# Ultra-processed foods, adiposity and risk of head and neck cancer and oesophageal adenocarcinoma in the European Prospective Investigation into Cancer and Nutrition study: a mediation analysis

Fernanda Morales-Berstein, Carine Biessy, Vivian Viallon, Ana Luiza Goncalves-Soares, Corinne Casagrande, Bertrand Hémon, Nathalie Kliemann, Manon Cairat, Jessica Giselle Blanco Lopez, Aline Al Nahas, et al.

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## ► To cite this version:

Fernanda Morales-Berstein, Carine Biessy, Vivian Viallon, Ana Luiza Goncalves-Soares, Corinne Casagrande, et al. Ultra-processed foods, adiposity and risk of head and neck cancer and oesophageal adenocarcinoma in the European Prospective Investigation into Cancer and Nutrition study: a mediation analysis. European Journal of Nutrition, 2023, 10.1007/s00394-023-03270-1. hal-04342423

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# Ultra-processed foods, adiposity and risk of head and neck cancer and oesophageal adenocarcinoma in the European Prospective Investigation into Cancer and Nutrition study: a mediation analysis

Fernanda Morales-Berstein¹,² Ⓒ · Carine Biessy³ · Vivian Viallon³ · Ana Goncalves-Soares¹,² · Corinne Casagrande³ ·

Bertrand Hémon³ · Nathalie Kliemann³,⁴ · Manon Cairat³,⁵ · Jessica Blanco Lopez³ · Aline Al Nahas³ · Kiara Chang⁶ ·

Eszter Vamos⁶ · Fernanda Rauber⁷,⁸ · Renata Bertazzi Levy⁷,⁸ · Diana Barbosa Cunha⁹ · Paula Jakszyn¹⁰,¹¹,¹² ·

Pietro Ferrari³ · Paolo Vineis¹³,¹⁴ · Giovanna Masala¹⁵ · Alberto Catalano¹⁶ Ⓒ · Emily Sonestedt¹⁷ ·

Yan Borné¹⁷ · Verena Katzke¹⁸ · Rashmita Bajracharya¹⁸ · Claudia Agnoli¹⁹ · Marcela Guevara²⁰,²¹,²² ·

Alicia Heath²³ · Loredana Radoi⁵ · Francesca Mancini⁵ · Elisabete Weiderpass²⁴ · José María Huerta²¹,²⁵ ·

María-José Sánchez²¹,²⁶,²⁷,²⁸ · Anne Tjønneland²⁹,³⁰ · Cecilie Kyrø²⁹ · Matthias B. Schulze³¹,³² · Guri Skeie³³ ·

Marko Lukic³³ · Tonje Braaten³³ · Marc Gunter³,²³ · Christopher Millett⁶,³⁴ · Antonio Agudo¹⁰,¹¹ · Paul Brennan³⁵ ·

M. Carolina Borges¹,² · Rebecca C. Richmond¹,² · Tom G. Richardson¹,² · George Davey Smith¹,² · Caroline L. Relton¹,² ·

Inge Huybrechts³ · on behalf of the EPIC Network

Received: 25 May 2023 / Accepted: 10 October 2023
© The Author(s) 2023

## Abstract

**Purpose** To investigate the role of adiposity in the associations between ultra-processed food (UPF) consumption and head and neck cancer (HNC) and oesophageal adenocarcinoma (OAC) in the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort.

**Methods** Our study included 450,111 EPIC participants. We used Cox regressions to investigate the associations between the consumption of UPFs and HNC and OAC risk. A mediation analysis was performed to assess the role of body mass index (BMI) and waist-to-hip ratio (WHR) in these associations. In sensitivity analyses, we investigated accidental death as a negative control outcome.

**Results** During a mean follow-up of 14.13 ± 3.98 years, 910 and 215 participants developed HNC and OAC, respectively. A 10% g/d higher consumption of UPFs was associated with an increased risk of HNC (hazard ratio [HR]= 1.23, 95% confidence interval [CI] 1.14–1.34) and OAC (HR = 1.24, 95% CI 1.05–1.47). WHR mediated 5% (95% CI 3–10%) of the association between the consumption of UPFs and HNC risk, while BMI and WHR, respectively, mediated 13% (95% CI 6–53%) and 15% (95% CI 8–72%) of the association between the consumption of UPFs and OAC risk. UPF consumption was positively associated with accidental death in the negative control analysis.

**Conclusions** We reaffirmed that higher UPF consumption is associated with greater risk of HNC and OAC in EPIC. The proportion mediated via adiposity was small. Further research is required to investigate other mechanisms that may be at play (if there is indeed any causal effect of UPF consumption on these cancers).

**Keywords** Epidemiology · Food processing · NOVA classification · Oesophageal cancer · Head and neck cancer · Adiposity · Mediation analysis

## Abbreviations

BMI Body mass index

CI Confidence interval

EPIC European Prospective Investigation into Cancer and Nutrition

FFQ Food frequency questionnaire

HNC Head and neck cancer

HR Hazard ratio

IARC International Agency for Research on Cancer

INHANCE International Head and Neck Cancer Epidemiology

MICE Multivariate imputation by chained equations

<footnote>Extended author information available on the last page of the article</footnote>
<table><tr><td>OAC</td><td>Oesophageal adenocarcinoma</td></tr><tr><td>PM</td><td>Proportion mediated</td></tr><tr><td>PNDE</td><td>Pure natural direct effect</td></tr><tr><td>SD</td><td>Standard deviation</td></tr><tr><td>TE</td><td>Total effect</td></tr><tr><td>TNIE</td><td>Total natural indirect effect</td></tr><tr><td>UPF</td><td>Ultra-processed food</td></tr><tr><td>WHR</td><td>Waist-to-hip ratio</td></tr></table>

## Introduction

In recent years, there has been growing interest in the potential role of industrial food processing in disease aetiology. The NOVA (not an abbreviation) classification system developed by Monteiro et al. [1, 2] categorises foods into four groups according to their degree and purpose of processing: (1) unprocessed or minimally processed foods, (2) processed culinary ingredients, (3) processed foods and (4) ultra-processed foods (UPFs). UPFs are industrial formulations manufactured in a complex way using ingredients not usually found in kitchens (e.g. maltodextrin, hydrogenated oils, modified starches) and cosmetic additives (e.g. emulsifiers, flavourings, colourants, artificial sweeteners) [2]. They are typically cheap, highly palatable, and widely available ready-to-eat products which are often consumed in large quantities, replacing more nutritious, unprocessed/minimally processed foods in the diet [3, 4]. Examples of UPFs include soft drinks, sweet or savoury packaged snacks, confectionery, packaged breads and buns, reconstituted meat products and pre-prepared frozen or shelf-stable dishes.

