Gut microbiota and diet in patients with different glucose tolerance
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...
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Published in | Endocrine Connections Vol. 5; no. 1; pp. 1 - 9 |
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Main Authors | , , , , , , , , , , |
Format | Journal Article |
Language | English |
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Bioscientifica Ltd
01.01.2016
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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. |
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AbstractList | 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. 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. |
Author | Kashtanova, Daria Egshatyan, Lilit Popenko, Anna Alexeev, Dmitry Karamnova, Natalia Kostryukova, Elena Tyakht, Alexander Vakhitova, Maria Tkacheva, Olga Babenko, Vladislav Boytsov, Sergey |
AuthorAffiliation | 3 The ‘Russian Clinical Research Center for Gerontology’ , 16, 1st Leonova Street, Moscow, RF 192226 , Russian Federation 5 Moscow Institute of Physics and Technology , Dolgoprudny, Institusky Lane, 9 Moscow, RF 141700 , Russian Federation 4 ‘Chronic noncommunicable Diseases Primary Prevention in the Healthcare System’ Department, National Research Centre for Preventive Medicine , bld. 10, Petroverigskiy Lane, Moscow 1 ‘Research of Age and Age-Associated Conditions’ Department, National Research Centre for Preventive Medicine , Building 10, Petroverigskiy Lane, Moscow, RF 101000 , Russian Federation 2 Laboratory of Bioinformatics, Scientific Research Institute for Physical-Chemical Medicine , Building 1a, Malaya Pirogovskaya street, Moscow, RF 119435 , Russian Federation 6 National Research Centre for Preventive Medicine , bld. 10, Petroverigskiy Lane, Moscow |
AuthorAffiliation_xml | – name: 3 The ‘Russian Clinical Research Center for Gerontology’ , 16, 1st Leonova Street, Moscow, RF 192226 , Russian Federation – name: 2 Laboratory of Bioinformatics, Scientific Research Institute for Physical-Chemical Medicine , Building 1a, Malaya Pirogovskaya street, Moscow, RF 119435 , Russian Federation – name: 6 National Research Centre for Preventive Medicine , bld. 10, Petroverigskiy Lane, Moscow – name: 1 ‘Research of Age and Age-Associated Conditions’ Department, National Research Centre for Preventive Medicine , Building 10, Petroverigskiy Lane, Moscow, RF 101000 , Russian Federation – name: 4 ‘Chronic noncommunicable Diseases Primary Prevention in the Healthcare System’ Department, National Research Centre for Preventive Medicine , bld. 10, Petroverigskiy Lane, Moscow – name: 5 Moscow Institute of Physics and Technology , Dolgoprudny, Institusky Lane, 9 Moscow, RF 141700 , Russian Federation |
Author_xml | – sequence: 1 givenname: Lilit surname: Egshatyan fullname: Egshatyan, Lilit email: lilit.egshatyan@yandex.ru – sequence: 2 givenname: Daria surname: Kashtanova fullname: Kashtanova, Daria – sequence: 3 givenname: Anna surname: Popenko fullname: Popenko, Anna – sequence: 4 givenname: Olga surname: Tkacheva fullname: Tkacheva, Olga – sequence: 5 givenname: Alexander surname: Tyakht fullname: Tyakht, Alexander – sequence: 6 givenname: Dmitry surname: Alexeev fullname: Alexeev, Dmitry – sequence: 7 givenname: Natalia surname: Karamnova fullname: Karamnova, Natalia – sequence: 8 givenname: Elena surname: Kostryukova fullname: Kostryukova, Elena – sequence: 9 givenname: Vladislav surname: Babenko fullname: Babenko, Vladislav – sequence: 10 givenname: Maria surname: Vakhitova fullname: Vakhitova, Maria – sequence: 11 givenname: Sergey surname: Boytsov fullname: Boytsov, Sergey |
BackLink | https://www.ncbi.nlm.nih.gov/pubmed/26555712$$D View this record in MEDLINE/PubMed |
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