Anti-Inflammatory, Antioxidant, Metabolic and Gut Microbiota Modulation Activities of Probiotic in Cardiac Remodeling Condition: Evidence from Systematic Study and Meta-Analysis of Randomized Controlled Trials
Bibliographic record
Abstract
Heart failure (HF) is a global pandemic with increasing prevalence and mortality rates annually. Its main cause is myocardial infarction (MI), followed by rapid cardiac remodeling. Several clinical studies have shown that probiotics can improve the quality of life and reduce cardiovascular risk factors. This systematic review and meta-analysis aimed to investigate the effectiveness of probiotics in preventing HF caused by a MI according to a prospectively registered protocol (PROSPERO: CRD42023388870). Four independent evaluators independently extracted the data using predefined extraction forms and evaluated the eligibility and accuracy of the studies. A total of six studies consisting of 366 participants were included in the systematic review. Probiotics are not significant in intervening left ventricular ejection fraction (LVEF) and high-sensitivity C-reactive protein (hs-CRP) when compared between the intervention group and the control group due to inadequate studies supporting its efficacy. Among sarcopenia indexes, hand grip strength (HGS) showed robust correlations with the Wnt biomarkers (p < 0.05), improved short physical performance battery (SPPB) scores were also strongly correlated with Dickkopf-related protein (Dkk)-3, followed by Dkk-1, and sterol regulatory element-binding protein 1 (SREBP-1) (p < 0.05). The probiotic group showed improvement in total cholesterol (p = 0.01) and uric acid (p = 0.014) compared to the baseline. Finally, probiotic supplements may be an anti-inflammatory, antioxidant, metabolic, and intestinal microbiota modulator in cardiac remodeling conditions. Probiotics have great potential to attenuate cardiac remodeling in HF or post-MI patients while also enhancing the Wnt signaling pathway which can improve sarcopenia under such conditions.
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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 | 0.011 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.022 | 0.031 |
| Bibliometrics | 0.005 | 0.005 |
| 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".