Several studies have shown that the consumption of UPFs may be associated with an increased risk of cancer [5–9]. In the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort, Kliemann et al. [9] found positive associations between higher UPF consumption and the risk of head and neck cancer (HNC; hazard ratio [HR] = 1.14 per one standard deviation [SD] higher UPF intake, 95% confidence interval [CI] 1.06–1.24) and oesophageal adenocarcinoma (OAC; HR = 1.21 per 1-SD higher UPF intake, 95% CI 1.05–1.39). They also found an inverse association between UPF consumption and oesophageal squamous cell carcinoma risk (HR = 0.79 per 1-SD higher UPF intake, 95% CI 0.64–0.96), although this did not withstand additional adjustments for alcohol intake, body mass index (BMI) and several dietary factors (HR = 0.90 per 1-SD higher UPF intake, 95% CI 0.72–1.11).

UPF consumption has also been positively associated with higher adiposity (i.e. BMI, fat mass, waist circumference and waist-to-hip ratio [WHR]) [10–14]. Since body fatness

(measured by BMI, waist circumference and WHR) is an established modifiable risk factor for OAC [15–20], and visceral adiposity (i.e. waist circumference and WHR) has been positively associated with HNC risk [21–23], it is plausible that the positive associations between UPF consumption and these upper-aerodigestive tract cancers are mediated via adiposity. Although BMI has been inversely associated with HNC risk, this seems to be a consequence of residual confounding related to smoking (an established risk factor for HNC), as smokers tend to have lower BMIs than non-smokers [23]. In a meta-analysis of 20 prospective cohort studies, BMI was positively associated with HNC risk when the analysis was restricted to never smokers [22]. Although adiposity may be one of the mechanisms underlying the association between UPF consumption and upper-aerodigestive tract cancer, this has not been investigated using mediation analysis.

The aim of this study was to reassess and further investigate the associations between the consumption of UPFs and the risk of HNC and OAC in the EPIC study. As a complement to the study by Kliemann et al. [9] (described above), this study explored the associations between UPF consumption and the risk of HNC and its subtypes (i.e. oral cavity, oropharynx, hypopharynx, larynx and unspecified/ overlapping cancers) as defined by the International Head and Neck Cancer Epidemiology (INHANCE) consortium. It also investigated effect modification by smoking status, alcohol intake, sex, physical activity, and education level in the associations between the consumption of UPFs and the risk of upper-aerodigestive tract cancers. Additionally, this study assessed the possibility of residual confounding using accidental death as a negative control outcome. Lastly, it examined the role of BMI and WHR in the associations between UPF consumption and the risk of HNC and OAC by means of a mediation analysis.

## Methods

### The EPIC cohort

The EPIC study has been fully described elsewhere [24–26]. Briefly, EPIC is one of the largest prospective cohort studies in Europe. It recruited 521,323 participants between 1992 and 2000. Participants were enrolled in 23 centres across 10 European countries, namely Denmark, France, Germany, Greece, Italy, the Netherlands, Norway, Spain, Sweden and the United Kingdom. Most were 35–69 years old at recruitment [24, 25]. They were either volunteers from the general population, blood donors, employees of
local companies, teachers/school employees or individuals
enrolled in local ongoing studies. All participants provided
written informed consent before completing the dietary and
lifestyle questionnaires. Anthropometric and blood pressure
data were also obtained at baseline. EPIC was approved by
the International Agency for Research on Cancer (IARC)
Ethics Committee and the local ethical review boards of all
EPIC centres.

Study sample

Participants who withdrew consent from the study were
not included in this research. We excluded participants
diagnosed with cancer before enrolment (n = 25,184) and
those with a length of follow-up equal to zero (n = 4148).
We also excluded participants who did not complete
the dietary or lifestyle questionnaires (n = 6259). We
additionally excluded participants with extreme energy
intake versus energy requirement ratios (top and bottom
1%) (n = 9573) and participants recruited in Greece due
to administrative issues (n = 26,048). After exclusions,
450,111 participants were included in the analyses
(Supplementary Fig. 1).

Dietary data and food processing variables

Semi-quantitative food frequency questionnaires (FFQs), extensive quantitative dietary questionnaires, and combined methods (i.e. semi-quantitative FFQs combined with 7-day records in the UK, and a non-quantitative FFQ combined with a 14-day record on hot meals in Malmö, Sweden) were used to obtain dietary data at baseline [25]. These were centre specific to account for local dietary habits and were either self-administered or administered in-person by trained interviewers. Furthermore, a standardised 24-h recall was used to obtain supplementary dietary data for a subsample of EPIC participants to calibrate baseline dietary measurements across EPIC centres [25, 27–30]. The dietary questionnaires and their mode of administration were described in detail in previous publications [25, 30].

The NOVA classification was used to categorise
foods into four groups according to their extent and
purpose of industrial processing [31]. Unprocessed/
minimally processed foods (NOVA 1) are natural foods
that may have undergone minimal processing for their
preservation, storage, safety, or edibility. Processed
culinary ingredients (NOVA 2) correspond to substances
derived from unprocessed/minimally processed foods

(e.g. oil, butter) or nature (e.g. salt) that are normally
consumed in combination with unprocessed/minimally
processed foods. Both processed foods (NOVA 3) and
UPFs (NOVA 4) are industrial products. The former
typically contain two or three common ingredients (i.e. a
combination of unprocessed/minimally processed foods
and processed culinary ingredients), while the latter
contain many ingredients (most of which are rarely used
in kitchens) and additives that make the final product
tastier and more attractive to consumers.

Food preparations made (at home or elsewhere) using
traditional methods were decomposed using standardised
recipes. Individual food items were then classified
according to their degree of processing. Food items were
combined into broader food categories for simplicity. Of
a total of 67 food categories in the dietary questionnaires,
19 were classified as unprocessed/minimally processed
foods, 5 as culinary ingredients, 13 as processed foods
and 30 as UPFs (see Supplementary Table 1 for details).

Here, we used the relative intake of each NOVA group
in grams per day (%g/d). We also used the absolute intake
in grams per day (g/d) and the absolute and relative intake
in kilocalories per day in sensitivity analyses (kcal/d and
%kcal/d, respectively).

Ascertainment of cancer cases

Incident cancer cases were identified through population-
based cancer registries in Denmark, Italy (except Naples),
the Netherlands, Norway, Spain, Sweden and the United
Kingdom. Participants in other centres (France, Germany,
Greece and Naples) were actively followed up using
health insurance records, pathology registries and direct
contact with participants or their next of kin.

