Dietary Flaxseed and Flaxseed Oil Differentially Modulate Aspects of the Microbiota Gut–Brain Axis Following an Acute Lipopolysaccharide Challenge in Male C57Bl/6 Mice
Bibliographic record
Abstract
The microbiota gut–brain axis (mGBA) is an important contributor to mental health and neurological and mood disorders. Lipopolysaccharides (LPS) are endotoxins that are components of Gram-negative bacteria cell walls and have been widely shown to induce both systemic and neuro-inflammation. Flaxseed (Linum usitatissimum) is an oilseed rich in fibre, n3-poly-unsaturated fatty acid (alpha-linolenic acid (ALA)), and lignan, secoisolariciresinol diglucoside, which all can induce beneficial effects across varying aspects of the mGBA. The objective of this study was to determine the potential for dietary supplementation with flaxseed or flaxseed oil to attenuate LPS-induced inflammation through modulation of the mGBA. In this study, 72 5-week-old male C57Bl/6 mice were fed one of three isocaloric diets for 3 weeks: (1) AIN-93G basal diet (BD), (2) BD + 10% flaxseed (FS), or (3) BD + 4% FS oil (FO). Mice were then injected with LPS (1 mg/kg i.p) or saline (n = 12/group) and samples were collected 24 h post-injection. Dietary supplementation with FS, but not FO, partially attenuated LPS-induced systemic (serum TNF-α and IL-10) and neuro-inflammation (hippocampal and/or medial prefrontal cortex IL-10, TNF-α, IL-1β mRNA expression), but had no effect on sickness and nest-building behaviours. FS-fed mice had enhanced fecal microbial diversity with increased relative abundance of beneficial microbial groups (i.e., Lachnospiraceae, Bifidobacterium, Coriobacteriaceae), reduced Akkermansia muciniphila, and increased production of short-chain fatty acids (SCFAs), which may play a role in its anti-inflammatory response. Overall, this study highlights the potential for flaxseed to attenuate LPS-induced inflammation, in part through modulation of the intestinal microbiota, an effect which may not be solely driven by its ALA-rich oil component.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".