Applications of Metabolomics to Precision Nutrition
Bibliographic record
Abstract
BACKGROUND: For thousands of years, disabilities due to nutrient deficiencies have plagued humanity. Rickets, scurvy, anemia, stunted growth, blindness, and mental handicaps due to nutrient deficiencies affected up to 1/10 of the world's population prior to 1900. The discovery of essential amino acids, vitamins, and minerals, in the early 1900s, led to a fundamental change in our understanding of food and a revolution in human health. Widespread vitamin and mineral supplementation, the development of recommended dietary allowances, and the implementation of food labeling and testing along with significant improvements in food production and food quality have meant that nutrient-related disorders have almost vanished in the developed world. The success of nutritional science in preventing disease at a population-wide level is one of the great scientific triumphs of the 20th century. The challenge for nutritional science in the 21st century is to understand how to use nutrients and other food constituents to enhance human health or prevent disease at a more personal level. This is the primary goal of precision nutrition. SUMMARY: Precision nutrition is an emerging branch of nutrition science that aims to use modern omics technologies (genomics, proteomics, and metabolomics) to assess an individual's response to specific foods or dietary patterns and thereby determine the most effective diet or lifestyle interventions to prevent or treat specific diseases in that individual. Metabolomics is vital to nearly every aspect of precision nutrition. It can be used to comprehensively characterize the thousands of chemicals in foods, to identify food byproducts in human biofluids or tissues, to characterize nutrient deficiencies or excesses, to monitor biochemical responses to dietary interventions, to track long-term or short-term dietary habits, and to guide the development of nutritional therapies. In this review, we will describe how metabolomics has been used to advance the field of precision nutrition by providing some notable examples or use cases. First, we will describe how metabolomics helped launch the field of precision nutrition through the diagnosis and dietary therapy of individuals with inborn errors of metabolism. Next, we will describe how metabolomics is being used to comprehensively characterize the full chemical complexity of many key foods, and how this is revealing much more about nutrients than ever imagined. Third, we will describe how metabolomics is being used to identify food consumption biomarkers and how this opens the door to a more objective and quantitative assessments of an individual's diet and their response to certain foods. Finally, we will describe how metabolomics is being coupled with other omics technologies to develop custom diets and lifestyle interventions that are leading to positive health benefits. Key Message: Metabolomics is vital to the advancement of nutritional science and in making the dream of precision nutrition a reality.
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".