From Rations to Reactions: A Narrative Review of Precision Nutrigenomics in Emergency, Disaster, and Critical Care Medicine
Bibliographic record
Abstract
Background: Traditional nutritional support in emergency and disaster medicine operates on a one-size-fits-all paradigm, often delayed until the hospital phase of care. This approach fails to address the profound metabolic heterogeneity of critically ill or traumatized individuals, potentially compromising recovery. Aim: This narrative review aims to synthesize current evidence and propose an integrated framework for implementing precision nutrigenomics across the continuum of crisis care, from pre-hospital settings through to rehabilitation. Methods: A systematic search of PubMed, Scopus, Web of Science, and CINAHL was conducted for literature published between 2010 and 2024. Results: The review identifies a viable pathway for personalized nutrition in crisis response, reliant on: 1) Point-of-Injury Metabolic Phenotyping via portable lab devices; 2) Genetically-Informed Formulary Development for enteral/parenteral nutrition; 3) Informatics-Enabled Dietary Data Integration into emergency protocols; and 4) Logistical Frameworks for delivering precision diets in austere or high-security environments. Key barriers include the validation of field-deployable biomarkers, cost, data interoperability, and the need for cross-disciplinary training. Conclusion: Precision nutrigenomics represents a transformative frontier in crisis medicine, with the potential to modulate immune response, reduce complications, and improve survival. Its realization requires dismantling silos between emergency responders, laboratory scientists, dietitians, and hospital logisticians to create a seamless, data-driven nutritional care pathway that begins at the moment of crisis intervention.
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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".