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Objective To assess the prospective associations between consumption of ultra-processed food and risk of cancer. Design Population based cohort study. Setting and participants 104 980 participants aged at least 18 years (median age 42.8 years) from the French NutriNet-Santé cohort (2009-17). Dietary intakes were collected using repeated 24 hour dietary records, designed to register participants’ usual consumption for 3300 different food items. These were categorised according to their degree of processing by the NOVA classification. Main outcome measures Associations between ultra-processed food intake and risk of overall, breast, prostate, and colorectal cancer assessed by multivariable Cox proportional hazard models adjusted for known risk factors. Results Ultra-processed food intake was associated with higher overall cancer risk (n=2228 cases; hazard ratio for a 10% increment in the proportion of ultra-processed food in the diet 1.12 (95% confidence interval 1.06 to 1.18); P for trend<0.001) and breast cancer risk (n=739 cases; hazard ratio 1.11 (1.02 to 1.22); P for trend=0.02). These results remained statistically significant after adjustment for several markers of the nutritional quality of the diet (lipid, sodium, and carbohydrate intakes and/or a Western pattern derived by principal component analysis). Conclusions In this large prospective study, a 10% increase in the proportion of ultra-processed foods in the diet was associated with a significant increase of greater than 10% in risks of overall and breast cancer. Further studies are needed to better understand the relative effect of the various dimensions of processing (nutritional composition, food additives, contact materials, and neoformed contaminants) in these associations. Study registration NCT03335644.
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2018;360:k322 | doi: 10.1136/bmj.k322 1
Consumption of ultra-processed foods and cancer risk: results
from NutriNet-Santé prospective cohort
Thibault Fiolet,1 Bernard Srour,1 Laury Sellem,1 Emmanuelle Kesse-Guyot,1 Benjamin Allès,1
Caroline Méjean,2 Mélanie Deschasaux,1 Philippine Fassier,1 Paule Latino-Martel,1
Marie Beslay,1 Serge Hercberg,1,4 Céline Lavalette,1 Carlos A Monteiro,3 Chantal Julia,1,4
Mathilde Touvier1
To assess the prospective associations between
consumption of ultra-processed food and risk of
Population based cohort study.
104980 participants aged at least 18 years (median
age 42.8 years) from the French NutriNet-Santé cohort
(2009-17). Dietary intakes were collected using
repeated 24 hour dietary records, designed to register
participants’ usual consumption for 3300 dierent
food items. These were categorised according to their
degree of processing by the NOVA classication.
Associations between ultra-processed food intake and
risk of overall, breast, prostate, and colorectal cancer
assessed by multivariable Cox proportional hazard
models adjusted for known risk factors.
Ultra-processed food intake was associated with
higher overall cancer risk (n=2228 cases; hazard
ratio for a 10% increment in the proportion of ultra-
processed food in the diet 1.12 (95% condence
interval 1.06 to 1.18); P for trend<0.001) and breast
cancer risk (n=739 cases; hazard ratio 1.11 (1.02
to 1.22); P for trend=0.02). These results remained
statistically signicant aer adjustment for several
markers of the nutritional quality of the diet (lipid,
sodium, and carbohydrate intakes and/or a Western
pattern derived by principal component analysis).
In this large prospective study, a 10% increase in the
proportion of ultra-processed foods in the diet was
associated with a signicant increase of greater than
10% in risks of overall and breast cancer. Further
studies are needed to better understand the relative
eect of the various dimensions of processing
(nutritional composition, food additives, contact
materials, and neoformed contaminants) in these
Cancer represents a major worldwide burden, with
14.1 million new cases diagnosed in 2012.1 According
to the World Cancer Research Fund/American Institute
for Cancer Research, about a third of the most common
neoplasms could be avoided by changing lifestyle
and dietary habits in developed countries.2 Therefore,
reaching a balanced and diversified diet (along with
avoidance of tobacco use and reduction in alcohol
intake) should be considered one of the most important
modifiable risk factors in the primary prevention of
At the same time, during the past decades, diets
in many countries have shifted towards a dramatic
increase in consumption of ultra-processed foods.4-8
After undergoing multiple physical, biological, and/
or chemical processes, these food products are
conceived to be microbiologically safe, convenient,
highly palatable, and aordable.9 10 Several surveys
(in Europe, the US, Canada, New Zealand, and Brazil)
assessing individual food intake, household food
expenses, or supermarket sales have suggested that
ultra-processed food products contribute to between
25% and 50% of total daily energy intake.10-18
This dietary trend may be concerning and deserves
investigation. Several characteristics of ultra-
processed foods may be involved in causing disease,
particularly cancer. Firstly, ultra-processed foods
often have a higher content of total fat, saturated
fat, and added sugar and salt, along with a lower
fibre and vitamin density.10-17 19 Beyond nutritional
composition, neoformed contaminants, some of which
have carcinogenic properties (such as acrylamide,
heterocyclic amines, and polycyclic aromatic
1Sorbonne Paris Cité
Epidemiology and Statistics
Research Center (CRESS),
Inserm U1153, Inra U1125,
Cnam, Paris 13 University,
Nutritional Epidemiology
Research Team (EREN),
Bobigny, France
34000 Montpellier, France
3Department of Nutrition,
School of Public Health,
University of São Paulo, São
Paulo 01246-904, Brazil
4Public Health Department,
Avicenne Hospital, AP-HP,
Bobigny, France
Correspondence to: B Srour
Additional material is published
online only. To view please visit
the journal online.
Cite this as: BMJ 2018;360:k322
Accepted: 10 January 2018
Ultra-processed foods are oen characterised by lower nutritional quality and
the presence of additives, substances from packaging in contact with food, and
compounds formed during production, processing, and storage
A few studies have observed ultra-processed food intake to be associated with
a higher incidence of dyslipidaemia in Brazilian children and higher risks of
overweight, obesity, and hypertension in Spanish university students
Although epidemiological data relating to cancer risk are lacking, mechanistic
studies suggest potential carcinogenic eects of several components commonly
found in ultra-processed foods
This study assessed the associations between ultra-processed food consumption
and risk of cancer in a large prospective cohort
A 10% increase in the proportion of ultra-processed foods in the diet was
associated with a signicant increase of more than 10% in the risks of overall
and breast cancer
If conrmed in other populations and settings, these results suggest that the
rapidly increasing consumption of ultra-processed foods may drive an increasing
burden of cancer in the next decades
2 doi: 10.1136/bmj.k322 |
2018;360:k322 | thebmj
hydrocarbons), are present in heat treated processed
food products as a result of the Maillard reaction.20
Secondly, the packaging of ultra-processed foods may
contain some materials in contact with food for which
carcinogenic and endocrine disruptor properties
have been postulated, such as bisphenol A.21 Finally,
ultra-processed foods contain authorised,22 but
controversial, food additives such as sodium nitrite
in processed meat or titanium dioxide (TiO2, white
food pigment), for which carcinogenicity has been
suggested in animal or cellular models.23 24
Studying potential eects on health of ultra-
processed foods is a very recent field of research,
facilitated by the development of the NOVA
classification of products according to their degree
of food processing.9 Nevertheless, epidemiological
evidence linking intake of ultra-processed food to
risk of disease is still very scarce and mostly based
on cross sectional and ecological studies.25-27 The
few studies performed observed that ultra-processed
food intake was associated with a higher incidence of
dyslipidaemia in Brazilian children and higher risks of
overweight, obesity, and hypertension in a prospective
cohort of Spanish university students.28-30
To our knowledge, this prospective study was the first
to evaluate the association between the consumption
of ultra-processed food products and the incidence of
cancer, based on a large cohort study with detailed and
up to date assessment of dietary intake.
Study population
The NutriNet-Santé study is an ongoing web based
cohort launched in 2009 in France with the objective of
studying the associations between nutrition and health,
as well as the determinants of dietary behaviours and
nutritional status. This cohort has been previously
described in detail.31 Briefly, participants aged over 18
years with access to the internet have been continuously
recruited from among the general population since
May 2009 by means of vast multimedia campaigns. All
questionnaires are completed online using a dedicated
website ( Participants
are followed using an online platform connected
to their email address. They can change their email
address, phone number, or postal address at any
time on the NutriNet-Santé website. Newsletters and
alerts about new questionnaires are sent by email. In
case of an “undelivered email” problem, participants
are contacted by telephone and then by regular mail.
The NutriNet-Santé study is conducted according to
the Declaration of Helsinki guidelines, and electronic
informed consent is obtained from each participant.
Data collection
At inclusion, participants completed a set of five
questionnaires related to sociodemographic and
lifestyle characteristics (for example, date of birth, sex,
occupation, educational level, smoking status, number
of children),32 anthropometry (height, weight), dietary
intakes (see below),33 34 physical activity (validated
seven day International Physical Activity Questionnaire
(IPAQ)),35 and health status (personal and family
history of diseases, drug use including use of hormonal
treatment for menopause and oral contraceptives, and
menopausal status).
Participants were invited to complete a series of
three non-consecutive, validated, web based 24 hour
dietary records every six months (to vary the season
of completion), randomly assigned over a two week
period (two weekdays and one weekend day).36-38 To
be included in the nutrition component of the NutriNet-
Santé cohort, only two dietary records were mandatory.
We did not exclude participants if they did not complete
all optional questionnaires. We averaged mean dietary
intakes from all the 24 hour dietary records available
during the first two years of each participant’s follow-
up and considered these as baseline usual dietary
intakes in this prospective analysis. The NutriNet-Santé
web based, self administered 24 hour dietary records
have been tested and validated against an interview
by a trained dietitian and against blood and urinary
biomarkers.36 37 Participants used the dedicated web
interface to declare all food and drinks consumed
during a 24 hour period for each of the three main meals
(breakfast, lunch, dinner) and any other eating occasion.
Portion sizes were estimated using previously validated
photographs or usual containers.39 We identified dietary
under-reporting on the basis of the method proposed by
Black, using the basal metabolic rate and Goldberg cut-
o, and excluded under-reporters of energy intake.40
We calculated mean daily alcohol, micronutrient and
macronutrient, and energy intake by using the NutriNet-
Santé food composition database, which contains more
than 3300 dierent items.41 We estimated amounts
consumed from composite dishes by using French
recipes validated by nutrition professionals. Sodium
intake was assessed via a specific module included in
the 24 hour records, taking into account native sodium
in foods, salt added during the cooking, and salt added
on the plate. It has been validated against sodium
urinary excretion biomarkers.37
Degree of food processing
We categorised all food and drink items of the
NutriNet-Santé composition table into one of the four
food groups in NOVA, a food classification system
based on the extent and purpose of industrial food
processing.942 43 This study primarily focused on the
“ultra-processed foods” NOVA group. This group
includes mass produced packaged breads and buns;
sweet or savoury packaged snacks; industrialised
confectionery and desserts; sodas and sweetened
drinks; meat balls, poultry and fish nuggets, and
other reconstituted meat products transformed
with addition of preservatives other than salt (for
example, nitrites); instant noodles and soups; frozen
or shelf stable ready meals; and other food products
made mostly or entirely from sugar, oils and fats,
and other substances not commonly used in culinary
preparations such as hydrogenated oils, modified
starches, and protein isolates. Industrial processes
2018;360:k322 | doi: 10.1136/bmj.k322 3
notably include hydrogenation, hydrolysis, extruding,
moulding, reshaping, and pre-processing by frying.
Flavouring agents, colours, emulsifiers, humectants,
non-sugar sweeteners, and other cosmetic additives
are often added to these products to imitate sensorial
properties of unprocessed or minimally processed
foods and their culinary preparations or to disguise
undesirable qualities of the final product.
