IMPACT OF PLANT BASED DIETS ON INFLAMMATORY MARKERS IN ADULTS A SYSTEMATIC REVIEW
Bibliographic record
Abstract
Background: Chronic low-grade inflammation is a key driver in the development of cardiometabolic and other non-communicable diseases. Diet, particularly plant-based eating patterns, has emerged as a modifiable factor influencing systemic inflammation. Although individual studies have investigated the impact of plant-based diets on inflammatory biomarkers, findings remain inconsistent due to variations in study design, population, and dietary assessment methods. A comprehensive synthesis of current evidence is needed to clarify the relationship and guide clinical nutrition strategies. Objective: This systematic review aims to evaluate the effects of plant-based dietary patterns on inflammatory biomarkers, specifically C-reactive protein (CRP), interleukin-6 (IL-6), and tumor necrosis factor-alpha (TNF-α), in adult populations. Methods: A systematic review was conducted following PRISMA guidelines. Electronic databases including PubMed, Scopus, Web of Science, and the Cochrane Library were searched for studies published between 2018 and 2024. Eligible studies included randomized controlled trials and observational studies examining the association between plant-based diets and inflammatory markers in adults. Two independent reviewers screened and selected studies, extracted data, and assessed risk of bias using the Cochrane Risk of Bias Tool and Newcastle-Ottawa Scale. Due to heterogeneity, a qualitative synthesis was performed. Results: Eight studies met the inclusion criteria, comprising four randomized controlled trials and four observational studies with a total sample size exceeding 1,800 participants. The majority of studies reported significant reductions in CRP, IL-6, and TNF-α levels among individuals adhering to plant-based diets compared to omnivorous or conventional diets (p < 0.05). Risk of bias was generally low to moderate, with consistent findings across study designs. Conclusion: Plant-based diets are associated with favorable reductions in systemic inflammatory biomarkers in adults, supporting their potential as a non-pharmacological strategy for reducing inflammation. However, further large-scale, long-term randomized trials are necessary to establish causality and explore underlying mechanisms.
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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 | 0.008 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.008 | 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".