HNC and OAC were defined using the 2nd and
3rd Revision of the International Classification
of Diseases for Oncology (ICDO-2 and ICDO-3).
According to the INHANCE consortium [32], HNC
cases include malignant neoplasms of the oral cavity
(topography codes C00.3-C00.6, C00.8-C00.9,
C02.0-C02.3, C03.0-C03.1, C03.9, C04.0-C04.1,
C04.8-C04.9, C05.0, C06.0-C06.2, C06.8-C06.9),
oropharynx (C01.9, C02.4, C05.1-C05.2, C09.0-C09.1,
C09.8-C09.9, C10.0-C10.4, C10.8-C10.9), hypopharynx
(C12.9-C13.2, C13.8-C13.9), larynx (C32.0-C32.3,
C32.8-C32.9), and oral cavity and pharynx unspecified/
overlapping regions (C02.8-C02.9, C05.8-C05.9, C14.0,
C14.2, C14.8). We did not exclude any histological
subtypes of HNC. Oesophageal cancer cases correspond
to topography codes C15.0–C15.5 and C15.8–C15.9. Among these, OAC cases were identified with codes 8140/3, 8144/3, 8480/3, 8481/3 and 8490/3. Other oesophageal cancer subtypes (e.g. squamous cell carcinoma and small cell carcinoma) were not investigated as outcomes in this study.

## Covariates

Data on age at recruitment, sub-centre (22 centres in total, split into 27 sub-centres as follows: Northeast of France, Northwest of France, South of France, South coast of France, Florence, Varese, Ragusa, Turin, Naples, Asturias, Granada, Murcia, Navarra, San Sebastian, Cambridge, Oxford health-conscious population, Oxford general population, Bilthoven, Utrecht, Heidelberg, Potsdam, Malmö, Umeå, Aarhus, Copenhagen, Southeast of Norway, Northwest of Norway), sex (male/female), education level (none, primary, technical/professional, secondary, further education), physical activity based on the Cambridge Physical Activity Index [33] (inactive, moderately inactive, moderately active, active), measured/self-reported height (continuous in cm) and smoking status (never, former, current, unknown) were obtained at baseline through anthropometric measurements and lifestyle questionnaires. Additionally, data on alcohol intake (continuous in g/d) were acquired using dietary questionnaires.

## Potential mediators

BMI and WHR were investigated as potential mediators in mediation analyses. BMI (continuous in kg/m²) was calculated from measured height and weight (measured using comparable, standardised methods) [34]. WHR (continuous) was estimated from measured waist and hip circumferences. Waist circumference was measured midway between the iliac crest and the lower ribs or at the narrowest torso circumference. Hip circumference was measured over the buttocks or at the widest point. EPIC-Oxford health-conscious population self-reported data were also used to estimate BMI and WHR, after the application of measurement error corrections [34, 35].

## Statistical analysis

### Descriptive characteristics

The participants' baseline characteristics were divided into sex-specific quartiles of relative UPF consumption (in

%g/d). Mean and SD estimates were obtained for continuous variables, while frequencies and percentages were obtained for binary/categorical variables. Furthermore, we made a histogram to graphically represent the distribution of UPF consumption (in %g/d) in the EPIC cohort.

### Data imputation

We used single-value imputation to deal with missing data in the covariates used to control for potential confounding (i.e. height, physical activity, education level and smoking status). When measured/self-reported height values were not available, missing values were imputed with mean centre-, age- and sex-specific height values [34]. Mode imputation was used for baseline binary and categorical covariates missing less than 5% of their values (i.e. education level: "primary school completed", physical activity: "moderately inactive", smoking status: "never"). Multiple imputation was used in sensitivity analyses (details in the "sensitivity analyses" subsection below).

### Main association analysis

Cox proportional hazards models with age as the underlying timescale were used to investigate the association between the intake of UPFs and the risk of HNC and OAC. We estimated HRs and 95% CIs per 10% g/d higher consumption of UPFs. Time of entry was defined as age at recruitment, while time of exit was defined as age at first cancer diagnosis (excluding non-melanoma skin cancer) or age at last follow-up (i.e. death, emigration, loss to follow-up or end of follow-up [i.e. between June 2008 and December 2013, depending on the centre]), whichever came first. Model 1 was stratified by age at recruitment in 1-year categories, sex and sub-centre. Model 2 was additionally adjusted for education, physical activity, height and smoking status. Model 3 was additionally adjusted for alcohol intake in g/d to reflect the association between the consumption of UPFs and cancer, regardless of alcohol intake (a well-known cancer risk factor [36–44] that forms part of some processed foods and UPFs).

We graphically assessed the proportional hazards assumption using log-log survival plots. Additionally, we tested proportionality using Schoenfeld residuals. We also used correlation matrices and variance inflation factors to assess the presence of multicollinearity. Non-linearity was assessed using likelihood ratio tests comparing UPF consumption (in %g/d) modelled with and without natural cubic splines.
We undertook additional analyses to investigate the associations between the consumption of UPFs and the risk of HNC subtypes (i.e. oral cavity, oropharynx, hypopharynx, larynx, and oral cavity and pharynx unspecified/overlapping cancers). Heterogeneity tests were used to assess differences between HNC subtype estimates.

Furthermore, we stratified Model 3 (for every exposure–outcome combination) by alcohol intake (as defined by Wozniak et al. [45], i.e. no/light alcohol intake [0.1–6 g/d (men); 0.1–3 g/d (women)], moderate alcohol intake [6.1–24 g/d (men); 3.1–24 g/d (women)], heavy alcohol intake [> 24 g/d)], sex (i.e. male, female), physical activity (i.e. inactive, moderately inactive, moderately active, active), smoking status (i.e. never smoker, former smoker, current smoker) and education level (i.e. primary school or less, secondary or technical/professional school, higher education) and performed likelihood ratio tests to explore interactions. Models were not adjusted for the stratification variable.

## Mediation analysis

Under the strong assumption that there is no residual confounding or measurement error in our study, we conducted a mediation analysis using the counterfactual framework [46] to further explore the mediating role of BMI and WHR in the associations between UPF consumption and the risk of HNC and OAC (Fig. 1).

In exploratory analyses, we ran linear regressions to study the associations between UPF consumption (i.e. the exposure) and both WHR and BMI (i.e. the potential mediators). We also ran exposure-adjusted Cox regressions to analyse the associations between the potential mediators and the risk of both HNC and OAC (i.e. the outcomes). Where there was evidence of an association between the potential mediator and both the exposure and the outcome, we used the “cmest” function in the “CMAverse” R package [47] to decompose the Total Effect (TE) of UPF consumption on the corresponding upper-aerodigestive

<figure><img src="image_3.png" /><figcaption>**Fig. 1** Mediation analysis diagram of the counterfactual two-way decomposition of the total effect of UPF consumption on the risk of head and neck cancer and oesophageal adenocarcinoma. All mediation models accounted for potential exposure–mediator interactions and were adjusted for age at recruitment in 1-year categories, sex, sub-centre, education, physical activity, height, smoking status and alcohol intake. The total effect (TE) corresponds to the sum of the pure natural direct effect (PNDE) and the total natural indirect effect (TNIE). Point estimates were obtained by direct counterfactual imputation estimation and confidence intervals were obtained using 1000 bootstrap repetitions. Abbreviations: BMI, body mass index; WHR, waist-to-hip ratio; UPF, ultra-processed food; HNC, head and neck cancer; OAC, oesophageal adenocarcinoma</figcaption></figure>
tract cancer into a Pure Natural Direct Effect (PNDE) and a Total Natural Indirect Effect (TNIE) (on the ratio scale $TE = PNDE \times TNIE$). The proportion mediated was also calculated (i.e. $100 \times (PNDE \times (TNIE - 1))/(TE - 1)$) for each exposure–mediator–outcome combination [48]. All mediation models accounted for potential exposure–mediator interactions and were adjusted for age at recruitment in 1-year categories, sex, sub-centre, education, physical activity, height, smoking status and alcohol intake. Point estimates were obtained by direct counterfactual imputation estimation and 95% CIs were obtained using 1000 bootstrap repetitions. The results were scaled to reflect a 10% g/d higher consumption of UPFs.

