Association of planetary health diet indices with diet composition, nutritional quality and environmental impacts in Italian adults
Bibliographic record
Abstract
BACKGROUND AND AIMS: Sustainable diets are increasingly recognized as a key strategy to promote human health while reducing environmental impacts. The Planetary Health Diet (PHD) provides a global framework for sustainable and healthy eating patterns, but evidence on its adherence and implications in specific populations is still limited. The aim of this study was to test the level of adherence, the environmental impact, and the nutritional quality of several scores assessing the level of adherence to the PHD in a cohort of Italian individuals. METHODS AND RESULTS: Dietary habits were assessed through validated food frequency questionnaires while various scores have been applied to evaluate the level of adherence to PHD (ELD-I, EAT, PHDI-Cacau, NB-EAT, PHDI-Bui) in 1936 Italian adults, using the Mediterranean diet (MEDI-LITE) as reference. The environmental impact was quantified as carbon and water footprints (CF and WF) using the SU-EATABLE LIFE database. Higher adherence to PHD-related indices generally corresponded to healthier nutrient profiles, higher fiber intake, and better concordance with Italian dietary recommendations, although some indices predicted lower intake of certain nutrients (e.g., vitamin B12, calcium). The MEDI-LITE index consistently predicted higher adequacy across dietary and nutrient recommendations. Absolute CF and WF showed mixed trends across indices, while energy-standardized values (per 1000 kcal) indicated lower impacts for all PHD-related scores, apart from the ELD-I. Adherence to the Mediterranean diet was also associated with favorable energy-adjusted environmental outcomes. CONCLUSION: These findings reinforce the existing alignment between the intrinsic characteristics of the Mediterranean diet with both nutrition and sustainability objectives.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".