The ultra-processed food group is defined by
opposition to the other NOVA groups: “unprocessed
or minimally processed foods” (fresh, dried, ground,
chilled, frozen, pasteurised, or fermented staple foods
such as fruits, vegetables, pulses, rice, pasta, eggs,
meat, fish, or milk), “processed culinary ingredients”
(salt, vegetable oils, butter, sugar, and other
substances extracted from foods and used in kitchens
to transform unprocessed or minimally processed
foods into culinary preparations), and “processed
foods” (canned vegetables with added salt, sugar
coated dried fruits, meat products preserved only by
salting, cheeses, freshly made unpackaged breads, and
other products manufactured with the addition of salt,
sugar, or other substances of the “processed culinary
ingredients” group). As previously described,44 we
identified homemade and artisanal food preparations,
decomposed them using standardised recipes, and
applied the NOVA classification to their ingredients.
Precision and examples are shown in appendix 1.
Case ascertainment
Participants self declared health events through the
yearly health status questionnaire, through a specific
check-up questionnaire for health events (every three
months), or at any time through a specific interface
on the study website. For each incident cancer
declared, a physician from the study team contacted
participants and asked them to provide any relevant
medical records. If necessary, the study physicians
contacted the patient’s physician and/or hospitals to
collect additional information. Afterwards, an expert
committee of physicians reviewed all medical data.
Our research team was the first in France to obtain
the authorisation by decree in the Council of State (No
2013-175) to link data from our cohorts to medico-
administrative databases of the national health
insurance system (SNIIRAM databases). We therefore
completed declared health events with the information
from these databases, thereby limiting any potential
bias due to participants with cancer who may not
report their disease to the study investigators. Lastly,
we used an additional linkage to the French national
cause specific mortality registry (CépiDC) to detect
deaths and potentially missed cases of cancer for
deceased participants. We classified cancer cases by
using the international classification of diseases, 10th
revision (ICD-10). In this study, we considered all first
primary cancers diagnosed between the inclusion date
and 1 January 2017 to be cases, except for basal cell
skin carcinoma, which we did not consider as cancer.
We obtained medical records for more than 90%
of cancer cases. Because of the high validity of self
reports (95% of self reported cancers for which a
medical record was obtained were confirmed by our
physicians), we included as cases all participants
who self reported incident cancers, unless they were
identified as non-case participants by a pathology
report, in which case we classified them as non-cases.
Statistical analysis
Up to 1 January 2017, we included 104980 participants
without cancer at baseline who provided at least two
valid 24 hour dietary records during their two first years
of follow-up. The flowchart is in appendix 2. For each
participant, we calculated the proportion (percentage
g/day) of ultra-processed foods in the total diet. We
determined the proportion of ultra-processed foods
in the diet by calculating a weight ratio rather than an
energy ratio to take into account processed foods that
do not provide any energy (in particular artificially
sweetened drinks) and non-nutritional factors
related to food processing (for example, neoformed
contaminants, food additives, and alterations to
the structure of raw foods). For all covariates except
physical activity, less than 5% of values were missing
and were imputed to the modal value (for categorical
variables) or to the median (for continuous variables).
Corresponding values are provided in the footnote to
table 1. The proportion of missing values was higher
for physical activity (14%), as the answers to all IPAQ
questions were needed to calculate the score. To avoid
massive imputation for a non-negligible number of
participants or exclusion of those with missing data
and risk of selection bias, we included a missing
class into the models for this variable. We examined
dierences in participants’ baseline characteristics
between sex specific quarters of the proportion of
ultra-processed food in the diet by using analysis of
variance or χ2 tests wherever appropriate. We used Cox
proportional hazards models with age as the primary
timescale to evaluate the association between the
proportion of ultra-processed foods in the diet (coded
as a continuous variable or as sex specific quarters) and
incidence of overall, breast, prostate, and colorectal
cancer. In these models, cancers at other locations
than the one studied were censored at the date of
diagnosis (that is, we considered them to be non-cases
for the cancer of interest and they contributed person
years until the date of diagnosis of their cancer). We
estimated hazard ratios and 95% confidence intervals
with the lowest quarter as the reference category. We
generated log-log (survival) versus log-time plots to
confirm risk proportionality assumptions. We tested
for linear trend by using the ordinal score on sex
specific quarters of ultra-processed food. Participants
contributed person time until the date of diagnosis of
cancer, the date of last completed questionnaire, the
date of death, or 1 January 2017, whichever occurred
first. Breast cancer analyses were additionally stratified
by menopausal status. For these, women contributed
person time to the “premenopause model” until
their age at menopause and to the “postmenopause
model” from their age at menopause. We determined
4 doi: 10.1136/bmj.k322 |
2018;360:k322 | thebmj
age at menopause by using the yearly health status
questionnaires completed during follow-up.
Models were adjusted for age (timescale), sex,
body mass index (kg/m2, continuous), height (cm,
continuous), physical activity (high, moderate, low,
calculated according to IPAQ recommendations35),
smoking status (never or former smokers, current
smokers), number of 24 hour dietary records
(continuous), alcohol intake (g/d, continuous),
energy intake (without alcohol, kcal/d, continuous),
family history of cancer (yes/no), and educational
level (less than high school degree, less than two
years after high school degree, two or more years
after high school degree). For breast cancer analyses,
we made additional adjustments for the number of
biological children (continuous), menopausal status
at baseline (menopausal/perimenopausal/non-
menopausal), hormonal treatment for menopause at
baseline (for postmenopausal analyses, yes/no), and
oral contraception use at baseline (for premenopausal
analyses, yes/no) (model 1=main model). To test for
the potential influence of the nutritional quality of the
diet in the relation between intake of ultra-processed
food and risk of cancer, this model was additionally
adjusted for lipid, sodium, and carbohydrate intakes
(model 2), for a Western dietary pattern derived from
principal component analysis (model 3) (details in
appendix 3), or for all these nutritional factors together
(model 4). In addition, we did mediation analyses
according to the method proposed by Lange et al to
evaluate the direct and indirect eect of the relation
between the exposure and the outcome through the
following nutritional mediators: intakes of sodium,
total lipids, saturated, mono-unsaturated and poly-
unsaturated fatty acids, carbohydrates, and a Western-
type dietary pattern.45 The methods are described in
appendix 4.
We did sensitivity analyses based on model 1 by
excluding cases of cancer diagnosed during the first
two years of each participant’s follow-up to avoid
reverse causality bias, testing sex specific fifths of the
proportion of ultra-processed foods in the diet instead
of sex specific quarters, and testing further adjustments
for prevalent depression at baseline (yes/no), dietary
supplement use at baseline (yes/no), healthy dietary
pattern (continuous, details in appendix 3), number of
cigarettes smoked in pack years (continuous), overall
fruit and vegetable consumption (continuous), and
Table1 | Baseline characteristics of study population according to sex specic quarters of ultra-processed food consumption (n=104 980), NutriNet-
Santé cohort, France, 2009-17*. Values are numbers (percentages) unless stated otherwise
Characteristics All participants
Quarters of ultra-processed food consumption†
P for trend‡1 (n=26 244) 2 (n=26 245) 3 (n=26 246) 4 (n=26 245)
Mean (SD) age, years 42.8 (14.8) 47.9 (13.5) 45.0 (14.0) 42.0 (14.4) 36.5 (13.6) <0.001
Female sex 82 159 (78.3) 20 539 (78.3) 20 540 (78.3) 20 541 (78.3) 20 542 (78.3)
Mean (SD) height, cm 166.8 (8.1) 166.3 (8.0) 166.7 (8.0) 167.0 (8.1) 167.3 (8.2) <0.001
Mean (SD) body mass index 23.8 (4.6) 23.8 (4.3) 23.8 (4.4) 23.8 (4.5) 23.8 (5.0) 0.9
Family history of cancer§ 35 668 (34.0) 10 542 (40.2) 9624 (36.7) 8625 (32.9) 6877 (26.2) <0.001
Higher education:
No 19 357 (18.4) 5154 (19.6) 4961 (18.9) 4637 (17.7) 4605 (17.6)
Yes, <2 years 18 076 (17.2) 3938 (15.0) 4091 (15.6) 4426 (16.9) 5621 (21.4)
Yes, 2 years 67 547 (64.3) 17 152 (65.4) 17 193 (65.5) 17 183 (65.5) 16 019 (61.0)
Smoking status:
<0.001 Current 17 763 (16.9) 4127 (15.7) 4065 (15.5) 4266 (16.3) 5305 (20.2)
Never/former 87 217 (83.1) 22 117 (84.3) 22 180 (84.5) 21 980 (83.8) 20 940 (79.8)
IPAQ physical activity level:¶
High 29 603 (28.2) 8753 (33.4) 7762 (29.6) 6983 (26.6) 6105 (23.3)
Moderate 38 874 (37.0) 9620 (36.7) 9953 (37.9) 9814 (37.4) 9487 (36.2)
Low 21 888 (20.9) 4407 (16.8) 5152 (19.6) 5839 (22.3) 6490 (24.7)
Mean (SD) energy intake without alcohol, kcal/d 1879.0 (473.7) 1810.6 (454.1) 1881.1 (457.7) 1908.5 (472.3) 1915.8 (501.8) <0.001
Mean (SD) alcohol intake, g/d 7.8 (11.9) 9.3 (13.3) 8.5 (11.9) 7.5 (11.3) 5.9 (10.5) <0.001
Mean (SD) total lipid intake, g/d 80.5 (25.5) 76.0 (24.3) 80.3 (24.4) 82.1 (25.3) 83.4 (27.3) <0.001
Mean (SD) carbohydrate intake, g/d 195.4 (57.9) 184.6 (57.8) 193.9 (55.3) 199.3 (56.6) 203.6 (60.2) <0.001
Mean (SD) sodium intake, mg/d 2700.1 (893.1) 2589.3 (881.6) 2731.8 (871.0) 2761.9 (884.1) 2717.7 (925.0) <0.001
Mean (SD) No of children 1.3 (1.2) 1.6 (1.2) 1.4 (1.2) 1.3 (1.2) 1.0 (1.2) <0.001
Menopausal status:**
Premenopausal 57 408 (69.9) 11 797 (57.4) 13 497 (65.7) 14 961 (72.8) 17 153 (83.5)
Perimenopausal 4282 (5.2) 1471 (7.2) 1148 (5.6) 997 (4.9) 666 (3.2)
Postmenopausal 20 469 (24.9) 7271 (35.4) 5895 (28.7) 4582 (22.3) 2721 (13.3)
Use of hormonal treatment for menopause** 4324 (5.3) 1602 (7.8) 1242 (6.1) 932 (4.5) 548 (2.7) <0.001
Oral contraception** 23 073 (22.0) 3779 (14.4) 4990 (19.0) 6209 (23.7) 8095 (30.8) <0.001
Mean (SD) ultra-processed food, % 18.7 (10.1) 8.5 (2.5) 14.3 (1.4) 19.8 (1.9) 32.3 (9.8)
IPAQ=International Physical Activity Questionnaire.
*For all covariates except physical activity, a very low proportion of values were missing (0-5%); these were replaced by modal value in study population: “≥2 years of higher education” for
educational level, 0 for No of biological children, 22.9 for body mass index, 166 cm for height, and non-smoker for smoking status.
†Sex specic quarters of proportion of ultra-processed food intake in total quantity of food consumed; sex specic cut-os for quarters of ultra-processed proportions were 11.8%, 16.8%, and
23.3% in men and 11.8%, 16.8%, and 23.4% in women.
‡P value for comparison between sex specic quarters of ultra-processed food consumption, by Fisher test or χ2 test where appropriate.
