Association between adherence to plant-based dietary patterns and obesity risk: a systematic review of prospective cohort studies
Bibliographic record
Abstract
The worldwide prevalence of obesity and its comorbidities is staggering, and elevated body mass index represents a leading risk factor of death globally. Consistent evidence demonstrates a high-quality plant-based diet as an effective intervention for weight management, although it may be particularly challenging to adopt in its entirety for habitual meat consumers or individuals with especially poor-quality diets. Plant-based diets are increasingly studied using indices such as the overall plant-based diet index (PDI), healthful PDI, and unhealthful PDI, which offer more flexibility than a binary classification of vegetarianism and better facilitate translation into dietary recommendations. We summarized these recently accumulated studies to comprehensively evaluate plant-based diets in relation to obesity risk. We searched Medline, Embase, and CINAHL databases through January 2022 and identified 9 prospective adult cohorts. Reporting of results was consistent with Preferred Reporting Items for Systematic Review and Meta-Analyses guidelines and certainty of the evidence was assessed using domains from GRADE. The PDI had a protective association with body weight gain and adiposity. Emphasis of healthful plant foods strengthened this association and emphasis of unhealthful plant foods demonstrated either a positive or null association. The certainty of the evidence was considered moderate. These findings have wide application to inform dietary interventions and sustainable policy recommendations. (Prospero ID: CRD42020198143).
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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 | 0.009 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.007 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".