Precision Nutrition for Cardiovascular Disease Prevention
Bibliographic record
Abstract
Background: Cardiovascular diseases (CVDs) are the leading cause of death globally, making their prevention a major challenge for modern society. For decades, treatments aimed at reducing CVD risk factors through nutritional recommendations and medications have had variable success. One of the main reasons behind this is the interindividual variability in response to drugs and nutritional interventions. The development of genomics has allowed the discovery of genetic variants influencing drug and food response, leading to more personalized treatments in the form of precision medicine and precision nutrition. The latter is founded on the principle that one diet does not fit all and the need to stratify individuals into subgroups based on their response to food and nutrients. Despite showing great promise in pushing forward the field of nutrition, health professionals have very little knowledge of precision nutrition, even though the general population is showing interest in more personalized nutritional guidance. Summary: This review aimed to provide an overview of key sources of interindividual variability observed in CVD risk factors in response to nutritional interventions. Despite some limitations, genetic testing is a mature predictive tool that should be at the forefront of tailored nutrition recommendations for CVD prevention. Although the epigenome-diet relationship shows great promise, it is still too early in its development to allow for its clinical deployment. Metabolomics has the potential to enhance genetic testing by complementing traditional self-reported dietary intake instruments as well as a very promising metabotyping method. Microbiome phenotyping, despite its complexity, provides a wealth of information on the health status of the host and its response to food and nutrients. Finally, current applications are discussed and an outline of the required steps for a successful implementation of precision nutrition in clinical practice as a tool for CVD prevention is presented. Key Messages: Precision nutrition is the cornerstone of a promising approach offering targeted nutritional recommendations for CVD prevention.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".