§Among rst degree relatives.
¶Available for 90 365 participants; participants were categorised into “high,” “moderate,” and “low” categories according to IPAQ guidelines.35
**Among women.
2018;360:k322 | doi: 10.1136/bmj.k322 5
season of inclusion in the cohort (spring/summer/
autumn/winter). We also investigated the association
between ultra-processed food and overall cancer risk
separately in dierent strata of the population: men,
women, younger adults (under 40 years), older adults
(40 years or over), smokers, non-smokers, participants
with a high level of physical activity, and those with a
low to moderate level of physical activity. We also tested
models after restriction of the study population to the
participants with at least six 24 hour dietary records
during the first two years of follow-up. Similarly, we
tested models including all participants with at least
one 24 hour dietary record during the first two years
of follow-up. We also tested associations between the
quantity (g/d) of each ultra-processed food group and
risk of cancer.
Secondary analyses tested the associations between
the proportion in the diet of each of the three other
NOVA categories of food processing (continuous) and
risk of cancer, using multivariate Cox models adjusted
for model 1 covariates. All tests were two sided, with
P<0.05 considered to be statistically significant. We
used SAS version 9.4 for the analyses.
Patient involvement
The research question developed in this article
corresponds to a strong concern of the participants
involved in the NutriNet-Santé cohort and of the public
in general. The results of this study will be disseminated
to the NutriNet-Santé participants through the cohort
website, public seminars, and a press release.
A total of 104980 participants (22821 (21.7%) men
and 82159 (78.3%) women) were included in the
study. The mean age of participants was 42.8 (SD
14.8, range 18.0-72.8) years. The mean number of
dietary records per participant over their first two years
of follow-up was 5.4 (SD 2.9); the minimum was 2,
but it represented only 7.2% (7558/104980) of the
participants. After the launching of the study by the
end of May 2009, half of the records were filled between
June and November and the other half between
December and May. Table 1 shows the main baseline
characteristics of participants according to quarters
of the proportion of ultra-processed foods in the diet.
Compared with the lowest quarter, participants in the
highest quarter of ultra-processed food intake tended
to be younger, current smokers, and less educated,
with less family history of cancer and a lower physical
activity level. Furthermore, they had higher intakes
of energy, lipids, carbohydrates, and sodium, along
with lower alcohol intake. Although there was a higher
proportion of women than men in this cohort, the
contribution of ultra-processed foods to the overall diet
was very similar between men and women (18.74% for
men and 18.71% for women; P=0.7). The distribution
of the proportion of ultra-processed food in the diet
in the study population is shown in appendix 5. Main
food groups contributing to ultra-processed food
intake were sugary products (26%) and drinks (20%),
followed by starchy foods and breakfast cereals (16%)
and ultra-processed fruits and vegetables (15%) (fig 1).
During follow-up (426362 person years, median
follow-up time five years), 2228 first incident cases
of cancer were diagnosed and validated, among
which were 739 breast cancers (264 premenopausal,
475 postmenopausal), 281 prostate cancers, and
153 colorectal cancers. Among these 2228 cases,
108 (4.8%) were identified during mortality follow-
up with the national CépiDC database. The dropout
rate in the NutriNet-Santé cohort was 6.7%. Table 2
shows associations between the proportion of ultra-
processed foods in the diet and risks of overall, breast,
prostate, and colorectal cancer. Figure 2 shows the
corresponding cumulative incidence curves. In model
1, ultra-processed food intake was associated with
increased risks of overall cancer (hazard ratio for a 10
point increment in the proportion of ultra-processed
foods in the diet 1.12 (95% confidence interval 1.06 to
1.18), P<0.001) and breast cancer (1.11 (1.02 to 1.22),
P=0.02). The latter association was more specifically
observed for postmenopausal breast cancer (P=0.04)
but not for premenopausal breast cancer (P=0.2). The
association with overall cancer risk was statistically
significant in all strata of the population investigated,
after adjustment for model 1 covariates: in men
(hazard ratio for a 10 point increment in the proportion
of ultra-processed foods in the diet 1.12 (1.02 to 1.24),
P=0.02, 663 cases and 22158 non-cases), in women
(1.13 (1.06 to 1.20), P<0.001, 1565 cases and 80594
non-cases), in younger adults (<40 years old 1.21
(1.09 to 1.35), P<0.001, 287 cases and 48627 non-
cases), in older adults (≥40 years old, 1.09 (1.03 to
1.16), P=0.03, 1941 cases and 54485 non-cases),
in smokers (including adjustment for pack years of
cigarettes smoked 1.18 (1.04 to 1.33), P=0.01, 255
cases and 15355 non-cases), in non-smokers (1.11
(1.05 to 1.17), P<0.001, 1943 cases and 85219 non-
cases), in participants with low to moderate levels of
physical activity (1.07 (1.00 to 1.15), P=0.04, 1216
cases and 59546 non-cases), and in those with a high
level of physical activity (1.19 (1.09 to 1.30), P<0.001,
744 cases and 28859 non-cases).
More specifically, ultra-processed fats and sauces
(P=0.002) and sugary products (P=0.03) and drinks
Salty snacks (2%) Fats (2%)
Processed meats
Meats, sh, eggs
Dairy products
fruits and
Starchy foods and
breakfast cereals
Sugary products 5%
Fig1 | Relative contribution of each food group to
ultra-processed food consumption in diet
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2018;360:k322 | thebmj
(P=0.005) were associated with an increased risk of
overall cancer, and ultra-processed sugary products
were associated with risk of breast cancer (P=0.006)
(appendix 6).
Further adjustment for several indicators of the
nutritional quality of the diet (lipid, sodium, and
salt intakes—model 2; Western pattern—model 3; or
both—model 4) did not modify these findings. The
Pearson correlation coecient between the proportion
of ultra-processed food in the diet and the Western
dietary pattern was low (0.06). Consistently, analyses
performed according to the method proposed by Lange
et al to assess a potential mediation of the relation
between ultra-processed food and risk of cancer
Table2 | Associations between ultra-processed food intake and risk of overall, prostate, colorectal, and breast cancer, from multivariable Cox
proportional hazard models*, NutriNet-Santé cohort, France, 2009-17 (n=104 980)
Proportion of ultra-processed food intake in the diet
Sex specic quarters‡
1 2 3 4
HR (95% CI) P for trend HR HR (95% CI) HR (95% CI) P for trend HR (95% CI)
All cancers
No of cases/non-cases 2228/102 752 712/25 532 607/25 638 541/25 705 368/25 877
Model 1 1.12 (1.06 to 1.18) <0.001 10.99 (0.89 to 1.11) 1.10 (0.99 to 1.24) 1.21 (1.06 to 1.38) 0.002
Model 2 1.12 (1.07 to 1.18) <0.001 11.00 (0.90 to 1.11) 1.11 (0.99 to 1.25) 1.23 (1.08 to 1.40) 0.001
Model 3 1.12 (1.06 to 1.18) <0.001 10.99 (0.89 to 1.11) 1.01 (0.98 to 1.23) 1.21 (1.06 to 1.38) 0.002
Model 4 1.13 (1.07 to 1.18) <0.001 11.00 (0.90 to 1.11) 1.11 (0.99 to 1.24) 1.23 (1.08 to 1.40) 0.001
Prostate cancer
No of cases/non-cases 281/22 540 96/5609 96/5609 59/5647 30/5675
Model 1 0.98 (0.83 to 1.16) 0.8 11.18 (0.89 to 1.57) 0.95 (0.69 to 1.32) 0.93 (0.61 to 1.40) 0.6
Model 2 0.98 (0.83 to 1.16) 0.8 11.18 (0.89 to 1.57) 0.95 (0.69 to 1.32) 0.93 (0.61 to 1.40) 0.6
Model 3 0.98 (0.83 to 1.15) 0.8 11.18 (0.89 to 1.56) 0.95 (0.68 to 1.31) 0.92 (0.61 to 1.39) 0.6
Model 4 0.98 (0.83 to 1.16) 0.8 11.18 (0.89 to 1.57) 0.95 (0.68 to 1.32) 0.93 (0.61 to 1.40) 0.6
Colorectal cancer
No of cases/non-cases 153/104 827 48/26 196 43/26 202 36/26 210 26/26 219
Model 1 1.13 (0.92 to 1.38) 0.2 11.10 (0.72 to 1.66) 1.17 (0.76 to 1.81) 1.49 (0.92 to 2.43) 0.1
Model 2 1.16 (0.95 to 1.42) 0.1 11.12 (0.74 to 1.70) 1.22 (0.79 to 1.90) 1.59 (0.97 to 2.60) 0.07
Model 3 1.13 (0.92 to 1.38) 0.2 11.09 (0.92 to 1.38) 1.16 (0.75 to 1.80) 1.48 (0.91 to 2.41) 0.1
Model 4 1.16 (0.95 to 1.42) 0.1 11.12 (0.74 to 1.70) 1.22 (0.79 to 1.89) 1.23 (1.08 to 1.40) 0.07
Breast cancer
No of cases/non-cases 739/81 420 247/20 292 202/20 338 179/20 361 111/20 429
Model 1 1.11 (1.02 to 1.22) 0.02 10.97 (0.81 to 1.17) 1.10 (0.90 to 1.34) 1.14 (0.91 to 1.44) 0.2
Model 2 1.11 (1.01 to 1.21) 0.03 10.96 (0.80 to 1.16) 1.09 (0.89 to 1.32) 1.12 (0.89 to 1.42) 0.2
Model 3 1.11 (1.02 to 1.22) 0.02 10.97 (0.80 to 1.17) 1.09 (0.90 to 1.33) 1.14 (0.91 to 1.44) 0.2
Model 4 1.11 (1.01 to 1.21) 0.03 10.96 (0.80 to 1.16) 1.08 (0.89 to 1.32) 1.13 (0.89 to 1.42) 0.2
Premenopausal breast cancer
No of cases/non-cases 264/57 151 90/14 263 70/14 284 55/14 299 49/14 305
Model 1 1.09 (0.95 to 1.25) 0.2 10.91 (0.67 to 1.25) 0.92 (0.65 to 1.29) 1.30 (0.90 to 1.86) 0.3
Model 2 1.07 (0.93 to 1.23) 0.4 10.90 (0.66 to 1.24) 0.90 (0.64 to 1.27) 1.25 (0.87 to 1.80) 0.4
Model 3 1.09 (0.95 to 1.26) 0.2 10.91 (0.67 to 1.25) 0.92 (0.66 to 1.30) 1.30 (0.91 to 1.88) 0.3
Model 4 1.08 (0.94 to 1.24) 0.3 10.91 (0.66 to 1.24) 0.91 (0.64 to 1.28) 1.27 (0.88 to 1.83) 0.4
Postmenopausal breast cancer
No of cases/non-cases 475/29 191 107/7309 128/7289 123/7294 117/7299
Model 1 1.13 (1.01 to 1.27) 0.04 11.23 (0.95 to 1.60) 1.28 (0.98 to 1.66) 1.39 (1.07 to 1.82) 0.02
Model 2 1.13 (1.00 to 1.27) 0.05 11.23 (0.95 to 1.60) 1.27 (0.98 to 1.65) 1.39 (1.05 to 1.81) 0.02
Model 3 1.13 (1.00 to 1.27) 0.04 11.23 (0.95 to 1.59) 1.27 (0.98 to 1.65) 1.38 (1.06 to 1.81) 0.02
Model 4 1.13 (1.00 to 1.27) 0.05 11.23 (0.95 to 1.59) 1.27 (0.97 to 1.65) 1.38 (1.05 to 1.81) 0.02
HR=hazard ratio.
