Is obesity related to the type of dietary fatty acids? An ecological study
Bibliographic record
Abstract
BACKGROUND: Animal studies and a few clinical trials lend credibility to the hypothesis that not all types of fatty acids carry the same potential for weight gain. Only a few epidemiological studies concerning this issue are currently available and results are conflicting. AIM: The purpose of the present ecological study was to test the existence of an association between obesity prevalence and the types of fat available in 168 countries. METHODS: Data on the prevalence of obesity (BMI >or= 30 kg/m2) for women over 15 years of age were obtained from the WHO Global InfoBase. Food balance sheets for the years 1998 to 2002 were obtained from the FAOSTAT database. Five-year means for energy, total fat, MUFA, PUFA, SFA and 'other fat' per capita were calculated, with their standard deviations, for each country. Bivariate correlations and a multiple linear regression model were used to test for the association between prevalence of obesity and types of fat available in these countries. RESULTS: Not surprisingly, dietary energy supply, SFA, PUFA and 'other fat' were positively associated with the prevalence of obesity. We also found, however, a strong negative association between MUFA availability and obesity prevalence (beta= -0.68, P<0.0001). CONCLUSION: Populations with a lower prevalence of obesity seem to consume a greater amount of MUFA. Considering the partial correlations between variables, our results suggest that in countries with higher obesity prevalence, it is the shift from MUFA to PUFA that particularly appears to be associated with the risk of obesity.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".