    • 9
  • Ramón Estruch
    • 3
    • 10
  • Álvaro Hernáez
    • 3
    • 11
  • Edward Yu
    • 4
    • 12
  • Enrique Gómez-Gracia
    • 13
  • Yan Zheng
    • 4
  • Fernando Arós
    • 3
    • 14
  • Dora Romaguera
    • 3
    • 15
  • Courtney Dennis
    • 7
  • Emilio Ros
    • 3
    • 16
  • José Lapetra
    • 3
    • 17
  • Lluis Serra-Majem
    • 3
    • 18
  • Christopher Papandreou
    • 3
    • 5
  • Olga Portoles
    • 3
    • 9
  • Montserrat Fitó
    • 3
    • 11
  • Jordi Salas-Salvadó
    • 3
    • 5
  • Frank B. Hu
    • 4
    • 6
    • 12
  • Miguel A. Martínez-González
    • 1
    • 2
    • 3
    • 4
  1. 1.Department of Preventive Medicine and Public Health, Facultad de MedicinaUniversidad de NavarraPamplonaSpain
  2. 2.IdiSNA, Navarra Institute for Health ResearchPamplonaSpain
  3. 3.CIBER Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos IIIMadridSpain
  4. 4.Department of NutritionHarvard T.H. Chan School of Public HealthBostonUSA
  5. 5.Human Nutrition Unit, Faculty of Medicine and Health Sciences, Pere Virgili Health Research InstituteRovira i Virgili UniversityReusSpain
  6. 6.Channing Division of Network Medicine, Department of MedicineBrigham and Women’s Hospital and Harvard Medical SchoolBostonUSA
  7. 7.Broad Institute of MIT and Harvard UniversityCambridgeUSA
  8. 8.Department of BiostatisticsHarvard T.H. Chan School of Public HealthBostonUSA
  9. 9.Department of Preventive MedicineUniversity of ValenciaValenciaSpain
  10. 10.Department of Internal Medicine, Biomedical Research Institute August Pi Sunyer (IDI- BAPS), Hospital Clinic, University of BarcelonaBarcelonaSpain
  11. 11.Cardiovascular and Nutrition Research Group (Regicor Study Group)Hospital del Mar Research Institute (IMIM)BarcelonaSpain
  12. 12.Department of Epidemiology, Harvard T.H. Chan School of Public HealthBostonUSA
  13. 13.Department of Preventive MedicineUniversity of MalagaMalagaSpain
  14. 14.Department of CardiologyUniversity Hospital, University of the Basque Country UPV/EHUVitoria-GasteizSpain
  15. 15.Health Research Institute of the Balearic Islands (IdISBa)University Hospital Son EspasesMallorcaSpain
  16. 16.Lipid Clinic, Department of Endocrinology and Nutrition Biomedical Research Institute August Pi Sunyer (IDIBAPS), Hospital ClinicUniversity of BarcelonaBarcelonaSpain
  17. 17.Department of Family Medicine, Research Unit, Primary Care Division of SevillaSevillaSpain
  18. 18.Research Institute of Biomedical and Health Sciences and Medical School University of Las Palmas de Gran CanariasLas Palmas de Gran CanariaSpain

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