*Model 1=multivariable Cox proportional hazard model adjusted for age (timescale), sex, energy intake without alcohol, number of 24 hour dietary records, smoking status, educational
level, physical activity, height, body mass index, alcohol intake, and family history of cancers; breast cancer models were additionally adjusted for menopausal status, hormonal treatment for
menopause, oral contraception, and number of children. Model 2=model 1 plus intakes of lipids, sodium, and carbohydrates. Model 3=model 1 plus Western dietary pattern (derived by factor
analysis). Model 4=model 1 plus intakes of lipids, sodium, and carbohydrates and Western dietary pattern (derived by factor analysis). Pearson correlation coecients with Western dietary
pattern were 0.5 for dietary lipids, 0.6 for sodium, and 0.40 for carbohydrates.
†Hazard ratio for increase of 10% in proportion of ultra-processed food intake in diet.
‡Sex specic cut-os for quarters of ultra-processed proportions were 11.8%, 16.8%, and 23.3% in men and 11.8%, 16.8%, and 23.4% in women. In premenopausal women, cut-os were
12.8%, 18.1%, and 25.0%. In postmenopausal women, cut-os were 10.1%, 14.3%, and 19.5%.
Time (days)
Cancer incidence
0 500 1000 1500 2000 2500
1.0 Quarter 1
Quarter 2
Quarter 3
Quarter 4
Fig2 | Cumulative cancer incidence (overall cancer risk)
according to quarters of proportion of ultra-processed
food in diet
2018;360:k322 | doi: 10.1136/bmj.k322 7
by these nutritional factors showed no statistically
significant mediation eect of any of the factors
tested.45 The mediated eects ranged between 0% and
2%, with all P>0.05 (appendix 4).
No association was statistically significant for
prostate and colorectal cancers. However, we observed
a borderline non-significant trend of increased risk of
colorectal cancer associated with ultra-processed food
intake (hazard ratio for quarter 4 versus quarter 1: 1.23
(1.08 to 1.40), P for trend=0.07) in model 4.
Sensitivity analyses (adjusted for model 1 covariates,
data not tabulated) excluding cancer cases diagnosed
during the first two years of follow-up provided similar
results (hazard ratio for a 10 point increment in the
proportion of ultra-processed foods in the diet 1.10
(1.03 to 1.17), P=0.005 for overall cancer risk, 1367
cases and 102502 non-cases included; 1.15 (1.03 to
1.29), P=0.02 for breast cancer risk, 441 cases and
80940 non-cases included). Similarly, results were
unchanged when we excluded non-validated cancer
cancers (hazard ratio for a 10 point increment in the
proportion of ultra-processed foods in the diet 1.11
(1.05 to 1.17), P<0.001 for overall cancer risk, 1967
cases and 102752 non-cases included; 1.12 (1.02 to
1.23), P=0.02 for breast cancer risk, 677 cases and
81274 non-cases included).
We obtained similar results when we included only
participants with at least six 24 hour records (overall
cancer risk: hazard ratio for a 10 point increment in
the proportion of ultra-processed foods in the diet 1.13
(1.06 to 1.21), P<0.001, 1494 cases and 47 920 non-
cases included) and when we re-included participants
with only one 24 hour record (overall cancer risk: 1.11
(1.06 to 1.16), P<0.001, 2383 cases and 122 196 non-
cases included).
Findings were also similar when we coded the
proportion of ultra-processed food in the diet as sex
specific fifths instead of quarters (overall cancer risk:
hazard ratio for highest versus lowest fifth 1.25 (1.08
to 1.47), P for trend<0.001; breast cancer risk: 1.25
(0.96 to 1.63), P for trend=0.03).
Further adjustment for the following variables, in
addition to model 1 covariates, did not modify the
results: dietary supplement use at baseline (hazard
ratio for a 10 point increment in the proportion of
ultra-processed foods in the diet 1.12 (1.06 to 1.17),
P<0.001 for overall cancer; 1.11 (1.02 to 1.22), P=0.02
for breast cancer), prevalent depression at baseline
(1.11 (1.06 to 1.17), P<0.001 for overall cancer; 1.11
(1.01 to 1.22), P=0.02 for breast cancer), healthy
dietary pattern (1.11 (1.05 to 1.17), P<0.001 for overall
cancer; 1.10 (1.00 to 1.21), P=0.04 for breast cancer),
overall fruit and vegetable consumption in g/d (1.10
(1.04 to 1.16), P<0.001 for overall cancer; 1.11 (1.01
to 1.22), P=0.03 for breast cancer), number of smoked
cigarettes in pack years (1.13 (1.07 to 1.19), P<0.001
for overall cancer; 1.13 (1.03 to 1.24), P=0.009 for
breast cancer), and season of inclusion in the cohort
(1.12 (1.06 to 1.18), P<0.001 for overall cancer; 1.12
(1.02 to 1.22), P=0.02 for breast cancer).
We also tested other methods for handling missing
data, such as multiple imputation and complete case
analysis (that is, exclusion of participants with missing
data for at least one covariate).46 The results were very
similar for the multiple imputation analysis (hazard
ratio for a 10 point increment in the proportion of
ultra-processed foods in the diet 1.11 (1.06 to 1.17),
P<0.001, 2228 cases and 102752 non-cases for
overall cancer; 1.11 (1.01 to 1.21), P=0.02, 739 cases
and 81420 non-cases for breast cancer) and for the
complete case analysis (1.11 (1.05 to 1.18), P<0.001,
1813 cases and 82824 non-cases for overall cancer;
1.14 (1.03 to 1.26), P=0.01, 579 cases and 64642
non-cases for breast cancer).
As a secondary analysis, we also tested associations
between the proportions of the three other NOVA
degrees of food processing and risk of cancer. We
found no significant associations between the
proportions of “processed culinary ingredients” or
“processed foods” with risk of cancer at any location
(all P>0.05). However, and consistent with our
findings, the consumption of “minimally/unprocessed
foods” was associated with lower risks of overall and
breast cancers (hazard ratio for a 10 point increment
in the proportion of unprocessed foods in the diet 0.91
(0.87 to 0.95), P<0.001, 2228 cases and 102752 non-
cases for overall cancer; 0.42 (0.19 to 0.91), P=0.03,
739 cases and 81420 non-cases for breast cancer), in
multivariable analyses adjusted for model 1 covariates.
In this large prospective cohort, a 10% increase in the
proportion of ultra-processed foods in the diet was
associated with significant increases of 12% in the
risk of overall cancer and 11% in the risk of breast
cancer. A few studies have previously suggested that
ultra-processed foods contribute to increasing the
risk of cardiometabolic disorders—such as obesity,29
hypertension,30 and dyslipidaemia28—but no previous
prospective epidemiological study has evaluated the
association between food processing and risk cancer.
Interpretation and comparison with other studies
No estimate is available of the proportion of ultra-
processed food in the diet at the national level in
France. However, in the nationally representative
INCA3 study conducted by the French Food safety
Agency in 2016,4 “transformed” foods included sweet
pastries, biscuits, dairy desserts, ice cream, fruit purée
and fruit in syrup, fruit and vegetable juices, soups and
broths, sandwiches, pizzas, and salted pastries, as well
as mixed dishes composed of egg, meat, fish, vegetable,
and/or starchy foods (cereals, legumes, or potatoes).
More than half of the “transformed” foods consumed
outside catering establishments by adults aged 18-79
were manufactured industrially, about a third were
homemade, and the rest was handcrafted (for example,
by caterers). These figures illustrate the important share
of processed, and especially industrially processed,
foods in the diet of French adults.
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2018;360:k322 | thebmj
Several hypotheses could be put forward to explain
our findings. The first one relates to the generally
poorer nutritional quality of diets rich in ultra-
processed foods. Diets that include a higher proportion
of processed food products tended to be richer in
energy, sodium, fat, and sugar and poorer in fibres and
various micronutrients in several studies conducted
in various countries.10-17 19 Ultra-processed foods
have also been associated with a higher glycaemic
response and a lower satiety eect.47 Although not
the unique determinant, excessive energy, fat, and
sugar intakes contribute to weight gain and risk
of obesity, with obesity recognised as a major risk
factor for post-menopausal breast, stomach, liver,
colorectal, oesophagus, pancreas, kidney, gallbladder,
endometrium, ovary, liver, and (advanced) prostate
cancers and haematological malignancies.29 For
instance, body fatness in post-menopausal women
is estimated to contribute 17% of the breast cancer
burden.2 Furthermore, most ultra-processed foods,
such as dehydrated soups, processed meats,
biscuits, and sauces, have a high salt content. Foods
preserved with salt are associated with an increased
risk of gastric cancer.29 Conversely, dietary fibre
intake decreases the risk of colorectal cancer, with a
convincing level of evidence,3 29 and may also reduce
the risk of breast cancer.3 However, the associations
between ultra-processed food intake and risk of cancer
observed in this study were statistically significant
despite adjustment for body mass index and remained
significant after further adjustment for a Western-type
dietary pattern and/or the energy, fat, sugar, and salt
content of the diet. Mediation analyses did not support
a strong eect of the “nutritional quality” component
in this association, suggesting that other bioactive
compounds contained in ultra-processed food may
contribute to explain the observed associations.
A second hypothesis concerns the wide range
of additives contained in ultra-processed foods.
Although maximum authorised levels normally
protect the consumers against adverse eects of each
individual substance in a given food product,48 the
eect on health of the cumulative intake across all
ingested foods and potential cocktail/interaction
eects remain largely unknown. More than 250
dierent additives are authorised for addition to food
products in Europe and the US.22 49 For some of them,
experimental studies in animal or cellular models
have suggested carcinogenic properties that deserve
further investigation in humans.23 24 50-53 One example
is titanium dioxide (TiO2), a common food additive
that contains nanoscale particles and that is used as
a whitening agent or in packaging in contact with food
or drinks to provide a better texture and antimicrobial
properties. Experimental studies, mainly conducted
in rodent models, suggest that this additive could
initiate or promote the development of pre-neoplastic
lesions in the colon, as well as chronic intestinal
inflammation. The World Health Organization and the
International Agency for Research on Cancer evaluated
TiO2 as “possibly carcinogenic to humans” (group
2B).24 The eects of intense artificial sweeteners
such as aspartame on human metabolism and on the
composition and functioning of gut microbiota are
also controversial.53 Although previous experimental
studies in animals confirmed the safety of aspartame,
their relevance to human health outcomes has been
questioned, particularly regarding potential long term
carcinogenicity.51 Another concern is the formation of
carcinogenic nitrosamines in meats containing sodium
nitrite when meat is charred or overcooked. These
N-nitroso compounds may be involved in causing
colorectal cancer.23 52
Thirdly, food processing and particularly heat
treatments produce neoformed contaminants (for
example, acrylamide) in ultra-processed products
such as fried potatoes, biscuits, bread, or coee. A
recent meta-analysis found a modest association
between dietary acrylamide and risk of both kidney
and endometrial cancer in non-smokers.54 In addition,
the European Food Safety Agency judged that
evidence from animal studies was sucient to classify
acrylamide as genotoxic.20
Lastly, bisphenol A is another contaminant
suspected of migrating from plastic packaging of ultra-
processed foods. Its endocrine disruptor properties
led the European Chemicals Agency to judge it as “a
substance of very high concern.”55 Increasing evidence
suggests involvement in the development of several
non-communicable diseases, including cancer linked
to endocrinal disruptors.21
Strengths and limitations of study
Strengths of this study pertain to its prospective
design and large sample size, along with a detailed
and up to date assessment of dietary intake. Repeated
24 hour dietary records (including 3300 dierent
food items) are more accurate than either food
frequency questionnaires with aggregated food
groups or household purchasing data. However,
some limitations should be acknowledged. 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 This might limit the
generalisability of the findings and may have resulted
in a lower incidence of cancer compared with national
estimates (age and sex standardised incidence rate per
100000 people per year: 786 cases in our cohort versus
972 cases in France57) and an overall lower exposure
to ultra-processed foods, with less contrast between
extreme categories. These points would tend to lead
to underestimation of the strength of the associations.
