Consumption of the cell-free or heat-treated fractions of a pitched kefir confers some but not all positive impacts of the corresponding whole kefir
Bibliographic record
Abstract
Introduction: Kefir consumption can have many metabolic health benefits, including, in the case of specific kefirs, improvements in plasma and liver lipid profiles. Our group has previously shown that these health benefits are dependent on the microbial composition of the kefir fermentation, and that a pitched kefir (PK1) containing specific traditional microbes can recapitulate the health benefits of a traditional kefir. In this study we investigated how different preparations of kefir impact cholesterol and lipid metabolism and circulating markers of cardiovascular disease risk and determine if freeze-drying impacts health benefits relative to past studies. Materials and methods: Eight-week-old male and female C57Bl/6 mice were fed a high fat diet (40% kcal from fat) supplemented with one of 3 freeze-dried kefir preparations (whole kefir, cell-free kefir, or heat-treated kefir) for 8 weeks prior to analysis of plasma and liver lipid profiles, circulating cardiovascular disease (CVD) biomarkers, cecal microbiome composition, and cecal short-chain fatty acid levels. These groups of mice were compared to others that were fed a control low-fat diet, control high fat diet or high fat diet supplemented with milk, respectively. Results: All kefir preparations lowered plasma cholesterol in both male and female mice, while only whole kefir lowered liver cholesterol and triglycerides. Plasma vascular cell adhesion molecule 1 (VCAM-1) was lowered by both whole kefir and heat-treated kefir in male mice but not females, while c-reactive protein (CRP) was unchanged across all high fat diet fed groups in males and females. Conclusion: These results indicate that some of the metabolic benefits of consumption of this kefir do not require whole kefir while also indicating that there are multiple compounds or components responsible for the different benefits observed.
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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) | 0.000 | 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) | 0.002 | 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".