Assessing the Roles of Retinol, Vitamin K2, Carnitine, and Creatine in Plant-Based Diets: A Narrative Review of Nutritional Adequacy and Health Implications
Bibliographic record
Abstract
Plant-based diets are associated with numerous health benefits, including reduced risks of chronic diseases. However, questions persist regarding the implications of lower dietary intakes of certain non-essential nutrients, such as retinol, vitamin K2, carnitine, and creatine, which are primarily found in animal-derived foods. This narrative review evaluates the roles of these nutrients in human physiology and examines whether their absence in plant-based diets is likely to impact health outcomes. Retinol requirements can be met through the consumption of provitamin A carotenoids in plant foods, even in individuals with reduced conversion efficiency. Endogenous synthesis adequately supports physiological needs for vitamin K2, and currently available evidence does not consistently demonstrate that dietary vitamin K2 provides additional benefits for bone or cardiovascular health. Carnitine and creatine levels may differ between individuals following omnivorous and plant-based diets, but these differences do not result in compromised muscle function, cognitive health, or metabolic outcomes. Current evidence does not indicate that the absence of these non-essential nutrients in plant-based diets adversely affects health or confers disadvantages compared to omnivorous diets.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".