However, the possibility that selection bias may have
led to an overestimation of some associations cannot
be totally excluded. Secondly, some misclassification
in the NOVA “ultra-processed food” category cannot
be ruled out. Thirdly, despite a multi-source strategy
for case ascertainment (combining validation of
health events declared by participants, medico-
administrative databases from the health insurance,
2018;360:k322 | doi: 10.1136/bmj.k322 9
and national death registry), exhaustive detection
of cancer cases cannot be guaranteed. Furthermore,
statistical power was limited for some cancer locations
(such as colorectal cancer), which may have impaired
our ability to detect hypothesised associations. Next,
the length of follow-up was relatively limited, as the
cohort was launched in 2009. It allowed us to study
mostly mid-term associations between consumption
of ultra-processed food and risk of cancer. As is
usually the case in nutritional epidemiology, we
made the assumption that the measured exposure at
baseline (especially as we averaged a two year period
of exposure) actually reflects more generally the usual
eating habits of the individual during adulthood,
including several years before his or her entry into
the cohort. However, as some carcinogenic processes
may take several decades, it will be important in the
future to reassess the associations between ultra-
processed food and cancer risk in the cohort, to
investigate longer term eects. This will be one of the
perspectives of our work for the upcoming five to 10
years. Lastly, although we included a large range of
confounding factors in the analyses, the hypothesis
of residual confounding resulting from unmeasured
behavioural factors and/or imprecision in the measure
of included covariates cannot be entirely excluded
owing to the observational design of this study. For
instance, oral contraception was a binary variable in
breast cancer models, as the precise doses, type, and
duration of contraceptive use across reproductive
life were not available. Randomised controlled trials
have long been considered the only gold standard
for elimination of confounding bias, but they do not
capture consumption as it is in daily life. Moreover,
a trial to investigate exposure for which a deleterious
eect is suspected would not be ethically feasible. Our
large observational cohort was therefore particularly
adapted to provide insights in this field.
Conclusions and policy implications
To our knowledge, this study was the first to investigate
and highlight an increase in the risk of overall—and
more specifically breast—cancer associated with
ultra-processed food intake. These results should
be confirmed by other large scale, population based
observational studies in dierent populations
and settings. Further studies are also needed to
better understand the relative eect of nutritional
composition, food additives, contact materials, and
neoformed contaminants in this relation. Rapidly
increasing consumption of ultra-processed foods
may drive an increasing burden of cancer and other
non-communicable diseases. Thus, policy actions
targeting product reformulation, taxation, and
marketing restrictions on ultra-processed products and
promotion of fresh or minimally processed foods may
contribute to primary cancer prevention.6 9 Several
countries have already introduced this aspect in their
ocial nutritional recommendations in the name of
the precautionary principle.58 59
We sincerely thank all the volunteers of the NutriNet-Santé cohort.
We also thank Véronique Gourlet, Lucien Martinez, Nathalie Arnault,
Stephen Besseau, Laurent Bourhis, Yasmina Chelghoum, Than Duong
Van, Younes Esseddik, Paul Flanzy, Julien Allègre, Mac Rakotondrazafy,
Régis Gatibelza, Fabien Szabo, Roland Andrianasolo, Fatoumata
Diallo, Ludivine Ursule, Cédric Agaesse, Claudia Chahine, Anne-Elise
Dussoulier, and Marion Genest for their technical contribution to the
NutriNet-Santé study.
Contributors: TF and BS contributed equally and are co-rst authors.
TF, BS, CJ, EKG, CAM, BA, and MT designed the research. SH, MT,
CJ, and EKG conducted the research. TF did the statistical analysis,
supervised by MT and BS. TF and MT wrote the paper. BS did
sensitivity analyses and was in charge of the revision of the paper.
All authors contributed to the data interpretation, revised each dra
for important intellectual content, and read and approved the nal
manuscript. MT is the guarantor.
Funding: The NutriNet-Santé study was supported by the following
public institutions: Ministère de la Santé, Institut de Veille Sanitaire
(InVS), Institut National de la Prévention et de l’Education pour la
Santé (INPES), Région Ile-de-France (CORDDIM), Institut National de
la Santé et de la Recherche Médicale (INSERM), Institut National de
la Recherche Agronomique (INRA), Conservatoire National des Arts
et Métiers (CNAM), and Université Paris 13. MD and PF were funded
by a PhD grant from the Cancéropôle Ile de France/Région Ile de
France (public funding). BS was funded by the French National Cancer
Institute (grant number INCa_8085). Researchers were independent
from funders. Funders had no role in the study design; the collection,
analysis, and interpretation of data; the writing of the report; or the
decision to submit the article for publication.
Competing interests: All authors have completed the ICMJE uniform
disclosure form at (available on
request from the corresponding author) and declare: no support from
any organisation for the submitted work other than that described
above; no nancial relationships with any organisations that might
have an interest in the submitted work in the previous three years; no
other relationships or activities that could appear to have influenced
the submitted work.
Ethical approval: The NutriNet-Santé study was approved by the
Institutional Review Board of the French Institute for Health and
Medical Research (IRB Inserm No 0000388FWA00005831) and the
Commission Nationale de l’Informatique et des Libertés (CNIL No
908450/No 909216). Electronic informed consent was obtained
from each participant.
Transparency statement: MT (the guarantor) arms that the
manuscript is an honest, accurate, and transparent account of the
study being reported; that no important aspects of the study have
been omitted; and that any discrepancies from the study as planned
(and, if relevant, registered) have been explained.
Data sharing: No additional data available.
This is an Open Access article distributed in accordance with the
Creative Commons Attribution Non Commercial (CC BY-NC 4.0)
license,which permits others to distribute, remix, adapt, build upon
this work non-commercially, and license their derivative works on
dierent terms, provided the original work is properly cited and the
use is non-commercial. See:
1 FerlayJ, SoerjomataramI, DikshitR, et al. Cancer incidence and
mortality worldwide: sources, methods and major patterns in
GLOBOCAN 2012. Int J Cancer2015;136:E359-86. doi:10.1002/
2 World Cancer Research Fund International/American Institute for
Cancer Research. Cancer preventability estimates for diet, nutrition,
body fatness, and physical activity. 2017.
3 Latino-MartelP, CottetV, Druesne-PecolloN, et al. Alcoholic
beverages, obesity, physical activity and other nutritional
factors, and cancer risk: A review of the evidence. Crit Rev Oncol
Hematol2016;99:308-23. doi:10.1016/j.critrevonc.2016.01.002
4 ANSES (French Agency for Food, Environmental and Occupational
Health & Safety). Étude individuelle nationale des consommations
alimentaires 3 (INCA 3). 2017.
5 MonteiroCA, MoubaracJC, CannonG, NgSW, PopkinB. Ultra-
processed products are becoming dominant in the global food
system. Obes Rev2013;14(Suppl 2):21-8. doi:10.1111/
10 doi: 10.1136/bmj.k322 |
2018;360:k322 | thebmj
6 MoodieR, StucklerD, MonteiroC, et al, Lancet NCD Action
Group.Prots and pandemics: prevention of harmful eects of
tobacco, alcohol, and ultra-processed food and drink industries.
Lancet2013;381:670-9. doi:10.1016/S0140-6736(12)62089-3
7 MoubaracJC, BatalM, MartinsAP, et al. Processed and ultra-
processed food products: consumption trends in Canada
from 1938 to 2011. Can J Diet Pract Res2014;75:15-21.
8 VennD, BanwellC, DixonJ. Australia’s evolving food practices: a risky
mix of continuity and change. Public Health Nutr2017;20:2549-58.
9 MonteiroCA, CannonG, MoubaracJC, LevyRB, LouzadaMLC,
JaimePC. The UN Decade of Nutrition, the NOVA food classication
and the trouble with ultra-processing. Public Health Nutr2018;21:5-
17. doi:10.1017/S1368980017000234
10 LuitenCM, SteenhuisIH, EylesH, Ni MhurchuC, WaterlanderWE.
Ultra-processed foods have the worst nutrient prole, yet they are
the most available packaged products in a sample of New Zealand
supermarkets--CORRIGENDUM. Public Health Nutr2016;19:539.
11 AdamsJ, WhiteM. Characterisation of UK diets according to degree
of food processing and associations with socio-demographics
and obesity: cross-sectional analysis of UK National Diet and
Nutrition Survey (2008-12). Int J Behav Nutr Phys Act2015;12:160.
12 CedielG, ReyesM, da Costa LouzadaML, et al. Ultra-processed
foods and added sugars in the Chilean diet (2010). Public Health
Nutr2018;21:125-33. doi:10.1017/S1368980017001161
13 Costa LouzadaML, MartinsAP, CanellaDS, et al. Ultra-processed
foods and the nutritional dietary prole in Brazil. Rev Saude
14 Martínez SteeleE, BaraldiLG, LouzadaML, MoubaracJC,
MozaarianD, MonteiroCA. Ultra-processed foods and added
sugars in the US diet: evidence from a nationally representative
cross-sectional study. BMJ Open2016;6:e009892. doi:10.1136/
15 MoubaracJC, MartinsAP, ClaroRM, LevyRB, CannonG, MonteiroCA,
Evidence from Canada. Consumption of ultra-processed foods and
likely impact on human health. Public Health Nutr2013;16:2240-8.
16 MoubaracJC, BatalM, LouzadaML, Martinez SteeleE,
MonteiroCA. Consumption of ultra-processed foods predicts diet
quality in Canada. Appetite2017;108:512-20. doi:10.1016/j.
17 PotiJM, MendezMA, NgSW, PopkinBM. Is the degree of food
processing and convenience linked with the nutritional quality of
foods purchased by US households? Am J Clin Nutr2015;101:1251-
62. doi:10.3945/ajcn.114.100925
18 SlimaniN, DeharvengG, SouthgateDA, et al. Contribution of highly
industrially processed foods to the nutrient intakes and patterns of
middle-aged populations in the European Prospective Investigation
into Cancer and Nutrition study. Eur J Clin Nutr2009;63(Suppl
4):S206-25. doi:10.1038/ejcn.2009.82
19 LouzadaML, MartinsAP, CanellaDS, et al. Impact of ultra-processed
foods on micronutrient content in the Brazilian diet. Rev Saude
20 Panel on Contaminants in the Food Chain. Acrylamide in food. EFSA
21 MunckeJ. Endocrine disrupting chemicals and other substances
of concern in food contact materials: an updated review of
exposure, eect and risk assessment. J Steroid Biochem Mol
Biol2011;127:118-27. doi:10.1016/j.jsbmb.2010.10.004
22 European Union. Database of authorized food additives. 2008.
23 BouvardV, LoomisD, GuytonKZ, et al, International Agency for
Research on Cancer Monograph Working Group. Carcinogenicity
of consumption of red and processed meat. Lancet
Oncol2015;16:1599-600. doi:10.1016/S1470-2045(15)00444-1
24 IARC Working Group on the Evaluation of Carcinogenic Risks to
Humans. Carbon black, titanium dioxide, and talc. IARC Monogr Eval
Carcinog Risks Hum2010;93:1-413.