## Sensitivity analyses

As a sensitivity analysis, we explored adjusting our Cox models for total water intake (i.e. water content from foods and drinks, in addition to drinking water and water used as an ingredient in preparations). This was to see whether differences in water content across NOVA groups may influence the associations between the relative intake of UPFs and the risk of HNC and OAC. Similarly, we explored adjustments for total energy intake.

We also reran our Cox models after excluding participants who were censored during the first two years of follow-up to avoid reverse causation due to undiagnosed cancer at recruitment.

Additionally, we repeated the analyses using the absolute intake of UPFs in grams per day (g/d) and the absolute and relative intake in kilocalories per day (kcal/d and %kcal/d, respectively) as the exposure.

Moreover, we conducted a complete case analysis excluding participants with missing data for at least one lifestyle covariate (i.e. smoking status, physical activity and education level). In addition, we used the ‘mice’ R package to perform multivariate imputation by chained equations (MICE) [49], whereby smoking status, physical activity and education level were imputed five times by predictive mean matching. We fit our models using the MICE imputed data sets and then pooled the results according to Rubin’s rules [50] to obtain average HR estimates and standard errors for each model. For the complete case analysis and the MICE analysis, we still used centre-, age- and sex-specific imputed height as a covariate, as this is standard practice when dealing with anthropometric variables as confounders in EPIC [34].

Finally, we performed a negative control outcome analysis (i.e. where the outcome is not plausibly linked to the exposure of interest) to help identify any residual confounding that could be biasing our results

[51]. We considered accidental deaths as the outcome (instead of upper-aerodigestive tract cancers) since the consumption of foods by their degree of processing is unlikely associated with the risk of being involved in a deadly accident (e.g. falls, transport accidents, accidental drowning). Any evidence of an association between UPF consumption and accidental deaths would suggest that our main results may be biased by the same factors that biased the negative control outcome results. Accidental deaths were defined as deaths due to events linked to codes V01–X59 in the 10th Revision of the International Classification of Diseases (ICD-10). For the negative control analysis, time of exit was defined as age at the time of death, emigration, loss to follow-up or end of follow-up, whichever came first. Participants were not censored at the time of cancer diagnosis, whereas they were in all other analyses in this study. The accidental death models accounted for the same covariates as the main analysis. BMI and type 2 diabetes mellitus would not normally be adjusted for in this analysis, as they are potential mediators and adjusting for them could induce collider bias (i.e. open backdoor paths from UPF consumption to accidental deaths through unobserved factors) [52]. Here, we did this in an explorative manner, assuming the absence of unobserved confounders of BMI, type 2 diabetes mellitus and accidental deaths.

## Statistical software

All statistical analyses and visualisations were performed using R version 4.2.3. We used version 3.2.10 of the “survival” R package for the Cox regressions and version 0.1.0 of the “CMAverse” R package [47] for the mediation analysis. We also used version 0.1.0 of the “ggforestplot” R package to create forest plots. To create tables, we used version 1.3.0 of the “tidyverse” R package and version 0.7.0 of the “flextable” R package. P-values for heterogeneity between HNC subtype estimates were obtained using version 4.18–0 of the “meta” R package. Non-linearity was assessed using version 4.2.3 of the “splines” R package. MICE was performed using version 3.16 of the “mice” R package. Two-sided *p*-values < 0.05 were considered statistically significant.

