Examining the associations between <i>a posteriori</i> dietary patterns and obesity indexes: Systematic review of observational studies
Bibliographic record
Abstract
Background: Few reports on the association of population-derived dietary patterns with excess body weight, using the approach of a systematic review currently exist. Aim: The aim of the present systematic review was to identify dietary patterns associated with body mass index (BMI), body weight (BW)/BMI change, weight status and weight loss maintenance status. Methods: Using MEDLINE (via PubMed) and EBSCO Host databases, we systematically reviewed studies from 1980 to 2020, which included men and women, aged ≥18 years. Primary outcome was BMI or the longitudinal change of individuals’ BW or BMI, or weight status (normal weight/overweight/obesity) or weight loss maintenance status. We included observational studies, with or without a prospective design. Studies which met the inclusion criteria were evaluated based on the Newcastle–Ottawa Scale; only a posteriori dietary patterns were evaluated. Results: Twenty-six studies were eligible for inclusion in the current analysis. The results indicate a relationship between adherence to a lacto-vegetarian dietary pattern, characterized by high intake of fruits, vegetables, whole grains, unprocessed cereals, dairy and low intake of high-fat foods and sweets, and longitudinal change of individuals’ BW or BMI, or the risk of overweight/obesity. Conclusions: Promotion of this healthy dietary pattern, as an alternative to focusing on specific nutrients or foods, may be a promising approach to be included in future long-term weight maintenance interventions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.022 | 0.099 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.010 |
| Bibliometrics | 0.015 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".