25 CanellaDS, LevyRB, MartinsAP, et al. Ultra-processed food
products and obesity in Brazilian households (2008-2009). PLoS
One2014;9:e92752. doi:10.1371/journal.pone.0092752
26 JuulF, HemmingssonE. Trends in consumption of ultra-processed
foods and obesity in Sweden between 1960 and 2010. Public
HealthNutr2015;18:3096-107. doi:10.1017/S1368980015000506
27 LouzadaML, BaraldiLG, SteeleEM, et al. Consumption of ultra-
processed foods and obesity in Brazilian adolescents and adults.
Prev Med2015;81:9-15. doi:10.1016/j.ypmed.2015.07.018
28 RauberF, CampagnoloPD, HomanDJ, VitoloMR. Consumption
of ultra-processed food products and its eects on children’s
lipid proles: a longitudinal study. Nutr Metab Cardiovasc
Dis2015;25:116-22. doi:10.1016/j.numecd.2014.08.001
29 MendonçaRD, PimentaAM, GeaA, et al. Ultraprocessed
food consumption and risk of overweight and obesity: the
University of Navarra Follow-Up (SUN) cohort study. Am J Clin
Nutr2016;104:1433-40. doi:10.3945/ajcn.116.135004
30 MendonçaRD, LopesAC, PimentaAM, GeaA, Martinez-
GonzalezMA, Bes-RastrolloM. Ultra-Processed Food Consumption
and the Incidence of Hypertension in a Mediterranean Cohort:
The Seguimiento Universidad de Navarra Project. Am J
31 HercbergS, CastetbonK, CzernichowS, et al. The Nutrinet-Santé
Study: a web-based prospective study on the relationship
between nutrition and health and determinants of dietary
patterns and nutritional status. BMC Public Health2010;10:242.
32 VergnaudAC, TouvierM, MéjeanC, et al. Agreement between web-
based and paper versions of a socio-demographic questionnaire
in the NutriNet-Santé study. Int J Public Health2011;56:407-17.
33 LassaleC, PéneauS, TouvierM, et al. Validity of web-based self-
reported weight and height: results of the Nutrinet-Santé study. J Med
Internet Res2013;15:e152. doi:10.2196/jmir.2575
34 TouvierM, MéjeanC, Kesse-GuyotE, et al. Comparison between
web-based and paper versions of a self-administered anthropometric
questionnaire. Eur J Epidemiol2010;25:287-96. doi:10.1007/
35 CraigCL, MarshallAL, SjöströmM, et al. International physical
activity questionnaire: 12-country reliability and validity.
Med Sci Sports Exerc2003;35:1381-95. doi:10.1249/01.
36 TouvierM, Kesse-GuyotE, MéjeanC, et al. Comparison between
an interactive web-based self-administered 24 h dietary record
and an interview by a dietitian for large-scale epidemiological
studies. Br J Nutr2011;105:1055-64. doi:10.1017/
37 LassaleC, CastetbonK, LaporteF, et al. Validation of a Web-based,
self-administered, non-consecutive-day dietary record tool against
urinary biomarkers. Br J Nutr2015;113:953-62. doi:10.1017/
38 LassaleC, CastetbonK, LaporteF, et al. Correlations between Fruit,
Vegetables, Fish, Vitamins, and Fatty Acids Estimated by Web-
Based Nonconsecutive Dietary Records and Respective Biomarkers
of Nutritional Status. J Acad Nutr Diet2016;116:427-438.e5.
39 Le MoulencN, DeheegerM, PrezioziP, et al. Validation du
manuel photo utilisé pour l’enquête alimentaire de l’étude SU.VI.
MAX[Validation of the food portion size booklet used in the SU.VI.
MAX study]. Cahiers de Nutrition et de Diététique1996;31:158-64.
40 BlackAE. Critical evaluation of energy intake using the Goldberg
cut-o for energy intake:basal metabolic rate. A practical guide
to its calculation, use and limitations. Int J Obes Relat Metab
Disord2000;24:1119-30. doi:10.1038/sj.ijo.0801376
41 Etude Nutrinet Santé. Table de composition des aliments, étude
NutriNet-Santé [Food composition table, NutriNet-Santé study]. Les
éditions INSERM/Economica, 2013.
42 MonteiroCA, CannonG, LevyRB, et al. NOVA. The star shines bright.
World Nutrition2016;7:28-38.
43 MoubaracJC, ParraDC, CannonG, MonteiroCA. Food Classication
Systems Based on Food Processing: Signicance and Implications
for Policies and Actions: A Systematic Literature Review and
Assessment. Curr Obes Rep2014;3:256-72. doi:10.1007/s13679-
44 JuliaC, MartinezL, AllèsB, et al. Contribution of ultra-processed foods
in the diet of adults from the French NutriNet-Santé study. Public
Health Nutr2018;21:27-37. doi:10.1017/S1368980017001367
45 LangeT, VansteelandtS, BekaertM. A simple unied approach
for estimating natural direct and indirect eects. Am J
Epidemiol2012;176:190-5. doi:10.1093/aje/kwr525
46 SterneJA, WhiteIR, CarlinJB, et al. Multiple imputation for missing
data in epidemiological and clinical research: potential and pitfalls.
BMJ2009;338:b2393. doi:10.1136/bmj.b2393
47 FardetA. Minimally processed foods are more satiating and less
hyperglycemic than ultra-processed foods: a preliminary study with
98 ready-to-eat foods. Food Funct2016;7:2338-46. doi:10.1039/
48 World Health Organization. Food additives. 2017. http://www.who.
49 US Food and Drug Administration. Food additive status list.
50 ChangX, ZhangY, TangM, WangB. Health eects of exposure to
nano-TiO2: a meta-analysis of experimental studies. Nanoscale Res
Lett2013;8:51. doi:10.1186/1556-276X-8-51
51 Panel on Food Additives and Nutrient Sources Added to Food.
Opinion on the re-evaluation of aspartame (E 951) as a food
additive. EFSA Journal2013;11:3496.
No commercial reuse: See rights and reprints Subscribe:
52 SantarelliRL, VendeuvreJL, NaudN, et al. Meat processing and colon
carcinogenesis: cooked, nitrite-treated, and oxidized high-heme
cured meat promotes mucin-depleted foci in rats. Cancer Prev Res
(Phila)2010;3:852-64. doi:10.1158/1940-6207.CAPR-09-0160
53 SuezJ, KoremT, ZeeviD, et al. Articial sweeteners induce glucose
intolerance by altering the gut microbiota. Nature2014;514:181-6.
54 Virk-BakerMK, NagyTR, BarnesS, GroopmanJ. Dietary acrylamide
and human cancer: a systematic review of literature. Nutr
Cancer2014;66:774-90. doi:10.1080/01635581.2014.
55 European Chemical Agency (ECHA). Member State Committee
support document for identication of 4,4’-isopropylidenediphenol
(bisphenol a) as a substance of very high concern because of its toxic
for reproduction (Article 57 c) properties. Adopted on 2 December
56 AndreevaVA, SalanaveB, CastetbonK, et al. Comparison of
the sociodemographic characteristics of the large NutriNet-
Santé e-cohort with French Census data: the issue of volunteer
bias revisited. J Epidemiol Community Health2015;69:893-8.
57 Institut National du Cancer. Les Cancer en France. 2014. http://les/Les%20cancers%20en%20
58 Ministry of Health of Brazil. Dietary guidelines for the Brazilian
population.Ministry of Health of Brazil, 2014.
59 Haut Conseil de la Santé Publique. Avis relatif à la révision des
repères alimentaires pour les adultes du futur Programme National
Nutrition Santé 2017-2021, 2017.
Appendix 1-6
... Ultra-processed foods (UPFs), as defined by the NOVA classification system, are formulations of ingredients derived from very little or no whole foods, combined with substances such as taste enhancers, colorants, and other food additives, intended to enhance the cosmetic properties or shelf life of the product [1]. UPF consumption drives much of the intake of potentially harmful foods (e.g., ultra-processed meat), nutrients (e.g., salt, sugar), certain food additives, and other compounds (e.g., endocrine-disrupting chemicals (EDCs) such as phthalates and bisphenols) [2,3]. ...
... UPF consumption drives much of the intake of potentially harmful foods (e.g., ultra-processed meat), nutrients (e.g., salt, sugar), certain food additives, and other compounds (e.g., endocrine-disrupting chemicals (EDCs) such as phthalates and bisphenols) [2,3]. Large cohort studies and national surveys using the NOVA system have linked UPF intake with cancer [1], cardiovascular disease [2], and total mortality [3,4] as well as other health outcomes [5]. ...
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PurposeUltra-processed food (UPF), as defined by the NOVA classification, is related to lower diet quality, which may adversely affect maternal health and neonatal outcomes. This study aims to describe nutrient intake of pregnant women by the share of UPF in the diet and to identify associations between UPF intake and maternal and neonatal outcomes.Methods In this cross-sectional study, pregnant women (n = 206) were recruited upon arrival to the obstetrics ward for delivery, and asked to complete a Food Frequency Questionnaire (FFQ), and questionnaires regarding environmental exposures, and socio-demographic characteristics. Neonatal measurements and clinical data were obtained following delivery. UPF energy intake was expressed as absolute and in terms of percent from total energy. Women with high intake of energy from UPF were compared to those with low intake.ResultsAmong 206 pregnant women, dietary intake of UPF ranged from 15.6% to 43.4% of total energy in the first and fourth quartiles of UPF consumption, respectively. Women in the fourth quartile of energy from UPF had lower intakes of vitamin C, beta-carotene, vitamin B6, and potassium, which is indicative of inferior diet quality. Percent energy from UPF was associated with maternal obesity (BMI ≥ 30) (OR = 1.06, 95% CI: 1.06, 1.10, p = 0.008) and shorter male infant ano-genital distance (AGD) (B = −1.9, 95% CI: −3.5, −0.24, p = 0.02).ConclusionsUPF intake during pregnancy is associated with undesirable maternal and neonatal outcomes and more research is needed to confirm these findings.
... In addition to moral and sustainable aspects, health factors are also among the motives [2]. Recent reviews have shown that the increased consumption of animal-based foods, especially red meat and highly processed products, is related to higher risks of various types of cancer [3][4][5][6]. Consequently, a vegetarian or vegan diet is often associated with a healthy diet. So far, a vegan diet has only been scientifically studied regarding the health aspects. ...
Full-text available
Over the past few years, the number of people who have avoided animal products has been rising steadily. A plant-based diet is associated with a healthier lifestyle and has positive effects on various diseases. More and more healthy active people and performance-orientated athletes are giving up animal products for various reasons, such as for an improved performance or faster regeneration. However, the data in this context are limited. This study aimed to obtain initial findings on the influence of a diet change to veganism on the performance of strength-trained individuals. For this study, a total of 15 omnivorous individuals were recruited. They documented their dietary food intakes over 16 weeks. Every four weeks, the strength performance was tested via a leg press and bench press. In the first 8 weeks, the participants maintained their omnivorous diet, followed by 8 weeks of a vegan dietary phase. In total, 10 subjects participated successfully, and their data were part of the statistical analyses. There was no difference in the absolute and relative strength performance for the leg and bench press after changing to a vegan diet. For the total calorie intake and carbohydrates, only a small treatment effect, but no time effect, was observed. However, for the protein intake, a time and group effect were detected. In addition, the relative protein intake decreased significantly and was lower than the current recommendations for athletes. The results demonstrate that a change to a vegan diet has no beneficial nor negative effect on the strength performance when the total calorie intake and carbohydrate content are covered in the first 8 weeks.
... Studies have shown that a higher degree of food processing is associated with the development of NCDs, constituting a risk factor for hypertension [13], obesity [14,15], cancer [16], and MetS [17]. This fact is attributed to the high content of sodium, refined carbohydrates, saturated fats, and trans fats often present in these foods. ...