## Results

### Descriptive characteristics

In total, we included 450,111 participants of which 70.8% were female. The mean age at recruitment was 51.1 years
<table><caption>**Table 1** Baseline characteristics of study participants by sex-specific quartiles of relative ultra-processed food consumption (%g/d)</caption><thead><tr><th></th><th>Q1<br>(M: ≤8.95%g/d;<br>F: ≤6.94%g/d)</th><th>Q2 (M: 8.96–<br>13.16%g/d; F:<br>6.95–11.30%g/d)</th><th>Q3 (M: 13.17–<br>18.62%g/d; F:<br>11.31–17.54%g/d)</th><th>Q4 (M: 18.63–<br>85.16%g/d; F:<br>17.55–90.53%g/d)</th><th>Overall</th></tr></thead><tbody><tr><td>Participants, n</td><td>112,529</td><td>112,528</td><td>112,527</td><td>112,527</td><td>450,111</td></tr><tr><td>Age at recruitment (years),<br>mean ± SD</td><td>52.9 ± 7.74</td><td>52.5 ± 8.87</td><td>51.1 ± 10.2</td><td>48.1 ± 11.1</td><td>51.1 ± 9.75</td></tr><tr><td>Females, n (%)</td><td>79,672 (70.8)</td><td>79,672 (70.8)</td><td>79,671 (70.8)</td><td>79,671 (70.8)</td><td>318,686 (70.8)</td></tr><tr><td>Country, n (%)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>France</td><td>38,677 (34.4)</td><td>19,085 (17.0)</td><td>7592 (6.7)</td><td>2049 (1.8)</td><td>67,403 (15.0)</td></tr><tr><td>Italy</td><td>17,500 (15.6)</td><td>13,098 (11.6)</td><td>8935 (7.9)</td><td>5012 (4.5)</td><td>44,545 (9.9)</td></tr><tr><td>Spain</td><td>23,830 (21.2)</td><td>8159 (7.3)</td><td>4975 (4.4)</td><td>3025 (2.7)</td><td>39,989 (8.9)</td></tr><tr><td>United Kingdom</td><td>2987 (2.7)</td><td>13,407 (11.9)</td><td>26,794 (23.8)</td><td>32,228 (28.6)</td><td>75,416 (16.8)</td></tr><tr><td>The Netherlands</td><td>3217 (2.9)</td><td>10,035 (8.9)</td><td>13,335 (11.9)</td><td>9951 (8.8)</td><td>36,538 (8.1)</td></tr><tr><td>Germany</td><td>6324 (5.6)</td><td>11,200 (10.0)</td><td>14,136 (12.6)</td><td>16,897 (15.0)</td><td>48,557 (10.8)</td></tr><tr><td>Sweden</td><td>8854 (7.9)</td><td>16,577 (14.7)</td><td>14,299 (12.7)</td><td>8944 (7.9)</td><td>48,674 (10.8)</td></tr><tr><td>Denmark</td><td>10,934 (9.7)</td><td>19,621 (17.4)</td><td>14,384 (12.8)</td><td>10,075 (9.0)</td><td>55,014 (12.2)</td></tr><tr><td>Norway</td><td>206 (0.2)</td><td>1346 (1.2)</td><td>8077 (7.2)</td><td>24,346 (21.6)</td><td>33,975 (7.5)</td></tr><tr><td>Height<sup>a</sup> (cm), mean ± SD</td><td>164 ± 8.65</td><td>166 ± 8.99</td><td>167 ± 8.83</td><td>168 ± 8.57</td><td>166 ± 8.86</td></tr><tr><td>Measured BMI<sup>b</sup> (kg/m<sup>2</sup>),<br>mean ± SD</td><td>26.2 ± 4.25</td><td>25.5 ± 4.10</td><td>25.4 ± 4.12</td><td>25.5 ± 4.41</td><td>25.6 ± 4.22</td></tr><tr><td>Missing, n (%)</td><td>27,851 (24.8)</td><td>15,490 (13.8)</td><td>14,348 (12.8)</td><td>26,873 (23.9)</td><td>84,562 (18.8)</td></tr><tr><td>Measured WHR<sup>c</sup>, mean ± SD</td><td>0.86 ± 0.1</td><td>0.84 ± 0.1</td><td>0.83 ± 0.1</td><td>0.84 ± 0.1</td><td>0.84 ± 0.1</td></tr><tr><td>Missing, n (%)</td><td>31,561 (28.0)</td><td>24,491 (21.8)</td><td>23,256 (20.7)</td><td>33,945 (30.2)</td><td>113,253 (25.2)</td></tr><tr><td>Education level, n (%)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>None</td><td>8622 (7.7)</td><td>3120 (2.8)</td><td>2190 (1.9)</td><td>1619 (1.4)</td><td>15,551 (3.5)</td></tr><tr><td>Primary school completed</td><td>30,898 (27.5)</td><td>28,771 (25.6)</td><td>26,533 (23.6)</td><td>24,862 (22.1)</td><td>111,064 (24.7)</td></tr><tr><td>Technical/Professional school</td><td>14,072 (12.5)</td><td>24,757 (22.0)</td><td>30,300 (26.9)</td><td>34,653 (30.8)</td><td>103,782 (23.1)</td></tr><tr><td>Secondary school</td><td>28,716 (25.5)</td><td>23,685 (21.0)</td><td>20,675 (18.4)</td><td>20,834 (18.5)</td><td>93,910 (20.9)</td></tr><tr><td>Higher education</td><td>27,810 (24.7)</td><td>28,964 (25.7)</td><td>27,535 (24.5)</td><td>24,622 (21.9)</td><td>108,931 (24.2)</td></tr><tr><td>Missing</td><td>2411 (2.1)</td><td>3231 (2.9)</td><td>5294 (4.7)</td><td>5937 (5.3)</td><td>16,873 (3.7)</td></tr><tr><td>Physical activity level, n (%)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Inactive</td><td>27,550 (24.5)</td><td>21,938 (19.5)</td><td>20,355 (18.1)</td><td>18,189 (16.2)</td><td>88,032 (19.6)</td></tr><tr><td>Moderately inactive</td><td>39,820 (35.4)</td><td>38,982 (34.6)</td><td>36,912 (32.8)</td><td>34,227 (30.4)</td><td>149,941 (33.3)</td></tr><tr><td>Moderately active</td><td>27,477 (24.4)</td><td>28,369 (25.2)</td><td>29,751 (26.4)</td><td>34,602 (30.8)</td><td>120,199 (26.7)</td></tr><tr><td>Active</td><td>17,185 (15.3)</td><td>21,700 (19.3)</td><td>22,470 (20.0)</td><td>21,760 (19.3)</td><td>83,115 (18.5)</td></tr><tr><td>Missing</td><td>497 (0.4)</td><td>1539 (1.4)</td><td>3039 (2.7)</td><td>3749 (3.3)</td><td>8824 (2.0)</td></tr><tr><td>Smoking status, n (%)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Never</td><td>57,091 (50.7)</td><td>54,677 (48.6)</td><td>53,788 (47.8)</td><td>53,738 (47.8)</td><td>219,294 (48.7)</td></tr><tr><td>Former</td><td>28,412 (25.2)</td><td>31,268 (27.8)</td><td>32,246 (28.7)</td><td>30,754 (27.3)</td><td>122,680 (27.3)</td></tr><tr><td>Current</td><td>24,549 (21.8)</td><td>24,830 (22.1)</td><td>24,684 (21.9)</td><td>25,651 (22.8)</td><td>99,714 (22.2)</td></tr><tr><td>Missing</td><td>2477 (2.2)</td><td>1753 (1.6)</td><td>1809 (1.6)</td><td>2384 (2.1)</td><td>8423 (1.9)</td></tr><tr><td>Alcohol intake (g/d), mean ± SD</td><td>15.3 ± 21.1</td><td>13.0 ± 17.1</td><td>10.7 ± 14.4</td><td>8.00 ± 12.3</td><td>11.7 ± 16.8</td></tr><tr><td>Energy intake (kcal/d),<br>mean ± SD</td><td>2100 ± 622</td><td>2110 ± 598</td><td>2140 ± 607</td><td>2200 ± 666</td><td>2140 ± 625</td></tr><tr><td>Carbohydrate intake (g/d),<br>mean ± SD</td><td>237 ± 75.7</td><td>245 ± 72.3</td><td>257 ± 75.6</td><td>278 ± 91.6</td><td>254 ± 80.7</td></tr><tr><td>Fat intake (g/d), mean ± SD</td><td>79.4 ± 28.2</td><td>81.4 ± 28.6</td><td>82.7 ± 29.8</td><td>82.6 ± 31.7</td><td>81.5 ± 29.6</td></tr><tr><td>Fibre intake (g/d), mean ± SD</td><td>24.3 ± 8.74</td><td>23.3 ± 8.10</td><td>23.8 ± 8.54</td><td>23.9 ± 9.04</td><td>23.8 ± 8.62</td></tr><tr><td>Sodium intake (mg/d),<br>mean ± SD</td><td>2500 ± 968</td><td>2650 ± 1110</td><td>2630 ± 1190</td><td>2650 ± 1290</td><td>2610 ± 1150</td></tr></tbody></table>
<table><caption>**Table 1** (continued)</caption><thead><tr><th></th><th>Q1<br>(M: ≤8.95%g/d;<br>F: ≤6.94%g/d)</th><th>Q2 (M: 8.96–<br>13.16%g/d; F:<br>6.95–11.30%g/d)</th><th>Q3 (M: 13.17–<br>18.62%g/d; F:<br>11.31–17.54%g/d)</th><th>Q4 (M: 18.63–<br>85.16%g/d; F:<br>17.55–90.53%g/d)</th><th>Overall</th></tr></thead><tbody><tr><td>Relative intake of ultra-<br>processed foods (%g/d),<br>mean ± SD</td><td>4.95 ± 1.90</td><td>9.65 ± 1.53</td><td>14.6 ± 1.85</td><td>25.7 ± 7.78</td><td>13.7 ± 8.76</td></tr><tr><td>Absolute intake of ultra-<br>processed foods (g/d),<br>mean ± SD</td><td>141 ± 80.1</td><td>273 ± 104</td><td>393 ± 138</td><td>648 ± 321</td><td>364 ± 264</td></tr><tr><td>Relative intake of processed<br>foods (%g/d), mean ± SD</td><td>16.9 ± 12.0</td><td>14.2 ± 10.0</td><td>12.5 ± 8.85</td><td>10.7 ± 7.43</td><td>13.6 ± 10.0</td></tr><tr><td>Absolute intake of processed<br>foods (g/d), mean ± SD</td><td>445 ± 367</td><td>385 ± 313</td><td>328 ± 270</td><td>271 ± 237</td><td>357 ± 308</td></tr><tr><td>Relative intake of unprocessed/<br>minimally processed foods<br>(%g/d), mean ± SD</td><td>76.6 ± 12.7</td><td>74.9 ± 10.7</td><td>71.9 ± 9.50</td><td>62.7 ± 10.3</td><td>71.5 ± 12.1</td></tr><tr><td>Absolute intake of unprocessed/<br>minimally processed foods<br>(g/d), mean ± SD</td><td>2190 ± 985</td><td>2130 ± 818</td><td>1940 ± 699</td><td>1590 ± 653</td><td>1960 ± 833</td></tr></tbody></table>

