Potential reductions in ultra-processed food consumption substantially improve population cardiometabolic-related dietary nutrient profiles in eight countries
Bibliographic record
Abstract
Background and aims: The negative effect on dietary nutrient profiles is the most obvious mechanism explaining the higher risk of cardiometabolic diseases associated with increased dietary share of UPF observed in large cohort studies. We estimate the proportion of diets with excessive energy density, excessive free sugars or saturated fat contents and insufficient fiber that could be avoided, if UPF consumption was reduced to levels among lowest consumers across eight countries, as well as the proportion of diets with multiple inadequacies. Methods and results: Using nationally-representative cross-sectional surveys from Brazil (2008–09), Chile (2010), Colombia (2005), Mexico (2012), Australia (2011–12), the UK (2008–16), Canada (2015), and the US (2015–16), inadequate energy density (≥2.25 kcal/g) or contents of free sugars (>10% of total energy intake), saturated fats (>10% of total energy intake) and fiber (<25 g/2000 kcal) population attributable fractions were quantified. Substantial reductions in nutrient inadequacies would be observed ranging from 50.4% in Chile to 76.8% in US for dietary energy density, from 15.5% in Colombia to 68.4% in Australia for free sugars, from 9.5% in Canada to 35.0% in Mexico for saturated fats, and from 10.3% in UK to 37.9% in Mexico for fiber. Higher reductions would be observed for diets with multiple nutrient inadequacies: from 27.3% in UK to 77.7% in Australia for ≥3 and from 69.4% in Canada to 92.1% in US, for 4 inadequacies. Conclusions: Lowering dietary contribution of UPF to levels among country-specific lowest consumers is a way to improve population cardiometabolic-related dietary nutrient profiles.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".