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Background The processing of food can cause changes that turn them into risk factors for chronic diseases. A higher degree of food processing is associated with the development of chronic non-communicable diseases (NCDs), including the metabolic syndrome (MetS). The objective of this study was to analyze the relationship between ultra-processed food (UPF) consumption and the prevalence of MetS and its risk factors, focusing on a population group especially subjected to precarious living conditions and food insecurity. Method Cross-sectional population-based study with women (19 to 59 years) from Quilombola communities of Alagoas. The socioeconomic, demographic, anthropometric, health status, lifestyle, and food intake (24-h recall) variables were analyzed. The dependent variable was the MetS, defined using the harmonization criteria of the Joint Interim Statement, and its components. The foods consumed were categorized according to the Nova Classification, assuming the highest UPF consumption as risk exposure. The measure of association was the prevalence ratio (PR) and respective 95%CI, calculated by Poisson regression with robust variance. We also analyzed the association with the Nova score of UPF consumption. Results We investigated 895 women (38.9 ± 11.0 years), of whom 48.3% had MetS. On average, 15.9% of the total energy intake came from UPF. Lower Nova scores were associated with a lower prevalence of diabetes and low HDL. Higher UPF consumption was associated with a 30% higher prevalence of hypertension (PR = 1.30; 95%CI: 1.06–1.61). Conclusion The highest UPF consumption was positively associated with the prevalence of hypertension, while a lower Nova score was a protective factor against diabetes and low HDL. UPF consumption in Quilombola communities is important but lower than that observed in the Brazilian population in general. It is suggested that public health programs be implemented to promote healthy eating while valuing the existing eating habits and traditions among the remaining Brazilian Quilombola people.
... Ultra-processed food consumption has been linked to an increased risk of cardiovascular disease (Srour et al., 2019) and several types of cancer (Fiolet et al., 2018). However, canned or processed food is not necessarily unhealthy, and it is not always the processing that makes the food less healthy (Meijer et al., 2021). ...
Full-text available
Abstract This study aimed to assess the food-related attitudes through the application of a structured questionnaire to 1,000 individuals applied in three environments (groups) in the Federal District of Brazil (supermarkets, universities and hospitals/clinics) using multivariate logistic regression, with special focus on pesticides and genetically modified (GM) food. Outpatients in hospital/clinic, women and older individuals were significantly more likely to adopt diets or attitudes considered or perceived as healthy (including high consumption of fruits and vegetables, acquiring organic food, and adopting procedures to remove pesticide residues from food). When income and/or education impacted the results, the correlation was negative. Over 60% of the population believe it is possible to produce food without using pesticides, mainly the hospital/clinic group, younger individuals, and women, and 95.3% think that the presence of pesticides in food should be indicated on the labels, mainly the hospital/clinic group and older individuals. High worry about pesticides and GM food was associated with most healthy food-related attitudes. The results of this study are important for food-related communication strategies conducted by health authorities, aiming at driving specific population segments to a healthier and more conscious diet.
... A population-based cohort study was done by Fiolet et al, to assess the prospective associations between the consumption of UPF and the risk of cancer [24], and suggested that there is a positive association between industrially processed meat and the risk of CRC, colon cancer, and rectal cancer. ...
... Ultra-processed foods have undergone multiple biological and chemical (for example, the addition of food preservatives) processes to become palatable and affordable (Fiolet et al., 2018). The National Research Council (US) Committee on Diet, Nutrition and Cancer reported that over 2500 chemicals are added to foods to alter flavour, taste, colour and cost. ...
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It was estimated the proportion and number of invasive cancer cases and deaths for 26 cancer types in adults aged 30 years and older in the United States in 2014. In this study was found that these cancers were attributable to modifiable risk factors such as cigarette smoking, second-hand smoke, alcohol intake, physical inactivity, excess body weight, red and processed meat consumption, low consumption of vegetables and fruits, dietary calcium, and ultraviolet radiation and six cancer-associated infections. Several databases were reviewed including PUBMED, Google scholar, and Web of Science. The facts suggest that a number of individuals in the US present risk factors for cancer, which development in malignancies would ultimately depend on the interaction of environmental and genetic factors.
... With regards to excessive consumption saturated fat, similar observations were made [99]. It was found that, the risk of breast cancer 11% increases with the 10% increase of ultra-processed food [100]. Conversely, a diet rich in lean protein, fruits, vegetables, whole grains and legumes is connected with a lowered risk of breast cancer [101]. ...
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Among the cancer diseases, breast cancer is becoming fast growing leading cause of oncologic mortality among women. It is hoped that, evaluation of different dimensions of breast cancer and its associated factors such as environmental and genetic factors, will increase the efforts of prevention. Current studies suggest that mutations in some genes play an crucial role in susceptibility of breast cancer. Thus, researchers aimed to isolate such breast cancer susceptible genes. Some epidemiologic studied also suggested that, moderate to vigorous exercise for 3-4 hours per week can lower the risk for breast cancer for 30%-40% in women than those of the sedentary women. Obese or overweight women have 50%-250% greater risk for breast cancer after menopause. Alcohol use also increases the risk of both postmenopausal and premenopausal breast cancer. Thus, these trends of sedentary lifestyle, decreasing physical activity and increasing obesity may lead to an increase in the incidence of breast cancer. Thus, adoption of healthy food habits, increasing physical activity and changes in sedentary lifestyle may have a great impact on the prevalence of this disease in future. The treatment strategies for breast cancer are broad-ranging depending on tumor’s biology; stages of the disease; tolerance, and acceptance of the patient. There is various treatment approaches of breast cancer such as radiology, surgery, and systematic therapy (chemotherapy, endocrine therapy and biological therapy) etc. In this article required information about risk factors and current breast cancer prevention strategies are collected through literature review.
Nutritional problems occur very frequently in patients with cancer and different problems are associated with separate phases of the disease. Therefore, it is principally recommended to regularly screen all patients with cancer for nutritional disorders and in the case of conspicuous results meticulous diagnostics should be carried to clarify the underlying causes. The focus is on food intake and possible disturbing complaints, the physical performance index, nutritional status including weight change and body composition, the metabolic pattern and the presence of a systemic inflammatory reaction. As anti-cancer treatments frequently induce gastrointestinal derangements which endanger adequate food intake, individualized nutritional care should be offered routinely. After successful curative treatment patients are at risk of developing a metabolic syndrome; therefore, a balanced diet and regular physically activity are recommended. During palliative treatment special attention should be paid to the development of malnutrition. Patients are particularly endangered by cachexia with the combination of inadequate food intake, inactivity and prevailing catabolism. The treatment of cachexia requires a multiprofessional approach to ensure adequate food intake, guiding and supporting physical activity and interventions for normalization of the metabolic situation. In addition, the need for psychological and social support should be discussed. Dietary supplements are of minor relevance; however, deficits in micronutrients, such as vitamins and trace elements need to be balanced. At the end of life, care should primarily be focused on alleviating debilitating symptoms. To reliably support all patients affected by nutritional disorders, clear structures need to be established, responsibilities assigned and standardized procedures codified.
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El patrón alimentario promovido y dominante en la actualidad, se caracteriza por una fuerte industrialización y presencia de productos comestibles ultraprocesados cuyo perfil nutricional afecta la salud, y además, genera implicaciones social, cultural, económica y ambientalmente negativas, asociadas a las formas de producción, procesamiento, distribución y consumo de estos productos, propiciando un distanciamiento entre el ser humano y el alimento, deteriorando la cultura alimentaria e invisibilizando la alimentación como hecho social. En este documento se reflexiona sobre la importancia de migrar hacia un patrón alimentario basado en alimentos reales y se esboza la alimentación real como propuesta parael análisis de los asuntos alimentarios y nutricionales. La alimentación real es una concepción vanguardista con poca teorización, podría identificarse como un patrón de alimentación que supera la visión limitada del nutricionismo, a su vez, representa unbajo impacto ambiental, es pertinente desde el punto de vista sociocultural y promueve la adopción de estilos de vida saludables
Background: Obesity is currently a major public health issue in all over the world. Food is important for survival. Consumption of fast foods has become almost a global phenomenon. India’s fast-food industry is expanding at the rate of 40% every year. India ranks 10th in the fast-food per capita spending with 2.1% of expenditure in annual total spending1. Aim: The aim of the study was to identify the level of knowledge, attitude and practice regarding Neighborhood fast food availability and fast-food consumption among households in selected urban area, in Puducherry”, to find out the correlation between knowledge and attitude regarding neighbourhood fast food availability and fast-food consumption among households and to find association of level of knowledge and attitude regarding neighbourhood fast food availability and fast-food consumption among households with selected demographic variables. Subjects and Methods: A descriptive cross sectional research was conducted among 50households in Lawspet area Puducherry, by convenience sampling technique, quantitative approach. Data was collected by using self-structured questionnaire developed by the investigator. Interview method was used to collect socio demographic data and to assess the level of knowledge, attitude and practice regarding Neighborhood fast food availability and fast-food consumption. Results: The study result shows that 50 households out of 34(68%) had moderate knowledge, 35(70%) had positive attitude, 21(42%) of them prefer favourite fast-food place is road side shop/restaurants, 2(40%) used to spend on fast food daily a average amount of above Rs.80 and 31(62%) were aware about fast foods through advertisement, social media and by the newspaper. Conclusion: The study concluded that households in urban area prefer fast food that there was a moderate knowledge, had positive attitude, eat fast food few times a month, favourite fast-food place prefer at road side shop/restaurants, Everyone spend Rs. 80/- on fast food daily aware about fast foods consumptions and In effect but prefer more fast food leads obesity. Community health nurse play effective role in teaching public about the effect of fast-food consumption and avoidance of fast foods. Researcher created awareness to to avoid fast foods and fast-food consumption to all households by distributing pamphlets.
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Objective To assess the consumption of ultra-processed foods and analyse its association with the content of added sugars in the Chilean diet. Design Cross-sectional study of national dietary data obtained through 24 h recalls and classified into food groups according to the extent and purpose of food processing (NOVA classification). Setting Chile. Subjects A probabilistic sample of 4920 individuals (aged 2 years or above) studied in 2010 by a national dietary survey (Encuesta Nacional de Consumo Alimentario). Results Ultra-processed foods represented 28·6 ( se 0·5) % of total energy intake and 58·6 ( se 0·9) % of added sugars intake. The mean percentage of energy from added sugars increased from 7·7 ( se 0·3) to 19·7 ( se 0·5) % across quintiles of the dietary share of ultra-processed foods. After adjusting for several potential sociodemographic confounders, a 5 percentage point increase in the dietary share of ultra-processed foods determined a 1 percentage point increase in the dietary content of added sugars. Individuals in the highest quintile were three times more likely (OR=2·9; 95 % CI 2·4, 3·4) to exceed the 10 % upper limit for added sugars recommended by the WHO compared with those in the lowest quintile, after adjusting for sociodemographic variables. This association was strongest among individuals aged 2–19 years (OR=3·9; 95 % CI 2·7, 5·9). Conclusions In Chile, ultra-processed foods are important contributors to total energy intake and to the consumption of added sugars. Actions aimed at limiting consumption of ultra-processed foods are being implemented as effective ways to achieve WHO dietary recommendations to limit added sugars and processed foods, especially for children and adolescents.