Baseline characteristics stratified by sex-specific quartiles of relative intake of ultra-processed foods (%g/d). Abbreviations: BMI, Body mass index; WHR, Waist-to-hip ratio; Q1, quartile 1; Q2, quartile 2; Q3, quartile 3; Q4, quartile 4; M, Males; F, Females; SD, standard deviation

ªWhen measured/self-reported height values were not available, missing values were imputed with mean centre-, age- and sex-specific height values

ᵇAccording to the World Health Organization [53], a healthy adult BMI lies between 18.5 and 24.9 kg/m²

ᶜAccording to the World Health Organization [53], a healthy WHR corresponds to <0.9 for males and <0.85 for females

(SD 9.8, range 17.8–98.5 years). The mean consumption of UPFs in the cohort was 13.7% g/d (364 g/d), ranging from a mean intake of 8% g/d (156.9 g/d) in Spain to 18.6% g/d (520.5 g/d) in the United Kingdom. A histogram of the proportion of UPFs in the diet (in %g/d) is available in Supplementary Fig. 2. On average, males consumed a higher proportion of UPFs than females (14.7% vs 13.3%, *p* < 0.001). Participants with technical education were among the highest consumers of UPFs (Table 1). UPFs contributed greatly to the diet of younger, taller, and more physically active participants. Participants who did not provide data on their physical activity and education also tended to consume more UPFs. In terms of diet quality, UPFs were highly consumed by participants who consumed less alcohol and more calories, carbohydrates, fat and sodium.

The UPF group was mainly composed of fizzy drinks (14.1% of absolute UPF consumption in g/d), non-carbonated sweetened beverages (12.1%), ultra-processed dairy products (12.0%), ultra-processed breads (12.0%) and ultra-processed meats (9.9%) (Supplementary Table 1). Beer and wine (46.2%) were the main contributors to the processed

foods group, followed by processed breads (22.7%) and cheese (10.2%). The unprocessed/minimally processed foods group primarily comprised tea and coffee (32.2%), water (17.3%), milk and plain yoghurt (12.4%), fruit (11.2%) and vegetables (8.9%).

### Associations between the consumption of ultra-processed foods and upper-aerodigestive tract cancers

During a mean follow-up of 14.13 ± 3.98 years (6,358,569 person-years, median follow-up = 14.95 years; range 1 day–22.79 years), 1125 incident cases of HNC and OAC were documented. Of these, 910 had HNC (i.e. 234 oral cavity, 235 oropharynx, 66 hypopharynx, 310 larynx and 65 oral cavity and pharynx unspecified/overlapping regions) and 215 had OAC.

The proportional hazards assumption was met in all models (Supplementary Figs. 3, 4, 5, 6), and we did not find evidence of multicollinearity between covariates (Supplementary Fig. 7 and Supplementary Table 2).
<figure><img src="image_4.png" /></figure>

**Fig. 2** Associations between the consumption of ultra-processed foods (in %g/d) and the risk of head and neck cancer and oesophageal adenocarcinoma. Hazard ratios per 10% g/d higher ultra-processed food intake. Time of entry was defined as age at recruitment, while time of exit was defined as age at first cancer diagnosis (excluding non-melanoma skin cancer) or age at last follow-up (i.e. death, emigration, loss to follow-up or end of follow-up), whichever came first.

Model 1 was stratified by age at recruitment in 1-year categories, sex, and sub-centre. Model 2 was additionally adjusted for education, physical activity, height, and smoking status. Model 3 was additionally adjusted for alcohol intake. N=450,111, of which 910 and 215 had head and neck cancer and oesophageal adenocarcinoma, respectively. Abbreviations: CI, confidence interval; UPF, ultra-processed food

**Fig. 3** Associations between the consumption of ultra-processed foods (in %g/d) and head and neck cancer subtypes. Hazard ratios per 10% g/d higher ultra-processed food intake. Time of entry was defined as age at recruitment, while time of exit was defined as age at first cancer diagnosis (excluding non-melanoma skin cancer) or age at last follow-up (i.e. death, emigration, loss to follow-up or end of follow-up), whichever came first. Model 1 was stratified by age at recruitment in 1-year categories, sex, and sub-centre. Model 2 was additionally adjusted for education, physical activity, height and smoking status. Model 3 was additionally adjusted for alcohol intake. N=450,111, of which 234, 235, 66, 310 and 65 had cancer of the oral cavity, oropharynx, hypopharynx, larynx, and oral cavity and pharynx unspecified/overlapping regions, respectively. Abbreviations: CI, confidence interval; UPF, ultra-processed food

<figure><img src="image_5.png" /></figure>
Furthermore, there was no evidence of non-linearity between UPF consumption and HNC (p = 0.54) (Supplementary Fig. 8). The non-linearity test for the association between UPF consumption and the risk of OAC was not informative due to the limited number of OAC cases in the dataset (results not shown).

## Head and neck cancer (HNC)

A higher proportion of UPF in the diet (in %g/d) was associated with a higher risk of HNC, even after accounting for alcohol intake (HR = 1.23 per 10% g/d higher UPF intake, 95% CI 1.14–1.34) (Fig. 2 and Supplementary Table 3). We did not find evidence of heterogeneity between HNC subtypes (p-value for heterogeneity = 0.11) (Fig. 3 and Supplementary Table 4).

