Plant and Animal-Based Dietary Patterns and Cardiometabolic Diseases in the Brazilian Population: Cross-Sectional Analysis of the Brazilian National Health Survey
Bibliographic record
Abstract
Background: Brazil’s dietary patterns and significant socioeconomic and geographic diversity present unique challenges for the prevention of cardiometabolic diseases. Methods: In this cross-sectional study, we analyzed data from a nationwide representative survey to understand how dietary patterns related to cardiometabolic diseases. We classified the dietary pattern of participants as whole plant-based, processed plant-based, and animal-based. Then, they were categorized into high, intermediate, and low consumption. Logistic regression analysis was used to test the prevalence of obesity, hypertension, hypercholesterolemia, diabetes, stroke, and heart diseases according to the level of intake of each of the three dietary patterns. Results: Compared to the low intake of a whole plant-based dietary pattern, a high intake was associated with a lower prevalence of obesity (OR 0.64; 95% CI 0.54, 0.75) and hypercholesterolemia (OR 0.69, 95% CI 0.56, 0.85). A processed plant-based dietary pattern (including items such as soda and sweets) was inversely associated with the prevalence of obesity (OR 0.90; 95% CI 0.83, 0.97), hypertension (OR 0.82; 95% CI 0.76, 0.88), hypercholesterolemia (OR 0.81; 95% CI 0.74, 0.88), and diabetes (OR 0.53; 95% CI 0.48, 0.59). A high intake of animal-based dietary patterns was associated with a lower prevalence of heart diseases (OR: 0.60; 95% CI 0.40, 0.90). Conclusions: In this cross-sectional analysis, greater adherence to specific dietary patterns was associated with differences in the prevalence of cardiometabolic conditions. However, causality cannot be established, and longitudinal studies are warranted to confirm these findings.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".