Association between ultra-processed foods consumption and micronutrient intake and diet quality in Iranian adults: a multicentric study
Bibliographic record
Abstract
OBJECTIVE: To identify ultra-processed foods (UPF) contribution to daily energy and nutrient intake in Iranians and examine whether UPF intake is associated with nutrient profile and diet quality. DESIGN: In this cross-sectional study, a validated FFQ was used to evaluate usual dietary intake over the preceding year. NOVA system was applied to categorise foods based on their levels of processing. Diet quality was evaluated using the nutrient adequacy ratio (NAR), Nutrient Rich Food Index (NRF) and hybrid nutrient density. SETTING: The LIPOKAP study conducted in five cities of Iran (Isfahan, Birjand, Bandar Abbas, Kermanshah and Shahrekord). PARTICIPANTS: A total of 1994 adults aged ≥18 years were recruited using stratified multistage random cluster sampling method. RESULTS: UPF were responsible for 8·5 % of daily energy intake. In the adjusted model, UPF consumption was inversely associated with carbohydrate, protein, refined and whole grains, fibre, fruit and meat, but was positively linked to energy, total fat, saturated and trans fatty acids and cholesterol. Compared with those in the lowest tertile, individuals in the highest tertile of UPF had smaller NAR for Ca, Mg, Zn, Fe, phosphorus, thiamin, niacin, folate and vitamin C. Both NRF and hybrid nutrient density decreased when the share of daily energy intake from UPF increased. CONCLUSION: The higher consumption of UPF is associated with poorer diet quality and lower nutrient intake. It is recommended that UPF be replaced with minimally processed foods to improve diet quality and nutrient profile.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".