In stratified analyses for the association between UPF consumption (in %g/d) and HNC risk, we did not find evidence of effect modification by alcohol intake (p-value for interaction = 0.46), physical activity level (p-value for interaction = 0.48), smoking status (p-value for interaction = 0.46) or education level (p-value for interaction = 0.31) (Supplementary Table 5). There was some evidence of an interaction by sex (p-value for interaction = 0.006), with a positive association between UPF consumption and HNC risk among males (HR = 1.34 per 10% g/d higher UPF intake, 95% CI 1.22–1.48,

N = 131,425, events = 603) but not among females (HR = 1.03, 95% CI 0.87–1.21, N = 318,686, events = 307).

## Oesophageal adenocarcinoma (OAC)

After accounting for alcohol intake, UPF consumption (in %g/d) was positively associated with OAC risk (HR = 1.24 per 10% g/d higher UPF intake, 95% CI 1.05–1.47) (Fig. 2 and Supplementary Table 3).

When we stratified the association between UPF consumption (in %g/d) and OAC risk, we did not find evidence of differing estimates across levels of alcohol intake (p-value for interaction = 0.18), physical activity (p-value for interaction = 0.94), smoking status (p-value for interaction = 0.99), sex (p-value for interaction = 0.44) or education (p-value for interaction = 0.83) (Supplementary Table 5).

## Mediating role of adiposity in the associations between ultra-processed food consumption and upper-aerodigestive tract cancers

In exploratory analyses (Supplementary Table 6), we found positive associations between UPF consumption and both BMI (mean change = 0.24 kg/m² per 10% g/d higher UPF intake, 95% CI 0.22–0.26) and WHR (mean change = 0.41 per 10% g/d higher UPF intake, 95% CI 0.38–0.43).

<table><caption>**Table 2** Mediation analysis for the associations between ultra-processed food consumption (in %g/d) and head and neck cancer and oesophageal adenocarcinoma, where the potential mediators (i.e. waist-to-hip ratio and body mass index) were measured at baseline</caption><thead><tr><th>Association</th><th>Mediator</th><th>Effect</th><th>Estimate</th><th>95% CI</th><th>P-value</th><th>P-value for interaction</th></tr></thead><tbody><tr><td rowspan="4">Ultra-processed food consumption and head and neck cancer risk</td><td rowspan="4">Waist-to-hip ratio</td><td>TE, HR</td><td>1.23</td><td>1.12–1.33</td><td>&lt;0.001</td><td>0.03</td></tr><tr><td>PNDE, HR</td><td>1.22</td><td>1.11–1.32</td><td>&lt;0.001</td><td></td></tr><tr><td>TNIE, HR</td><td>1.01</td><td>1.01–1.01</td><td>&lt;0.001</td><td></td></tr><tr><td>PM, %</td><td>5.32</td><td>2.73–10.27</td><td>&lt;0.001</td><td></td></tr><tr><td rowspan="4">Ultra-processed food consumption and oesophageal adenocarcinoma risk</td><td rowspan="4">Waist-to-hip ratio</td><td>TE, HR</td><td>1.20</td><td>1.02–1.36</td><td>0.032</td><td>0.38</td></tr><tr><td>PNDE, HR</td><td>1.17</td><td>0.99–1.33</td><td>0.06</td><td></td></tr><tr><td>TNIE, HR</td><td>1.03</td><td>1.02–1.03</td><td>&lt;0.001</td><td></td></tr><tr><td>PM, %</td><td>14.85</td><td>7.57–72.27</td><td>0.03</td><td></td></tr><tr><td rowspan="4">Body mass index</td><td rowspan="4"></td><td>TE, HR</td><td>1.21</td><td>1.02–1.37</td><td>0.038</td><td>0.28</td></tr><tr><td>PNDE, HR</td><td>1.18</td><td>1.00–1.34</td><td>0.06</td><td></td></tr><tr><td>TNIE, HR</td><td>1.02</td><td>1.01–1.03</td><td>&lt;0.001</td><td></td></tr><tr><td>PM, %</td><td>13.38</td><td>5.69–52.86</td><td>0.04</td><td></td></tr></tbody></table>

Point estimates were obtained by direct counterfactual imputation estimation and confidence intervals were obtained using 1000 bootstrap repetitions. Results were scaled to reflect a 10% g/d higher consumption of ultra-processed foods. Mediation models were adjusted for age at recruitment in 1-year categories, sex, sub-centre, education, physical activity, height, smoking status and alcohol intake. The total effect corresponds to the multiplication of the pure natural direct effect and the total natural indirect effect. Waist-to-hip ratio models included 336,858 participants, of which 828 and 195 had head and neck cancer and oesophageal adenocarcinoma, respectively. Body mass index models included 365,549 participants, of which 212 had oesophageal adenocarcinoma. *HR* hazard ratio; *CI* confidence interval; *TE* total effect in presence of the mediator; *PNDE* pure natural direct effect; *TNIE* total natural indirect effect; *PM* proportion mediated
Moreover, BMI was positively associated with OAC risk (HR = 1.08 per 1 kg/m² higher BMI, 95% CI 1.04–1.11), but inversely associated with HNC (HR = 0.98 per 1 kg/m² higher BMI, 95% CI 0.96–0.99). WHR was positively associated with the risk of both HNC (HR = 1.02 per 0.01 higher WHR, 95% CI 1.01–1.03) and OAC (HR = 1.06 per 0.01 higher WHR, 95% CI 1.04–1.08).

### WHR as a mediator between UPF consumption and HNC risk

Only a small part of the positive association between UPF consumption (in %g/d) and HNC risk was mediated via WHR (5%, 95% CI 3–10%, *p* < 0.001; TNIE HR = 1.01 per 10% g/d higher UPF intake, 95% CI 1.01–1.01) (Table 2). Most of the association was not explained by WHR (PNDE HR = 1.22 per 10% g/d higher UPF intake, 95% CI 1.11–1.32). Furthermore, there was some evidence of an interaction between UPF consumption and WHR (*p*-value for interaction = 0.03).

### WHR as a mediator between UPF consumption and OAC risk

The TE of UPF consumption (in %g/d) on OAC risk was decomposed into a PNDE of 1.17 (95% CI 0.99–1.33) and a TNIE via WHR of 1.03 (95% CI 1.02–1.03) (Table 2). Hence, the proportion mediated by WHR was 15% (95% CI 8–72%, *p* = 0.03) in the association with HNC.

### BMI as a mediator between UPF consumption and OAC risk

Most of the association between UPF consumption (in %g/d) and higher OAC risk was not mediated via BMI (PNDE HR = 1.18 per 10% g/d higher UPF intake, 95% CI 1.00–1.34) (Table 2). BMI mediated 13% (95% CI 6–53%, *p* = 0.04) of the association (TNIE HR = 1.02 per 10% g/d higher UPF intake, 95% CI 1.01–1.03).

