Dietary biomarkers of ultra-processed foods: A narrative review
Bibliographic record
Abstract
<h2>Summary</h2> The rapidly increasing prevalence of ultra-processed foods (UPFs) in global diets necessitates a more comprehensive understanding of this food group's effects on health and disease. Nutritional biomarkers are critical for developing this understanding, as they provide an objective assessment of the body's response to UPF intake. Here, we critically assessed the findings of five studies-all of which were identified via a systematic search of the literature using stringent criteria and were focused on the biomarkers of UPFs. Based on the data extracted, we categorized UPF biomarkers into organic acids (including amino acids), lipids/lipid-like molecules, xenobiotic food components (specifically associated with UPFs), and other molecular compounds (dietary oxysterols, nucleotides, proteins, etc). These findings emphasized the importance of future studies concerning UPF and food processing techniques, while providing a succinct summary of the current biomarkers of UPFs in relevant literature.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".