Dietary and health biomarkers—time for an update
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In the dietary and health research area, biomarkers are extensively used for multiple purposes. These include biomarkers of dietary intake and nutrient status, biomarkers used to measure the biological effects of specific dietary components, and biomarkers to assess the effects of diet on health. The implementation of biomarkers in nutritional research will be important to improve measurements of dietary intake, exposure to specific dietary components, and of compliance to dietary interventions. Biomarkers could also help with improved characterization of nutritional status in study volunteers and to provide much mechanistic insight into the effects of food components and diets. Although hundreds of papers in nutrition are published annually, there is no current ontology for the area, no generally accepted classification terminology for biomarkers in nutrition and health, no systematic validation scheme for these biomarker classes, and no recent systematic review of all proposed biomarkers for food intake. While advanced databases exist for the human and food metabolomes, additional tools are needed to curate and evaluate current data on dietary and health biomarkers. The Food Biomarkers Alliance (FoodBAll) under the Joint Programming Initiative-A Healthy Diet for a Healthy Life (JPI-HDHL)-is aimed at meeting some of these challenges, identifying new dietary biomarkers, and producing new databases and review papers on biomarkers for nutritional research. This current paper outlines the needs and serves as an introduction to this thematic issue of Genes & Nutrition on dietary and health biomarkers.
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.
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 | 0.001 | 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 it