Current evidence on the association of the metabolic syndrome and dietary patterns in a global perspective
Bibliographic record
Abstract
The metabolic syndrome (MetS) is a key indicator of two main causes of death worldwide: CVD and diabetes. The present paper aimed to perform a review of the population-based research on the association of dietary patterns and the MetS in terms of methodology and findings. For the purpose of the present study, a scoping literature review was conducted using MEDLINE and EMBASE databases and hand searching in Google Scholar. Thirty-nine population-based studies were selected. Most of these studies used the factor analysis method and the a priori dietary approach, which had been initially extracted via a posteriori methods such as using the Mediterranean dietary pattern. The main finding was that following the Mediterranean or similar 'healthy' pattern reduced risk of the MetS, while following a 'Western' pattern increased risk of the MetS. The methodological approach in determining the dietary pattern of a population, whether a priori or a posteriori, should be chosen based on the purpose of the research. Overall, evidence suggests a diet based on the components of the Mediterranean diet and the avoidance of the 'Western' diet can aid in preventing the MetS.
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".