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Ultra-Processed Foods May Be Linked to Increased Risk of Cancer

2023· article· en· W4323037970 on OpenAlexaboutno aff

Bibliographic record

VenueOncology Times · 2023
Typearticle
Languageen
FieldMedicine
TopicConsumer Attitudes and Food Labeling
Canadian institutionsnot available
Fundersnot available
KeywordsCancerFood scienceBusinessEnvironmental healthRisk analysis (engineering)Environmental scienceMedicineChemistryInternal medicine

Abstract

fetched live from OpenAlex

nutrition; diet: nutrition; dietResearchers from the Imperial School of Public Health in London have produced the most comprehensive assessment to date of the association between ultra-processed foods and the risk of developing cancers. Ultra-processed foods are food items heavily processed during their production, such as fizzy drinks, mass-produced packaged bread, many ready meals, and most breakfast cereals. Ultra-processed foods are often relatively cheap, convenient, and heavily marketed, often as healthy options. But these foods are also generally higher in salt, fat, and sugar, and contain artificial additives. It is now well-documented they are linked with a range of poor health outcomes, including obesity, type 2 diabetes, and cardiovascular disease. The first U.K. study of its kind used U.K. Biobank records to collect information on the diets of 200,000 middle-aged adult participants. Researchers monitored participants' health over a 10-year period, looking at the risk of developing any cancer overall as well as the specific risk of developing 34 types of cancer. They also looked at the risk of people dying from cancer. The study found that higher consumption of ultra-processed foods was associated with a greater risk of developing cancer overall, specifically with ovarian and brain cancers. It was also associated with an increased risk of dying from cancer, most notably with ovarian and breast cancers. For every 10 percent increase in ultra-processed food in a person's diet, there was an increased incidence of 2 percent for cancer overall and a 19 percent increase for ovarian cancer specifically. Each 10 percent increase in ultra-processed food consumption was also associated with increased mortality for cancer overall by 6 percent, alongside a 16 percent increase for breast cancer and a 30 percent increase for ovarian cancer. These links remained after adjusting for a range of socio-economic, behavioral, and dietary factors, such as smoking status, physical activity, and body mass index. The Imperial team carried out the study, which was published in eClinicalMedicine, in collaboration with researchers from the International Agency for Research on Cancer, University of São Paulo, and NOVA University Lisbon (2023; doi: 10.1016/j.eclinm.2023.101840). Previous research from the team reported the levels of consumption of ultra-processed foods in the U.K., which are the highest in Europe for both adults and children. The team also found that higher consumption of ultra-processed foods was associated with a greater risk of developing obesity and type 2 diabetes in adults, and a greater weight gain in children extending from childhood to young adulthood in the U.K. “This study adds to the growing evidence that ultra-processed foods are likely to negatively impact our health, including our risk for cancer,” noted Eszter Vamos, PhD, lead senior author for the study from Imperial College London's School of Public Health. “Given the high levels of consumption in U.K. adults and children, this has important implications for future health outcomes. “Although our study cannot prove causation, other available evidence shows that reducing ultra-processed foods in our diet could provide important health benefits. Further research is needed to confirm these findings and understand the best public health strategies to reduce the widespread presence and harms of ultra-processed foods in our diet,” Vamos noted. According to Kiara Chang, MSc, first author for the study, from Imperial College London's School of Public Health, “The average person in the U.K. consumes more than half of their daily energy intake from ultra-processed foods. This is exceptionally high and concerning as ultra-processed foods are produced with industrially derived ingredients and often use food additives to adjust color, flavor, consistency, texture, or extend shelf life. “Our bodies may not react the same way to these ultra-processed ingredients and additives as they do to fresh and nutritious minimally processed foods,” she stated. “However, ultra-processed foods are everywhere and highly marketed with cheap prices and attractive packaging to promote consumption. This shows our food environment needs urgent reform to protect the population from ultra-processed foods.” The World Health Organization and the United Nations' Food and Agriculture Organization have previously recommended restricting ultra-processed foods as part of a healthy sustainable diet. There are ongoing efforts to reduce ultra-processed food consumption around the world, with countries such as Brazil, France, and Canada updating their national dietary guidelines with recommendations to limit such foods. Brazil has also banned the marketing of ultra-processed foods in schools. There are currently no similar measures to tackle ultra-processed foods in the U.K. “We need clear front-of-pack warning labels for ultra-processed foods to aid consumer choices, and our sugar tax should be extended to cover ultra-processed fizzy drinks, fruit-based and milk-based drinks, as well as other ultra-processed products,” Chang noted. “Lower income households are particularly vulnerable to these cheap and unhealthy ultra-processed foods. Minimally processed and freshly prepared meals should be subsidized to ensure everyone has access to healthy, nutritious, and affordable options.” The researchers noted that their study is observational, so it does not show a causal link between ultra-processed foods and cancer due to the observational nature of the research. More work is needed in this area to establish a causal link.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.312
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.033
GPT teacher head0.361
Teacher spread0.328 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2023
Admission routes1
Has abstractyes

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