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Record W4407040147 · doi:10.3390/nu17030525

Assessing the Roles of Retinol, Vitamin K2, Carnitine, and Creatine in Plant-Based Diets: A Narrative Review of Nutritional Adequacy and Health Implications

2025· review· en· W4407040147 on OpenAlexaff
David Goldman, Cassandra Warbeck, Robby Barbaro, Cyrus Khambatta, Matthew Nagra

Bibliographic record

VenueNutrients · 2025
Typereview
Languageen
FieldNursing
TopicVitamin K Research Studies
Canadian institutionsUniversity of British ColumbiaUniversity of Alberta
Fundersnot available
KeywordsCreatineVitaminEssential nutrientRetinolCarnitineNutrientCarotenoidBiologyVitamin D and neurologyPhysiologyMedicineEnvironmental healthFood scienceEndocrinologyEcology

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.428
Threshold uncertainty score0.967

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.077
GPT teacher head0.449
Teacher spread0.372 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations6
Published2025
Admission routes1
Has abstractyes

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