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Egshatyan L, Kashtanova D, Popenko A, et al. Gut microbiota and diet in patients with different glucose tolerance. Endocr Connect. 2015;5(1):1-9doi: 10.1530/EC-15-0094.
Egshatyan, L., Kashtanova, D., Popenko, A., Tkacheva, O., Tyakht, A., Alexeev, D., Karamnova, N., Kostryukova, E., Babenko, V., Vakhitova, M., & Boytsov, S. (2016). Gut microbiota and diet in patients with different glucose tolerance. Endocrine connections, 5(1), 1-9. https://doi.org/10.1530/EC-15-0094
Egshatyan, Lilit, et al. "Gut microbiota and diet in patients with different glucose tolerance." Endocrine connections vol. 5,1 (2016): 1-9. doi: https://doi.org/10.1530/EC-15-0094
Egshatyan L, Kashtanova D, Popenko A, Tkacheva O, Tyakht A, Alexeev D, Karamnova N, Kostryukova E, Babenko V, Vakhitova M, Boytsov S. Gut microbiota and diet in patients with different glucose tolerance. Endocr Connect. 2016 Jan;5(1):1-9. doi: 10.1530/EC-15-0094. Epub 2015 Nov 10. PMID: 26555712; PMCID: PMC4674628.
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Endocr Connect. 2016 Jan;5(1):1-9. doi: 10.1530/EC-15-0094. Epub 2015 Nov 10.

Gut microbiota and diet in patients with different glucose tolerance.

Endocrine connections

Lilit Egshatyan, Daria Kashtanova, Anna Popenko, Olga Tkacheva, Alexander Tyakht, Dmitry Alexeev, Natalia Karamnova, Elena Kostryukova, Vladislav Babenko, Maria Vakhitova, Sergey Boytsov

Affiliations

  1. 'Research of Age and Age-Associated Conditions' DepartmentNational Research Centre for Preventive Medicine, Building 10, Petroverigskiy Lane, Moscow RF 101000, Russian FederationLaboratory of BioinformaticsScientific Research Institute for Physical-Chemical Medicine, Building 1a, Malaya Pirogovskaya street, Moscow RF 119435, Russian FederationThe 'Russian Clinical Research Center for Gerontology'16, 1st Leonova Street, Moscow RF 192226, Russian Federation'Chronic Noncommunicable Diseases Primary Prevention in the Healthcare System' DepartmentNational Research Centre for Preventive Medicine, bld. 10, Petroverigskiy Lane, MoscowMoscow Institute of Physics and TechnologyDolgoprudny, Institusky Lane, 9 Moscow, RF, 141700, Russian FederationNational Research Centre for Preventive Medicinebld. 10, Petroverigskiy Lane, Moscow [email protected].
  2. 'Research of Age and Age-Associated Conditions' DepartmentNational Research Centre for Preventive Medicine, Building 10, Petroverigskiy Lane, Moscow RF 101000, Russian FederationLaboratory of BioinformaticsScientific Research Institute for Physical-Chemical Medicine, Building 1a, Malaya Pirogovskaya street, Moscow RF 119435, Russian FederationThe 'Russian Clinical Research Center for Gerontology'16, 1st Leonova Street, Moscow RF 192226, Russian Federation'Chronic Noncommunicable Diseases Primary Prevention in the Healthcare System' DepartmentNational Research Centre for Preventive Medicine, bld. 10, Petroverigskiy Lane, MoscowMoscow Institute of Physics and TechnologyDolgoprudny, Institusky Lane, 9 Moscow, RF, 141700, Russian FederationNational Research Centre for Preventive Medicinebld. 10, Petroverigskiy Lane, Moscow.
  3. 'Research of Age and Age-Associated Conditions' DepartmentNational Research Centre for Preventive Medicine, Building 10, Petroverigskiy Lane, Moscow RF 101000, Russian FederationLaboratory of BioinformaticsScientific Research Institute for Physical-Chemical Medicine, Building 1a, Malaya Pirogovskaya street, Moscow RF 119435, Russian FederationThe 'Russian Clinical Research Center for Gerontology'16, 1st Leonova Street, Moscow RF 192226, Russian Federation'Chronic Noncommunicable Diseases Primary Prevention in the Healthcare System' DepartmentNational Research Centre for Preventive Medicine, bld. 10, Petroverigskiy Lane, MoscowMoscow Institute of Physics and TechnologyDolgoprudny, Institusky Lane, 9 Moscow, RF, 141700, Russian FederationNational Research Centre for Preventive Medicinebld. 10, Petroverigskiy Lane, Moscow 'Research of Age and Age-Associated Conditions' DepartmentNational Research Centre for Preventive Medicine, Building 10, Petroverigskiy Lane, Moscow RF 101000, Russian FederationLaboratory of BioinformaticsScientific Research Institute for Physical-Chemical Medicine, Building 1a, Malaya Pirogovskaya street, Moscow RF 119435, Russian FederationThe 'Russian Clinical Research Center for Gerontology'16, 1st Leonova Street, Moscow RF 192226, Russian Federation'Chronic Noncommunicable Diseases Primary Prevention in the Healthcare System' DepartmentNational Research Centre for Preventive Medicine, bld. 10, Petroverigskiy Lane, MoscowMoscow Institute of Physics and TechnologyDolgoprudny, Institusky Lane, 9 Moscow, RF, 141700, Russian FederationNational Research Centre for Preventive Medicinebld. 10, Petroverigskiy Lane, Moscow 'Research of Age and Age-Associated Conditions' DepartmentNational Research Centre for Preventive Medicine, Building 10, Petroverigskiy Lane, Moscow RF 101000, Russian FederationLaboratory of BioinformaticsScientific Research Institute for Physical-Chemical Medicine, Building 1a, Malaya Pirogovskaya street, Moscow RF 119435, Russian Fed

PMID: 26555712 PMCID: PMC4674628 DOI: 10.1530/EC-15-0094

Abstract

Type 2 diabetes (T2D) is a serious disease. The gut microbiota (GM) has recently been identified as a new potential risk factor in addition to well-known diabetes risk factors. To investigate the GM composition in association with the dietary patterns in patients with different glucose tolerance, we analyzed 92 patients: with normal glucose tolerance (n=48), prediabetes (preD, n=24), and T2D (n=20). Metagenomic analysis was performed using 16S rRNA sequencing. The diet has been studied by a frequency method with a quantitative evaluation of food intake using a computer program. Microbiota in the samples was predominantly represented by Firmicutes, in a less degree by Bacteroidetes. Blautia was a dominant genus in all samples. The representation of Blautia, Serratia was lower in preD than in T2D patients, and even lower in those with normal glucose tolerance. After the clustering of the samples into groups according to the percentage of protein, fat, carbohydrates in the diet, the representation of the Bacteroides turned to be lower and Prevotella abundance turned to be higher in carbohydrate cluster. There were more patients with insulin resistance, T2D in the fat-protein cluster. Using the Calinski-Harabasz index identified the samples with more similar diets. It was discovered that half of the patients with a high-fat diet had normal tolerance, the others had T2D. The regression analysis showed that these T2D patients also had a higher representation of Blautia. Our study provides the further evidence concerning the structural modulation of the GM in the T2DM pathogenesis depending on the dietary patterns.

© 2015 The authors.

Keywords: 16S rRNA; dietary patterns; gut microbiota; impaired glucose metabolism; insulin resistance; type 2 diabetes

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