Effects of probiotics, prebiotics, and synbiotics on cardiometabolic risk factors in children and adolescents with overweight or obesity: a systematic review and Bayesian network meta-analysis
Bibliographic record
Abstract
The efficacy of probiotics, prebiotics, or synbiotics in children and adolescents with overweight or obesity remains uncertain. This systematic review evaluates their intervention effects through a network meta-analysis of randomized clinical trials (RCTs). Searches of 4 electronic databases until January 7, 2024, yielded 17 papers reporting on 15 RCTs involving 820 participants. Multiple-strain probiotics (MSP) showed significant efficacy in reducing BMI (Mean Difference (MD) −2.13 kg/m2, 95% credible interval (CrI) [−2.7, −1.57]), waist circumference (MD −1.34 cm, 95% CrI [−2.33, −0.35]), total cholesterol (MD −6.55 mg/dL, 95% CrI [−10.61, −2.45]), triglycerides (MD −3.71 mg/dL, 95% CrI [−5.76, −1.67]), leptin (MD −3.99 ng/mL, 95% CrI [−4.68, −3.3]), and hypersensitive C-reactive protein (Hs-CRP) (MD −1.21 mg/L, 95% CrI [−1.45, −0.97]). Synbiotics were effective in reducing BMI-z score (MD −0.07, 95% CrI [−0.10, −0.04]) and LDL-C (MD −1.54 mg/dL, 95% CrI [−1.98, −1.09]) but led to a slight increase in fasting glucose (MD 1.12 mg/dL, 95% CrI [0.75, 1.49]). Single-ingredient prebiotics and single-strain probiotics also had some beneficial effects on BMI and Hs-CRP, respectively. Moderate to low evidence suggests MSP may be a potential choice for improving BMI and reducing lipids, leptin, and Hs-CRP levels, implying that MSP could aid in managing pediatric obesity and related metabolic issues by modulating the gut microbiota. Although synbiotics show their favorable effects on body metrics and lipid control, their potential impact on blood glucose currently prevents them from being an alternative to MSP for treating pediatric obesity. Further large-scale, well-designed studies are needed to confirm these findings.
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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 | 0.016 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.032 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".