Advertisement

Acta Diabetologica

, Volume 56, Issue 5, pp 569–579 | Cite as

Glutamate interactions with obesity, insulin resistance, cognition and gut microbiota composition

  • María Encarnación Palomo-Buitrago
  • Mònica Sabater-Masdeu
  • Jose Maria Moreno-Navarrete
  • Estefanía Caballano-Infantes
  • María Arnoriaga-Rodríguez
  • Clàudia Coll
  • Lluís Ramió
  • Martina Palomino-Schätzlein
  • Patricia Gutiérrez-Carcedo
  • Vicente Pérez-Brocal
  • Rafael Simó
  • Andrés Moya
  • Wifredo Ricart
  • José Raúl HeranceEmail author
  • José Manuel Fernández-RealEmail author
Original Article
Part of the following topical collections:
  1. Gut Microbiome and Metabolic Disorders

Abstract

Aims

To investigate the interactions among fecal and plasma glutamate levels, insulin resistance cognition and gut microbiota composition in obese and non-obese subjects.

Methods

Gut microbiota composition (shotgun) and plasma and fecal glutamate, glutamine and acetate (NMR) were analyzed in a pilot study of obese and non-obese subjects (n = 35). Neuropsychological tests [Trail making test A (TMT-A) and Trail making test B (TMT-B)] scores measured cognitive information about processing speed, mental flexibility and executive function.

Results

Trail-making test score was significantly altered in obese compared with non-obese subjects. Fecal glutamate and glutamate/glutamine ratio tended to be lower among obese subjects while fecal glutamate/acetate ratio was negatively associated with BMI and TMT-A scores. Plasma glutamate/acetate ratio was negatively associated with TMT-B. The relative abundance (RA) of some bacterial families influenced glutamate levels, given the positive association of fecal glutamate/glutamine ratio with Corynebacteriaceae, Coriobacteriaceae and Burkholderiaceae RA. In contrast, Streptococaceae RA, that was significantly higher in obese subjects, negatively correlated with fecal glutamate/glutamine ratio. To close the circle, Coriobacteriaceae/Streptococaceae ratio and Corynebacteriaceae/Streptococaceae ratio were associated both with TMT-A scores and fecal glutamate/glutamine ratio.

Conclusions

Gut microbiota composition is associated with processing speed and mental flexibility in part through changes in fecal and plasma glutamate metabolism.

Keywords

Microbiota Metabolomics Glutamate Trail making test Cognition 

Notes

Acknowledgements

The authors acknowledge the technical assistance of Emilio Loshuertos (Girona Biomedical Research Institute, IdIBGi) and Oscar Rovira.

Author contributions

MEPB, MSM and MAR researched the data, performed the statistical analysis and wrote and edited the manuscript. CC and LR researched the data and performed neuropsychological assessment TMT-A and TMT-B MP-S, PG-C and JRH researched the data, performed the 1H-NMR for plasma and feces metabolomic analysis and contributed to the writing and editing of the manuscript. VP-B, AM performed the gut microbiota composition analysis and contributed to the writing of the manuscript. JMM-N, EC-I, RS, JRH, WR contributed to the discussion and reviewed the manuscript. JMF-R Carried out the conception and coordination of the study, contributed to statistical analysis and writing the manuscript and directly participated in the execution of the study. JMF-R is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Funding

This work was supported by FIS Grant (PI15/01934), FIS Grant (PI16/02064) from the National Institute of Health Carlos III and by ERDF (European Regional Development Fund).

Compliance with ethical standards

Conflict of interest

The authors declare no conflict of interest.

Ethical standards

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Supplementary material

592_2019_1313_MOESM1_ESM.pdf (273 kb)
Supplementary material 1 (PDF 272 KB)
592_2019_1313_MOESM2_ESM.pdf (46 kb)
Supplementary material 2 (PDF 45 KB)

