Probiotic Supplementation and Micronutrient Status in Healthy Subjects: A Systematic Review of Clinical Trials
Bibliographic record
Abstract
Micronutrient deficiencies are a worldwide public health concern. Emerging evidence supports the ability of probiotics to enhance micronutrient status, which could aid in the prevention of non-communicable disease-associated malnutrition. This systematic review evaluated evidence of the efficacy of probiotic supplementation to improve micronutrient status in healthy subjects. The authors searched for published English language peer-reviewed journal articles in PubMed, Scopus, Embase, and Google Scholar databases from inception to July 2020 using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The quality of eligible studies was assessed using the Revised Cochrane Risk-of-Bias tool (RoB)2 and Risk of Bias in Non-Randomized Studies of Interventions tool (ROBINS-I tool). Fourteen original studies out of 2790 met the inclusion criteria. The results indicated that, despite varying degrees of efficacy, the intake of certain probiotics in healthy subjects was associated with a positive impact on the status of certain micronutrients (vitamin B12, calcium, folate, iron and zinc). A limitation was that studies were widely heterogeneous in terms of participant age, probiotic strain, species, dosage, intervention duration, and form of administration. Additional clinical trials are warranted to determine the most effective strains of probiotics, doses and durations of interventions.
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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 | 0.013 | 0.046 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.010 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".