Perspective: Striking a Balance between Planetary and Human Health—Is There a Path Forward?
Bibliographic record
Abstract
The global adoption of predominantly plant-based, sustainable, healthy diets will help reduce the risk of obesity- and malnutrition-related noncommunicable diseases while protecting the future health of our planet. This review examines the benefits and limitations of different types of plant-based diets in terms of health and nutrition, affordability and accessibility, cultural (ethical and religious) acceptability, and the environment (i.e., the 4 pillars underlying sustainable healthy diets). Results suggest that, without professional supervision, traditional plant-based diets (vegan, vegetarian, and pescatarian diets) can increase the risk of nutritional deficiencies among infants, children/adolescents, women, pregnant/lactating women, and the elderly. In contrast, flexitarian diets and territorial diversified diets (TDDs; e.g., Mediterranean and New Nordic diets) that include large quantities of plant-sourced foods, low amounts of red meat, and moderate amounts of poultry, fish, eggs, and dairy can meet the energy and nutrition needs of different populations without the need for dietary education or supplementation. Compared with vegan, vegetarian, and pescatarian diets, more diverse flexitarian diets and TDDs are associated with reduced volumes of food waste and may be more acceptable and easier to maintain for people who previously followed Western diets. Although flexitarian diets and TDDs have a greater impact on the environment than vegan, vegetarian, and pescatarian diets, the negative effects are considerably reduced compared with Western diets, especially if diets include locally sourced seasonal foods. Further studies are required to define more precisely optimal sustainable healthy diets for different populations and to ensure that diets are affordable and accessible to people in all countries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".