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Record W4310806176 · doi:10.3389/fnut.2022.992682

Intake of slow-digesting carbohydrates is related to changes in the microbiome and its functional pathways in growing rats with obesity induced by diet

2022· article· en· W4310806176 on OpenAlexaff
Julio Plaza‐Díaz, Manuel Manzano, Francisco Javier Ruiz‐Ojeda, María D. Girón, Rafael Salto, José M. López-Pedrosa, A. Santos-Fandila, Maria Teresa Garcia-Corcoles, Ricardo Rueda, Ángel Gil

Bibliographic record

VenueFrontiers in Nutrition · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGut microbiota and health
Canadian institutionsChildren's Hospital of Eastern Ontario
FundersUniversidad de GranadaFundación Ramón ArecesAbbott Laboratories
KeywordsBiologyMetagenomicsGut floraFood scienceMicrobiomeDigestion (alchemy)ObesityFood groupPrebioticBiochemistryEndocrinologyBioinformaticsMedicineChemistryGene

Abstract

fetched live from OpenAlex

Introduction The main cause of insulin resistance in childhood is obesity, which contributes to future comorbidities as in adults. Although high-calorie diets and lack of exercise contribute to metabolic disease development, food quality rather than the quantity of macronutrients is more important than food density. The purpose of the present study was to examine the effects of changing the quality of carbohydrates from rapidly to slowly digestible carbohydrates on the composition of the gut microbiota and the profiles of the functional pathways in growing rats with obesity due to a high-fat diet (HFD). Methods During the course of 4 weeks, rats growing on an HFD-containing carbohydrates with different digestive rates were fed either HFD-containing carbohydrates with a rapid digestion rate (OBE group) or HFD-containing carbohydrates with a slow digestion rate (OBE-ISR group). A non-obese group (NOB) was included as a reference, and rats were fed on a rodent standard diet (AIN93G). An analysis of gut microbiota was conducted using 16S rRNA-based metagenomics; a linear mixed-effects model (LMM) was used to determine changes in abundance between baseline and 4 weeks of treatment, and functional pathways were identified. Gut microbiota composition at bacterial diversity and relative abundance, at phylum and genus levels, and functional profiles were analyzed by integrating the Integrated Microbial Genomes (IMG) database. Results The groups showed comparable gut microbiota at baseline. At the end of the treatment, animals from the ISR group exhibited differences at the phylum levels by decreasing the diversity of Fisher’s index and Firmicutes (newly named as Bacillota ), and increasing the Pielou’s evenness and Bacteroidetes (newly named as Bacteroidota ); at the genus level by increasing Alistipes, Bifidobacterium , Bacteroides , Butyricimonas , Lachnoclostridium , Flavonifractor , Ruminiclostridium 5 , and Faecalibaculum and decreasing Muribaculum , Blautia , and Ruminiclostridium 9 . Remarkably, relative abundances of genera Tyzzerella and Angelakisella were higher in the OBE group compared to NOB and OBE-ISR groups. In addition, some microbiota carbohydrate metabolism pathways such as glycolysis, glucuronic acid degradation, pentose phosphate pathway, methanogenesis, and fatty acid biosynthesis exhibited increased activity in the OBE-ISR group after the treatment. Higher levels of acetate and propionate were found in the feces of the ISR group compared with the NOB and OBE groups. Conclusion The results of this study demonstrate that replacing rapidly digestible carbohydrates with slowly digestible carbohydrates within an HFD improve the composition of the gut microbiota. Consequently, metabolic disturbances associated with obesity may be prevented.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.088
Threshold uncertainty score0.348

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.011
GPT teacher head0.210
Teacher spread0.199 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations26
Published2022
Admission routes1
Has abstractyes

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