Making Sense of the Relationship Between Ultra-Processed Foods, Obesity, and Other Chronic Diseases
Bibliographic record
Abstract
Ultra-processed foods (UPFs) is a food category within the NOVA system. The key feature of UPFs are foods that have been highly processed and contain various additives, especially those that are industrially produced. It is claimed that UPFs are inherently unhealthy. The classification system is highly controversial. This paper critically evaluates the evidence. In stark contrast to conventional systems for food classification, the NOVA system disregards the nutritional values of foods. As a result, many foods generally considered to be healthy are included as UPFs, whereas many unhealthy foods are excluded. Epidemiological studies, mainly prospective cohort studies, have consistently reported an association between the intake of UPFs and risk of obesity, cardiovascular disease (CVD), type 2 diabetes, common mental disorders (especially depression), and all-cause mortality. A similar association has been reported for cancer and hypertension, but the supporting evidence is weaker. The most plausible explanation for this is that the associations are largely due to a limited number of unhealthy foods, such as processed meat and sugar-sweetened beverages. Studies of the relationship between UPFs and obesity present a different picture. There is much evidence that suggests that UPFs play a major causal role in obesity. The high contents of fat, sugar, carbohydrates, and sodium commonly present in UPFs makes these foods hyperpalatable. In addition, UPFs typically have a high energy density. As a result of these two features of UPFs, most people consume an excessive energy intake when presented with UPFs. Because UPFs include a wide range of foods, many of which are healthy, it is likely that while many UPFs are obesogenic, many others are not.
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 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 | 0.018 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".