The rapid rise of ultra-processed foods brings up human health concerns
Bibliographic record
Abstract
• A novel NOVA classification system was introduced based on different food processing degrees • Global consumption of ultra-processed foods (UPFs) has increased dramatically • The rapid rise of UPFs brings up human health concerns • Long-term intake of UPFs leads to addiction due to the additives including fat, caffeineand sugar • Large cohort studies showed UPFs increase the risk of chronic diseases and death The global consumption of ultra-processed foods (UPFs) has surged in recent decades, driven by shifts in lifestyle, dietary patterns, and socioeconomic dynamics, with accelerated growth observed post-COVID-19 pandemic. Defined as industrially formulated ready-to-consume products, UPFs undergo extensive processing involving additives such as flavour enhancers, emulsifiers, stabilisers, and artificial pigments. This process disrupts the natural food matrix and raises significant concerns regarding long-term health implications. This review systematically analyses global UPF consumption trends across nations and critically evaluates the health risks associated with dietary additives in UPFs, with a focus on fat, sugar, and caffeine-induced addictive eating behaviours. A novel NOVA-based classification framework is proposed to categorise foods by processing intensity, complemented by comparative analysis of global consumption data. Furthermore, we syntheze evidence from eight longitudinal cohort studies encompassing 522,682 participants to elucidate correlations between UPF intake and elevated incidence rates of obesity, cardiometabolic disorders (cardiovascular disease and type 2 diabetes), functional gastrointestinal syndromes, and specific cancers. These findings provide critical insights for public health initiatives and food industry practices, advocating for precision in food safety regulation and processing technology optimization.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".