References

  1. 1.
    Dinan TG, Cryan JF (2015) The impact of gut microbiota on brain and behaviour: implications for psychiatry. Curr Opin Clin Nutr Metab Care 18:552–558.  https://doi.org/10.1097/MCO.0000000000000221 CrossRefGoogle Scholar
  2. 2.
    Clarke G, Stilling RM, Kennedy PJ, Stanton C, Cryan JF, Dinan TG (2014) Minireview: Gut microbiota: the neglected endocrine organ. Mol Endocrinol 28:1221–1238.  https://doi.org/10.1210/me.2014-1108 CrossRefGoogle Scholar
  3. 3.
    Mazzoli R, Pessione E (2016) The neuro-endocrinological role of microbial glutamate and GABA signaling. Front Microbiol 7:1934.  https://doi.org/10.3389/fmicb.2016.01934 CrossRefGoogle Scholar
  4. 4.
    Hedberg TG, Stanton PK (1996) Long-term plasticity in cingulate cortex requires both NMDA and metabotropic glutamate receptor activation. Eur J Pharmacol 310:19–27.  https://doi.org/10.1016/0014-2999(96)00371-8 CrossRefGoogle Scholar
  5. 5.
    Ernst T, Jiang CS, Nakama H, Buchthal S, Chang L (2010) Lower brain glutamate is associated with cognitive deficits in HIV patients: a new mechanism for HIV-associated neurocognitive disorder. J Magn Reson Imaging 32:1045–1053.  https://doi.org/10.1002/jmri.22366 CrossRefGoogle Scholar
  6. 6.
    Corrigan JD, Hinkeldey NS (1987) Relationships between Parts A and B of the trail making test. J Clin Psychol 43:402–409CrossRefGoogle Scholar
  7. 7.
    Lezak MD, Howieson DB, Loring DW, Hannay HJ, Fischer JS (2004) Neuropsychological assessment, 4th edn. Oxford University, New YorkGoogle Scholar
  8. 8.
    Okumoto S, Funck D, Trovato M, Forlani G (2016) Editorial: amino acids of the glutamate family: functions beyond primary metabolism. Fornt Plant Sci 7:318.  https://doi.org/10.3389/fpls.2016.00318 Google Scholar
  9. 9.
    Yan D (2007) Protection of the glutamate pool concentration in enteric bacteria. Proc Natl Acad Sci USA 104:9475–9480CrossRefGoogle Scholar
  10. 10.
    Buckel W, Barker HA (1974) Two pathways of glutamate fermentation by anaerobic bacteria. J Bacteriol 117:1248–1260.  https://doi.org/10.1073/pnas.0703360104 Google Scholar
  11. 11.
    Org E, Blum Y, Kasela S, et al (2017) Relationships between gut microbiota, plasma metabolites, and metabolic syndrome traits in the METSIM cohort. Genome Biol 18:70.  https://doi.org/10.1186/s13059-017-1194-2 CrossRefGoogle Scholar
  12. 12.
    Moszak M, Klupczyńska A, Kanikowska A, et al (2018) The influence of a 3-week body mass reduction program on the metabolic parameters and free amino acid profiles in adult Polish people with obesity. Adv Clin Exp Med 27:749–757.  https://doi.org/10.17219/acem/70796 CrossRefGoogle Scholar
  13. 13.
    Wahl S, Yu Z, Kleber M, et al (2012) Childhood obesity is associated with changes in the serum metabolite profile. Obes Facts 5:660–670.  https://doi.org/10.1159/000343204 CrossRefGoogle Scholar
  14. 14.
    Reinehr T, Wolters B, Knop C, et al (2014) Changes in the serum metabolite profile in obese children with weight loss. Eur J Nutr 54:173–181.  https://doi.org/10.1007/s00394-014-0698-8 CrossRefGoogle Scholar
  15. 15.
    Cheng S, Rhee EP, Larson MG, et al (2012) Metabolite profiling identifies pathways associated with metabolic risk in humans. Circulation 125:2222–2231.  https://doi.org/10.1161/CIRCULATIONAHA.111.067827 CrossRefGoogle Scholar
  16. 16.
    Hooper LV, Wong MH, Thelin A, Hansson L, Falk PG, Gordon JI (2001) Molecular analysis of commensal host-microbial relationships in the intestine. Science 291:881–884CrossRefGoogle Scholar
  17. 17.
    Liu R, Hong J, Xu X, et al (2017) Gut microbiome and serum metabolome alterations in obesity and after weight-loss intervention. Nat Med 23:859–868.  https://doi.org/10.1038/nm.4358 CrossRefGoogle Scholar
  18. 18.
    Serrano M, Moreno-Navarrete JM, Puig J, et al (2013) Serum lipopolysaccharide-binding protein as a marker of atherosclerosis. Atherosclerosis 230:223–227.  https://doi.org/10.1016/j.atherosclerosis.2013.07.004 CrossRefGoogle Scholar
  19. 19.
    Wishart DS, Jewison T, Guo AC, et al (2013) HMDB 3. 0—the human metabolome. Database 41:D801–D807.  https://doi.org/10.1093/nar/gks1065 Google Scholar
  20. 20.
    Ulrich EL, Akutsu H, Doreleijers JF, et al (2008) BioMagResBank. Nucl Acids Res 36:402–408.  https://doi.org/10.1093/nar/gkm957 CrossRefGoogle Scholar
  21. 21.
    Langmead B, Salzberg SL (2012) Fast gapped-read alignment with Bowtie 2. Nat Methods 9:357–359.  https://doi.org/10.1038/nmeth.1923 CrossRefGoogle Scholar
  22. 22.
    Lezak MD (1984) Neuropsychological assessment in behavioral toxicology—developing techniques and interpretative issues. Scand J Work Environ Heal 10:25–29CrossRefGoogle Scholar
  23. 23.
    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 9:311–326.  https://doi.org/10.1016/j.cmet.2009.02.002 CrossRefGoogle Scholar
  24. 24.
    Ottosson F, Brunkwall L, Ericson U, et al (2018) Connection between BMI-related plasma metabolite profile and gut microbiota. J Clin Endocrinol Metab 103:1491–1501.  https://doi.org/10.1210/jc.2017-02114 CrossRefGoogle Scholar
  25. 25.
    Fernandez-Real JM, Serino M, Blasco G, et al (2015) Gut microbiota interacts with brain microstructure and function. J Clin Endocrinol Metab 100:4505–4513.  https://doi.org/10.1210/jc.2015-3076 CrossRefGoogle Scholar
  26. 26.
    Leibowitz A, Boyko M, Shapira Y, Zlotnik A (2012) Blood glutamate scavenging: insight into neuroprotection. Int J Mol Sci 13:10041–10066.  https://doi.org/10.3390/ijms130810041 CrossRefGoogle Scholar
  27. 27.
    Zhang K, Fan Z, Wang Y, Faraone SV, Yang L, Chang S (2017) Genetic analysis for cognitive flexibility in the trail-making test in attention deficit hyperactivity disorder patients from single nucleotide polymorphism, gene to pathway level. World J Biol Psychiatry.  https://doi.org/10.1080/15622975.2017.1386324 Google Scholar
  28. 28.
    Ibrahim-Verbaas CA, Bressler J, Debette S et al (2016) GWAS for executive function and processing speed suggests involvement of the CADM2 gene. Mol Psychiatry 21:189–197.  https://doi.org/10.1038/mp.2015.37 CrossRefGoogle Scholar
  29. 29.
    Sano C (2009) History of glutamate production. Am J Clin Nutr 90:728–732.  https://doi.org/10.3945/ajcn.2009.27462F CrossRefGoogle Scholar
  30. 30.
    Kinoshita S, Udaka S, Shimono M (2004) Studies on amino acid fermentation production of L-glutamic acid by various microorganisms. J Gen Appl Microbiol 50:331–343Google Scholar
  31. 31.
    Lee JY, Na YA, Kim E, Lee HS, Kim P (2016) The Actinobacterium corynebacterium glutamicum, an industrial workhorse. J Microbiol Biotechnol 26:807–822.  https://doi.org/10.4014/jmb.1601.01053 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Italia S.r.l., part of Springer Nature 2019

