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Preferred fruit and vegetable consumption and colonic microbiota in young residents of Arkhangelsk
- Authors: Kukalevskaya N.N.1, Bazhukova T.A.1, Sabanaev M.A.1, Grjibovski A.M.1,2,3
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Affiliations:
- Northern State Medical University
- Northern (Arctic) Federal University n.a. M.V. Lomonosov
- M.K. Ammosov North-Eastern Federal University
- Issue: Vol 31, No 4 (2024)
- Pages: 279-290
- Section: ORIGINAL STUDY ARTICLES
- Submitted: 27.06.2024
- Accepted: 07.10.2024
- Published: 12.12.2024
- URL: https://hum-ecol.ru/1728-0869/article/view/633894
- DOI: https://doi.org/10.17816/humeco633894
- ID: 633894
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Abstract
Background: The composition of colonic microbiota is influenced by environmental factors, including dietary habits. Several studies on dietary habits and nutrition of Arctic residents have been published, but the information on the associations between fruit and vegetable consumption and gut microbiota is scarce.
Aim: This study aimed to evaluate the impact of preferred fruit and vegetable consumption on colonic microbiota in young residents of Arkhangelsk, using a sample of students and staff from a medical university.
Material and methods: The study included 90 healthy volunteers (23 men and 67 women) from Northern State Medical University in Arkhangelsk aged 18–45 years with a normal body mass index. Fruit and vegetable consumption was assessed using a questionnaire. Stool samples were collected for molecular genetic analysis of colonic microbiota. Associations between fruit and vegetable consumption and concentrations of 33 microbiota indicators were examined using multivariable median regression, with adjustments made for age, gender, and place of origin.
Results: Vegetables and fruits were consumed daily by 43.33% and 15.56% of respondents, respectively. The most frequently consumed vegetables were tomatoes (77.78%) and cucumbers (80.0%), while only 25.00% consumed potatoes and carrots. Among fruits, apples were consumed most frequently (74.44%), followed by bananas (57.78%) and citrus fruits (41.11%). Significant associations were found between Methanobrevibacter smithii and tomatoes ( p =0.008) and carrots ( p =0.006), between Prevotella spp. and cucumbers ( p =0.032), Blautia spp. and carrots ( p =0.002) and bananas ( p =0.020). Additionally, association was found for Acinetobacter spp. with tomatoes ( p =0.036), potatoes ( p =0.028) and citrus fruits ( p =0.019), Bifidobacterium spp. with potatoes ( p =0.039) and citrus fruits ( p =0.002). Direct association was found between Bacteroides spp. and cucumbers ( p =0.023).
Conclusion: Our findings on the associations between selected fruits and vegetables and microbial concentrations may contribute to the development of personalized and balanced diet to enrich microbiota biodiversity and improve the quality of life of the residents of the North.
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Table 1. Regression coefficients and their standard errors for the associations between consumption of selected vegetables and concentrations of gut microbiota species (lg CFU/g)
Род/вид представителя микробиоты толстой кишки Genus/species of colon microbiota | Регрессионные коэффициенты и их стандартные ошибки Regression coefficients and their standard errors | |||
Томаты Tomato | Огурцы Cucumber | Морковь Carrot | Картофель Potato | |
Acinetobacter spp | 0,18 (0,08)* | 0,18 (0,09) | - | -0,18 (0,08)* |