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Given evident multiple threats to food systems and supplies, food security, human health and welfare, the living and physical world and the biosphere, the years 2016–2025 are now designated by the UN as the Decade of Nutrition, in support of the UN Sustainable Development Goals. For these initiatives to succeed, it is necessary to know which foods contribute to health and well-being, and which are unhealthy. The present commentary outlines the NOVA system of food classification based on the nature, extent and purpose of food processing. Evidence that NOVA effectively addresses the quality of diets and their impact on all forms of malnutrition, and also the sustainability of food systems, has now accumulated in a number of countries, as shown here. A singular feature of NOVA is its identification of ultra-processed food and drink products. These are not modified foods, but formulations mostly of cheap industrial sources of dietary energy and nutrients plus additives, using a series of processes (hence ‘ultra-processed’). All together, they are energy-dense, high in unhealthy types of fat, refined starches, free sugars and salt, and poor sources of protein, dietary fibre and micronutrients. Ultra-processed products are made to be hyper-palatable and attractive, with long shelf-life, and able to be consumed anywhere, any time. Their formulation, presentation and marketing often promote overconsumption. Studies based on NOVA show that ultra-processed products now dominate the food supplies of various high-income countries and are increasingly pervasive in lower middle- and upper-middle-income countries. The evidence so far shows that displacement of minimally processed foods and freshly prepared dishes and meals by ultra-processed products is associated with unhealthy dietary nutrient profiles and several diet-related non-communicable diseases. Ultra-processed products are also troublesome from social, cultural, economic, political and environmental points of view. We conclude that the ever-increasing production and consumption of these products is a world crisis, to be confronted, checked and reversed as part of the work of the UN Sustainable Development Goals and its Decade of Nutrition. (NOVA, Ultra-processing)
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Background: Some available evidence suggests that high consumption of ultra-processed foods (UPFs) is associated with a higher risk of obesity. Collectively, this association and the nutritional characteristics of UPFs suggest that UPFs might also be associated with hypertension. Methods: We prospectively evaluated the relationship between UPF consumption and the risk of hypertension in a prospective Spanish cohort, the Seguimiento Universidad de Navarra project. We included 14,790 Spanish adult university graduates who were initially free of hypertension at baseline who were followed for a mean of 9.1 years (SD, 3.9 years; total person-years: 134,784). UPF (industrial formulations of chemical compounds which, beyond substances of common culinary use such as salt, sugar, oils, and fats, include substances also derived from foods but not used in culinary preparations) consumption was assessed using a validated semi-quantitative 136-item food-frequency questionnaire. Cox proportional hazards models were used to estimate adjusted hazard ratios (HRs) and 95% confidence intervals (CIs) for hypertension incidence. Results: During follow-up, 1,702 incident cases of hypertension were identified. Participants in the highest tertile of UPF consumption had a higher risk of developing hypertension (adjusted HR, 1.21; 95% CI, 1.06, 1.37; P for trend = 0.004) than those in the lowest tertile after adjusting for potential confounders. Conclusions: In this large prospective cohort of Spanish middle-aged adult university graduates, a positive association between UPF consumption and hypertension risk was observed. Additional longitudinal studies are needed to confirm our results.
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Background: Ultraprocessed food consumption has increased in the past decade. Evidence suggests a positive association between ultraprocessed food consumption and the incidence of overweight and obesity. However, few prospective studies to our knowledge have investigated this potential relation in adults. Objective: We evaluated the association between ultraprocessed food consumption and the risk of overweight and obesity in a prospective Spanish cohort, the SUN (University of Navarra Follow-Up) study. Design: We included 8451 middle-aged Spanish university graduates who were initially not overweight or obese and followed up for a median of 8.9 y. The consumption of ultraprocessed foods (defined as food and drink products ready to eat, drink, or heat and made predominantly or entirely from processed items extracted or refined from whole foods or synthesized in the laboratory) was assessed with the use of a validated semiquantitative 136-item food-frequency questionnaire. Cox proportional hazards models were used to estimate adjusted HRs and 95% CIs for incident overweight and obesity. Results: A total of 1939 incident cases of overweight and obesity were identified during follow-up. After adjustment for potential confounders, participants in the highest quartile of ultraprocessed food consumption were at a higher risk of developing overweight or obesity (adjusted HR: 1.26; 95% CI: 1.10, 1.45; P-trend = 0.001) than those in the lowest quartile of consumption. Conclusions: Ultraprocessed food consumption was associated with a higher risk of overweight and obesity in a prospective cohort of Spanish middle-aged adult university graduates. Further longitudinal studies are needed to confirm our results. This trial was registered at as NCT02669602.
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Objective To investigate trends in five key aspects of Australian food practice which have been implicated in diet-related health risks, specifically energy intake. They are: the replacement of home-prepared foods by commercially prepared foods; consumer reliance on ultra-processed foods; de-structured dining; increased pace of eating; and a decline in commensal eating. Design Data were from repeated cross-sections from the national Household Expenditure and Time Use Surveys. Trends in food practice aspects were examined using indicators of food expenditure across different food groups and time spent eating and cooking, including where, when and with whom eating activities took place. Setting Australia, 1989–2010. Subjects Nationally representative samples of Australian households. Results The share of the total food budget spent on food away from home rose steadily from 22·8 % in 1989 to 26·5 % in 2010, while spending on ultra-processed foods increased. The basic patterning of meals and the pace of eating changed little, although people spent more time eating alone and at restaurants. Cooking time declined considerably, particularly for women. Conclusions These changes have occurred over the same time that obesity and diet-related, non-communicable diseases have increased rapidly in Australia. Some aspects are implicated more than others: particularly the shift from domestic cooking to use of pre-prepared and ultra-processed foods, a reduction in time spent in food preparation and cooking, as well as an upsurge in time and money devoted to eating away from home. These are all likely to operate through the higher energy content of commercially prepared, compared with unprocessed or lightly processed, foods.
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Beyond nutritional composition, food structure is increasingly recognized to play a role in food health potential, notably in satiety and glycemic responses. Food structure is also highly dependent on processing conditions. The hypothesis for this study is, based on a data set of 98 ready-to-eat foods, that the degree of food processing would correlate with the satiety index (SI) and glycemic response. Glycemic response was evaluated according to two indices: the glycemic index (GI) and a newly designed index, the glycemic glucose equivalent (GGE). The GGE indicates how a quantity of a certain food affects blood glucose levels by identifying the amount of food glucose that would have an effect equivalent to that of the food. Then, foods were clustered within three processing groups based on the international NOVA classification: (1) raw and minimally processed foods; (2) processed foods; and (3) ultra-processed foods. Ultra-processed foods are industrial formulations of substances extracted or derived from food and additives, typically with five or more and usually many (cheap) ingredients. The data were correlated by nonparametric Spearman's rank correlation coefficient on quantitative data. The main results show strong correlations between GGE, SI and the degree of food processing, while GI is not correlated with the degree of processing. Thus, the more food is processed, the higher the glycemic response and the lower its satiety potential. The study suggests that complex, natural, minimally and/or processed foods should be encouraged for consumption rather than highly unstructured and ultra-processed foods when choosing weakly hyperglycemic and satiating foods.
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Objectives To investigate the contribution of ultra-processed foods to the intake of added sugars in the USA. Ultra-processed foods were defined as industrial formulations which, besides salt, sugar, oils and fats, include substances not used in culinary preparations, in particular additives used to imitate sensorial qualities of minimally processed foods and their culinary preparations. Design Cross-sectional study. Setting National Health and Nutrition Examination Survey 2009–2010. Participants We evaluated 9317 participants aged 1+ years with at least one 24 h dietary recall. Main outcome measures Average dietary content of added sugars and proportion of individuals consuming more than 10% of total energy from added sugars. Data analysis Gaussian and Poisson regressions estimated the association between consumption of ultra-processed foods and intake of added sugars. All models incorporated survey sample weights and adjusted for age, sex, race/ethnicity, family income and educational attainment. Results Ultra-processed foods comprised 57.9% of energy intake, and contributed 89.7% of the energy intake from added sugars. The content of added sugars in ultra-processed foods (21.1% of calories) was eightfold higher than in processed foods (2.4%) and fivefold higher than in unprocessed or minimally processed foods and processed culinary ingredients grouped together (3.7%). Both in unadjusted and adjusted models, each increase of 5 percentage points in proportional energy intake from ultra-processed foods increased the proportional energy intake from added sugars by 1 percentage point. Consumption of added sugars increased linearly across quintiles of ultra-processed food consumption: from 7.5% of total energy in the lowest quintile to 19.5% in the highest. A total of 82.1% of Americans in the highest quintile exceeded the recommended limit of 10% energy from added sugars, compared with 26.4% in the lowest. Conclusions Decreasing the consumption of ultra-processed foods could be an effective way of reducing the excessive intake of added sugars in the USA.
Objective Concerns have been raised about the potential health impact of ultra-processed foods (UPF) in the diet. Our objective was to investigate the contribution of UPF in the diet in a large French population and its association with sociodemographic factors and dietary patterns. Design Cross-sectional analysis of dietary data from 74 470 participants in the web-based NutriNet-Santé cohort. UPF were identified in repeated 24 h records and the proportion (in weight) of UPF in the total diet (UPFp) was computed for each participant. Associations of sociodemographic characteristics and UPFp in quartiles were assessed using multivariate multinomial logistic regression. Food group consumption and nutrient intakes across quartiles of UPFp were estimated using linear regression adjusted for sociodemographic factors and energy intake. Setting France. Results UPF contributed 18·4 % of the foods consumed in weight and 35·9 % of total energy intake. Higher UPFp consumption was independently associated with male gender, younger age, lower education, smoking, and overweight and obesity (all P <0·0001). Participants in the highest UPFp quartile consumed lower amounts of fruit and vegetables (difference between quartile 4 and quartile 1 of UPFp, Δ=−180·3 g/d) and higher amounts of sweet products (Δ=68·5 g/d) and soft drinks (Δ=98·6 g/d; all P <0·0001). They had higher intakes of energy (Δ=610 kJ/d (145·7 kcal/d)) and added sugar (Δ=17·1 g/d), and lower intakes of fibre (Δ=−4·04 g/d), β-carotene (Δ=−1019·6 μg/d) and Ca (Δ=−87·8 mg/d; all P <0·0001). Conclusions UPF represent an important part of the diet in adults from the French general population and are associated with unbalanced nutritional intakes.
This study describes food consumption patterns in Canada according to the types of food processing using the Nova classification and investigates the association between consumption of ultra-processed foods and the nutrient profile of the diet. Dietary intakes of 33,694 individuals from the 2004 Canadian Community Health Survey aged 2 years and above were analyzed. Food and drinks were classified using Nova into unprocessed or minimally processed foods, processed culinary ingredients, processed foods and ultra-processed foods. Average consumption (total daily energy intake) and relative consumption (% of total energy intake) provided by each of the food groups were calculated. Consumption of ultra-processed foods according to sex, age, education, residential location and relative family revenue was assessed. Mean nutrient content of ultra-processed foods and non-ultra-processed foods were compared, and the average nutrient content of the overall diet across quintiles of dietary share of ultra-processed foods was measured. In 2004, 48% of calories consumed by Canadians came from ultra-processed foods. Consumption of such foods was high amongst all socioeconomic groups, and particularly in children and adolescents. As a group, ultra-processed foods were grossly nutritionally inferior to non-ultra-processed foods. After adjusting for covariates, a significant and positive relationship was found between the dietary share of ultra-processed foods and the content in carbohydrates, free sugars, total and saturated fats and energy density, while an inverse relationship was observed with the dietary content in protein, fiber, vitamins A, C, D, B6 and B12, niacin, thiamine, riboflavin, as well as zinc, iron, magnesium, calcium, phosphorus and potassium. Lowering the dietary share of ultra-processed foods and raising consumption of hand-made meals from unprocessed or minimally processed foods would substantially improve the diet quality of Canadian.