Ultra-Processed Foods and Food Additives in CKD
Bibliographic record
Abstract
The term ultra-processed food (UPF) was developed almost two decades ago to define foods which have undergone sophisticated industrial processing. Many of these foods contain added sugars, unhealthy fats, salt, and food additives designed to enhance palatability, extend shelf life, and drive high sales. Owing to these convenient characteristics, the sales and consumption of UPF have consistently increased, with the tendency to replace minimally processed foods in the diet. This shift has led to a decrease in dietary quality and to an increase in the consumption of sugars, unhealthy fats, salt, and food additives, the latter raising concerns about potential toxicity. Therefore, UPF consumption has been associated with increased risk of developing noncommunicable diseases, including CKD, and may have an effect on kidney health in each CKD stage. Primary caregivers should be aware that UPF increases the risk of developing CKD. Providers of secondary and tertiary care should note that UPF contributes to the development of metabolic derangements such as metabolic acidosis, hyperkalemia, hyperphosphatemia, dyslipidemia, and dysbiosis. This review provides a summary of the main adverse effects of UPFs on general health as well as on kidney health. In addition, it gives practical guidance on how to define and assess UPF intake in research and clinical settings, with proposed strategies on how to address UPF consumption in individuals with CKD stages 3-5, aiming at reducing UPF consumption and increasing intake of minimally processed healthier food.
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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".