Agathobacter rectalis | -0,05 (0,37) | -0,36 (0,40) | 0,08 (0,36) | 0,03 (0,32) |
Akkermansia muciniphila | 0 (0,80) | 1,63 (2,76) | 0 (2.69) | -3,00 (2,49) |
Bacteroides spp | -0,18 (0,26) | 0.65 (0.28)* | 0.07 (0.25) | 0,07 (0,24) |
Bacteroides thethaiotaomicron | -0,17 (1,01) | 0,47 (0,88) | -0.10 (0.89) | -0,68 (0,83) |
Bifidobacterium spp | 0.46 (0.38) | 0.13 (0.37) | 0.16 (0.33) | 0.65 (0.31)* |
Blautia spp | -0,52 (2,23) | -0,52 (2,31) | 6.85 (2.12)* | 0,21 (2,08) |
Escherichia coli | 0,12 (0,44) | 0.14 (0.45) | 0.52 (0.42) | 0,11 (0,39) |
Faecalibacterium prausnitzii | 0 (0,23) | 0,55 (0,19)* | -0.08 (0.21) | -0,18 (0,23) |
Methanobrevibacter smithii | 5,63 (2,06)* | 1,10 (2,12) | -5,54 (1,97)* | -0,52 (1,88) |
Prevotella spp | 0,89 (0,96) | -2,00 (0,92)* | -0,56 (0,97) | 0,14 (0,89) |
Roseburia inulinivorans | 0,15 (0,34) | 0,01 (0,37) | -0,07 (0,32) | -0,10 (0,31) |
Ruminococcus spp | -1.00 (1.71) | 0 (2,20) | 0 (2,04) | 0,57 (1,97) |
Streptococcus spp | 0,04 (0,44) | 0,78 (0,47) | 0,25 (0,46) | 0,23 (0,42) |
Table 2. Regression coefficients and their standard errors for the associations between consumption of selected fruits and concentrations of gut microbiota species (lg CFU/g)
Род/вид представителя микробиоты толстой кишки Genus/species of colon microbiota | Регрессионные коэффициенты и их стандартные ошибки Regression coefficients and their standard errors | ||
Бананы Bananas | Цитрусовые Citrus | Яблоки Apple | |
Acinetobacter spp | 0,00 (1,00) | 0,15 (0,06)* | 0.00 (1.00) |
Agathobacter rectalis | -0,12 (0,34) | 0,09 (0,31) | -0.60 (0.32) |
Akkermansia muciniphila | 0,00 (1,00) | -0,21 (2,34) | -3.09 (2.74) |
Bacteroides spp | -0,13 (0,91) | -0,17 (0,77) | 0.34 (0.88) |
Bacteroides thethaiotaomicron | -0,12 (0,24) | 0,05 (0,22) | 0.15 (0.25) |
Bifidobacterium spp | -0,17 (0,33) | -0,70 (0,22)* | -0.59 (0.32) |
Blautia spp | 4,77 (2,01)* | 0,00 (1,00) | 0.00 (1.00) |
Escherichia coli | -0,14 (0,36) | -0,44 (0,35) | -0.30 (0.44) |
Faecalibacterium prausnitzii | -0,15 (0,18) | -0,22 (0,16) | 0.00 (1.00) |
Methanobrevibacter smithii | -0,24 (1,81) | 0,10 (1,70) | -0.97 (2.04) |
Prevotella spp | -0,23 (0,84) | -1,30 (0,87) | -0.79 (1.04) |
Roseburia inulinivorans | 0,05 (0,30) | 0,15 (0,29) | -0.36 (0.38) |
Ruminococcus spp | -0,30 (1,90) | -0,49 (1,81) | 0.48 (2.07) |
Streptococcus spp | -0,13 (0,44) | -0,30 (0,35) | 0.02 (0.81) |
* - уровень значимости р менее 0,05
Fig.1. Frequency fruits and vegetables consumption in the study sample.
Fig.2. Frequency of consumption of selected fruits and vegetables in the study sample
About the authors
Natalia N. Kukalevskaya
Northern State Medical University
Author for correspondence.
Email: n.kukalevskaya@yandex.ru
ORCID iD: 0000-0003-3371-1485
SPIN-code: 1844-4439
Russian Federation, 51 Troitski ave., 163069 Arkhangelsk
Tatyana A. Bazhukova
Northern State Medical University
Email: tbazhukova@yandex.ru
ORCID iD: 0000-0002-7890-2341
SPIN-code: 2220-2151
Dr. Sci (Med), Professor
Russian Federation, 51 Troitski ave., 163069 ArkhangelskMichael A. Sabanaev
Northern State Medical University
Email: mix.sabanaeff@gmail.com
ORCID iD: 0000-0001-5642-3019
SPIN-code: 8585-3051
Russian Federation, 51 Troitski ave., 163069 Arkhangelsk
Andrej M. Grjibovski
Northern State Medical University; Northern (Arctic) Federal University n.a. M.V. Lomonosov; M.K. Ammosov North-Eastern Federal University
Email: a.grjibovski@yandex.ru
ORCID iD: 0000-0002-5464-0498
SPIN-code: 5118-0081
MD, MPhil, PhD
Russian Federation, 51 Troitski ave., 163069 Arkhangelsk; Arkhangelsk; YakutskReferences
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