Authors and Affiliations

  • María Encarnación Palomo-Buitrago
    • 1
  • Mònica Sabater-Masdeu
    • 1
    • 2
  • Jose Maria Moreno-Navarrete
    • 1
    • 2
  • Estefanía Caballano-Infantes
    • 1
    • 2
  • María Arnoriaga-Rodríguez
    • 1
    • 2
  • Clàudia Coll
    • 3
  • Lluís Ramió
    • 3
  • Martina Palomino-Schätzlein
    • 9
  • Patricia Gutiérrez-Carcedo
    • 4
  • Vicente Pérez-Brocal
    • 5
    • 6
  • Rafael Simó
    • 7
    • 8
  • Andrés Moya
    • 5
    • 6
  • Wifredo Ricart
    • 1
    • 2
  • José Raúl Herance
    • 4
    Email author
  • José Manuel Fernández-Real
    • 1
    • 2
    Email author
  1. 1.Department of Diabetes, Endocrinology and Nutrition, Hospital of Girona “Dr Josep Trueta”Institut d’Investigació Biomèdica de Girona (IDIBGI)GironaSpain
  2. 2.CIBER de la Fisiopatología de la Obesidad y Nutrición (CIBERobn, CB06/03/010) and Instituto de Salud Carlos III (ISCIII)GironaSpain
  3. 3.Department of NeurologyInstitut d’Investigació Biomèdica de Girona (IDIBGI), Hospital of Girona “Dr Josep Trueta”GironaSpain
  4. 4.Medical Molecular Imaging Research Group, Vall d’Hebron Research InstituteInstituto de Salud Carlos III (ISCIII), CIBBIM-Nanomedicine, CIBER-bbnBarcelonaSpain
  5. 5.Genomics and Health AreaFoundation for the Promotion of Sanitary and Biomedical Research (FISABIO)ValènciaSpain
  6. 6.CIBER de Epidemiology y Salud Pública (CIBERESP), Instituto de Salud Carlos IIIMadridSpain
  7. 7.Diabetes and Metabolism Research UnitVall d’Hebron Research InstituteBarcelonaSpain
  8. 8.Department of Endocrinology, Vall d’Hebron Research InstituteInstituto de Salud Carlos III (ISCIII), CIBERDEMBarcelonaSpain
  9. 9.NMR FacilityCentro de Investigación Principe FelipeValenciaSpain

Personalised recommendations