Navy Bean Supplementation in Established High-Fat Diet-Induced Obesity Attenuates the Severity of the Obese Inflammatory Phenotype
Bibliographic record
Abstract
Cooked common beans (Phaseolus vulgaris) improve intestinal health in lean mice and attenuate intestinal dysbiosis and inflammation when consumed concurrent with obesity development. We determined the effects of a high-fat (HF) bean supplemented diet in mice with established obesity (induced by 12 weeks of HF diet (60% fat as kcal)) compared to obese mice consuming a HF or low-fat (LF) weight loss control diet. Obese C57BL/6 male mice remained consuming HF for eight weeks or were randomly switched from HF to an isocaloric HF with 15.7% cooked navy bean powder diet (HF→HFB) or LF (11% fat as kcal; HF→LF) (n = 12/group). HF→HFB improved the obese phenotype, including (i) fecal microbiome (increased Prevotella, Akkermansia muciniphila, and short-chain fatty acid levels), (ii) intestinal health (increased ZO-1, claudin-2, Muc2, Relmβ, and Reg3γ expression), and (iii) reduced adipose tissue (AT) inflammatory proteins (NFκBp65, STAT3, IL-6, MCP-1, and MIP-1α), versus HF (p < 0.05). Conversely, HF→LF reduced body weight and circulating hormones (leptin, resistin, and PAI-1) versus HF and HF→HFB (p < 0.05); however, AT inflammation and intestinal health markers were not improved to the same degree as HF→HFB (p < 0.05). Despite remaining on a HF obesogenic diet, introducing beans in established obesity improved the obese phenotype (intestinal health and adipose inflammation) more substantially than weight loss alone.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".