Multifunctional dietary approach reduces intestinal inflammation in relation with changes in gut microbiota composition in subjects at cardiometabolic risk: the SINFONI project
Bibliographic record
Abstract
The development of cardiometabolic (CM) diseases is associated with chronic low-grade inflammation, partly linked to alterations of the gut microbiota (GM) and reduced intestinal integrity. The SINFONI project investigates a multifunctional (MF) nutritional strategy’s impact combining different bioactive compounds on inflammation, GM modulation and CM profile. In this randomized crossover-controlled study, 30 subjects at CM-risk consumed MF cereal-products, enriched with polyphenols, fibers, slowly-digestible starch, omega-3 fatty acids or Control cereal-products (without bioactive compounds) for 2 months. Metabolic endotoxemia (lipopolysaccharide (LPS), lipopolysaccharide-binding protein over soluble cluster of differentiation-14 (LBP/sCD14), systemic inflammation and cardiovascular risk markers, intestinal inflammation, CM profile and response to a one-week fructose supplementation, were assessed at fasting and post mixed-meal. GM composition and metabolomic analysis were conducted. Mixed linear models were employed, integrating time (pre/post), treatment (MF/control), and sequence/period. Compared to control, MF intervention reduced intestinal inflammation (fecal calprotectin, p = 0.007) and endotoxemia (fasting LPS, p < 0.05), without alteration of systemic inflammation. MF decreased serum branched-chain amino acids compared to control (p < 0.05) and increased B.ovatus, B.uniformis, A.butyriciproducens and unclassified Christensenellaceae.CAG-74 (p < 0.05). CM markers were unchanged. A 2-month dietary intervention combining multiple bioactive compounds improved intestinal inflammation and induced GM modulation. Such strategy appears as an effective strategy to target low-grade inflammation through multi-target approach.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".