Consumption of kefir made with traditional microorganisms resulted in greater improvements in LDL cholesterol and plasma markers of inflammation in males when compared to a commercial kefir: a randomized pilot study
Bibliographic record
Abstract
Kefir has long been associated with health benefits; however, recent evidence suggests that these benefits are dependent on the specific microbial composition of the kefir consumed. This study aimed to compare how consumption of a commercial kefir without traditional kefir organisms and a pitched kefir containing traditional organisms affected plasma lipid levels, glucose homeostasis, and markers of endothelial function and inflammation in males with elevated LDL cholesterol. We utilized a crossover design in n = 21 participants consisting of two treatments of 4 weeks each in random order separated by a 4-week washout. Participants received either commercial kefir or pitched kefir containing traditional kefir organisms for each treatment period. Participants consumed 2 servings of kefir (350 g) per day. Plasma lipid profile, glucose, insulin, markers of endothelial function, and inflammation were measured in the fasting state before and after each treatment period. Differences within each treatment period and comparison of treatment delta values were performed using paired t tests and Wilcoxon signed-rank test, respectively. When compared to baseline, pitched kefir consumption reduced LDL-C, ICAM-1, and VCAM-1, while commercial kefir consumption increased TNF-α. Pitched kefir consumption resulted in greater reductions in IL-8, CRP, VCAM-1, and TNF-α when compared to commercial kefir consumption. These findings provide strong evidence that microbial composition is an important factor in the metabolic health benefits associated with kefir consumption. They also provide support for larger studies examining these to assess whether traditional kefir organisms are necessary to confer health benefits to individuals at risk of developing cardiovascular disease.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".