Ultra-processed Food = Greater Cancer Risk

[Tom.G]

Just out in the British Medical journal.
http://www.bmj.com/content/360/bmj.k322 or permalink doi: https://doi.org/10.1136/bmj.k322

And here is a link to the NOVA classification of "Ultra-processed" food.
http://archive.wphna.org/wp-content...1-3-28-38-Monteiro-Cannon-Levy-et-al-NOVA.pdf

Roughly, if 10%(by weight) of diet is Ultra-processed food, the cancer risk over the 7.5 years of study increased 10%

 

[Ygggdrasil]

Correlation is not causation. One obvious confounding factor which it appears from the abstract that the authors did not control for is socioeconomic status.

 

[Tom.G]

Correlation is not causation. One obvious confounding factor which it appears from the abstract that the authors did not control for is socioeconomic status.

Agreed.
See about 60% of the way thru the report under "Strengths and limitations of study":
"Firstly, as is generally the case in volunteer based cohorts, participants in the NutriNet-Santé cohort were more often women, with health conscious behaviours and higher socio-professional and educational levels than the general French population.56" (emphasis added)

Also article footnotes 31, 32, 56.

 

[russ_watters]

A couple other things (without first reading the articles...)
10% is a really low signal to noise ratio
"Processed" is typically pretty poorly defined and a broad set of actions

 

[Bystander]

"Processed"

My first reaction? "Dirty" stainless steel. "Dirty" meaning poorly refined/manufactured; lots of heavy metal impurities, As, Sb, and the like.

 

[Tom.G]

10% is a really low signal to noise ratio

Sure is! (About 0.4dB Signal-to-Noise Ratio. )

"Processed" is typically pretty poorly defined and a broad set of actions

Yes, again. That's why the second link is there, it points to the internationally recognized definitions.

 

[BWV]

Think this is an example of:

http://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.0020124

Abstract
Summary
There is increasing concern that most current published research findings are false. The probability that a research claim is true may depend on study power and bias, the number of other studies on the same question, and, importantly, the ratio of true to no relationships among the relationships probed in each scientific field. In this framework, a research finding is less likely to be true when the studies conducted in a field are smaller; when effect sizes are smaller; when there is a greater number and lesser preselection of tested relationships; where there is greater flexibility in designs, definitions, outcomes, and analytical modes; when there is greater financial and other interest and prejudice; and when more teams are involved in a scientific field in chase of statistical significance. Simulations show that for most study designs and settings, it is more likely for a research claim to be false than true. Moreover, for many current scientific fields, claimed research findings may often be simply accurate measures of the prevailing bias. In this essay, I discuss the implications of these problems for the conduct and interpretation of research