### Sensitivity analyses for the associations between ultra-processed food consumption and upper-aerodigestive tract cancers

Further adjusting for total water intake (including water in foods) or total energy intake produced similar results to those obtained in the main analyses (Supplementary Tables 7 and 8). Excluding participants censored in the first two years of follow-up (N = 442,536, since 7575 were excluded) did not substantially affect our results either (Supplementary

Table 9). Likewise, the complete case analysis results (N = 419,590, of which 851 had HNC and 191 had OAC) and the results obtained using multiple imputation were similar to those obtained in our main analysis (Supplementary Tables 10 and 11).

Repeating the analyses using either the absolute intake of UPFs in grams per day (g/d) or the relative intake of UPFs in kilocalories per day (%kcal/d) as the exposure produced comparable results to those obtained in the main analysis (Supplementary Table 12 and Supplementary Fig. 9). Nevertheless, using the absolute intake of UPFs in kilocalories per day (kcal/d) as the exposure produced slightly different results to those in the main analysis, namely because UPF consumption was no longer associated with HNC risk (HR = 1.01 per 100 kcal/d higher UPF intake, 95% CI 0.99–1.03) (Supplementary Table 12 and Supplementary Fig. 9).

Lastly, we found a positive association between UPF consumption (%g/d) and accidental deaths (HR = 1.12 per 10% g/d higher UPF intake, 95% CI 1.02–1.23) in the negative control outcome analysis, after accounting for all the covariates included in the upper-aerodigestive tract cancer models (Supplementary Table 13). This association also withstood additional adjustments for BMI and type 2 diabetes mellitus (i.e. factors that may influence recovery after an accident) (results not shown).

## Discussion

In this large prospective cohort, UPF consumption (in %g/d) was associated with an increased risk of HNC and OAC. We did not find evidence of heterogeneity between HNC subtype association estimates. Furthermore, the positive association between UPF intake and HNC may be stronger in males than in females. Our negative control analysis suggests that at least part of the observed associations between the consumption of UPFs and the risk of upper-aerodigestive tract cancers is likely due to the influence of residual confounding. However, this does not necessarily mean that the associations are entirely non-causal; only that any causal estimate is likely smaller than we observed. In our mediation analysis, adiposity (i.e. BMI and WHR) only mediated a small proportion of the positive associations between UPF consumption and HNC and OAC.

Apart from the study conducted by Kliemann et al. [9] (which motivated our research), only one other study has investigated the associations between UPF consumption
and HNC and OAC risk. Chang et al. [8] did not find
an association between UPF consumption and the risk
of HNC and OAC in the UK Biobank, in contrast to
the findings in EPIC. A possible explanation for the
null results in the UK Biobank could be limited power
(197,426 UK Biobank participants of which 342 and 186,
respectively, developed HNC and OAC over a median
follow-up of 10 years, versus 450,111 EPIC participants
of which 910 and 250, respectively, developed HNC and
OAC over a median follow-up of 15 years). Residual
confounding and the fact that the FFQs used were not
designed to capture the extent and purpose of food
processing may also partly explain the inconsistencies
between studies.

Our mediation results are in line with existing
findings. First, UPFs have been associated with excess
weight (i.e. obesity and BMI) and central adiposity (i.e.
WHR and waist circumference) in several observational
studies [10–13]. UPFs are highly palatable energy-dense
foods with low nutritional quality. They are convenient,
cheap, and often sold in large portions [4, 54]. This, in
addition to their reduced satiety potential [55], favours
the consumption of large portions and an excessive
amount of calories. Some studies even suggest that the
consumption of UPFs may disrupt the gut microbiota,
induce inflammation, and cause endocrine changes that
disturb energy balance and increase the risk of obesity
[11, 56, 57]. Second, multiple studies suggest that excess
weight and abdominal obesity are positively associated
with OAC risk[15–20, 58], and that central adiposity may
be a risk factor for HNC [21, 22]. Hence, it is plausible
that BMI and WHR mediate the association between UPF
consumption and OAC risk, and that WHR mediates the
association between UPF consumption and HNC risk.
Notwithstanding, our findings indicate that the mediated
effects via BMI and WHR are small and that other
mechanisms are likely involved.

A review of prospective cohort studies suggested that
diet quality did not play an important role in the posi-
tive associations between UPF consumption, obesity and
obesity-related outcomes (e.g. cancer), since the adjust-
ment for several dietary factors did not substantially
attenuate the associations [59]. The authors argue that
ultra-processing itself may be associated with disease
risk, independent of nutritional quality. This has profound
implications for the food industry, as it could mean that
UPF reformulation would not be sufficient to tackle the
risks associated with UPF consumption. We acknowledge

that this may just be another case of ‘highly consequential
but misleading findings’ [60], where the observed asso-
ciations are not causal but rather an artefact of residual
confounding [61, 62] (the association of UPF consump-
tion with accidental death in EPIC provides some sup-
port for this interpretation). However, if these associa-
tions truly reflect causality, the presence of carcinogenic
compounds in UPFs, such as neo-formed contaminants
produced during heat treatment, contaminants transferred
from packaging materials and additives used to preserve
and improve the organoleptic properties of food [5] may
partly explain the relation between UPF intake and upper-
aerodigestive tract cancer risk.

In sensitivity analyses, we did not find an association
between a 100 kcal/d higher UPF consumption and
HNC risk. This could be because the consumption
of artificially sweetened UPFs (which may contain
potentially carcinogenic compounds like aspartame
and 4-methylimidazole [63]) is likely disregarded or
underestimated when using kcal/d as a measure of
UPF intake. We acknowledge that this is an arguable
hypothesis since Chazelas et al. [64] did not find an
association between artificially sweetened drinks and
cancer in the NutriNet-Santé cohort. Nevertheless, a study
in the same cohort found a positive association between
higher artificial sweetener intake from all food sources
and cancer risk, suggesting other artificially sweetened
UPFs (e.g. yoghurts, breakfast cereals, gelatine desserts)
may play a role in cancer incidence [65].

Some of the strengths of this study include the large
sample size of the EPIC cohort and its long follow-up
time. The prospective nature of EPIC and the availability
of measured rather than self-reported BMI and WHR
were also advantages. Moreover, EPIC's multi-centre
design increases the diversity of our study sample.
Another advantage is that cancer cases were detected
through registries (which provide detailed information on
cancer subtypes) and active follow-up methods, both of
which are unlikely to be affected by measurement error.
Finally, the use of several measures of UPF intake (%g/d,
g/d, %kcal/d and kcal/d) as the exposure makes our
results more comparable to previous studies investigating
the associations between the consumption of UPFs and
cancer [5-9].

We acknowledge that this study has several limitations.
For instance, in our mediation analysis we assumed that
the associations between UPF consumption and upper-
aerodigestive tract cancers were not influenced by

<figure><img src="image_6.png" /></figure>
residual confounding or measurement error. These are
strong assumptions since residual confounding due to
unmeasured (e.g. human papilloma-virus infection) or
imprecisely measured (e.g. smoking and alcohol intake)
confounders inevitably biased our estimates to some
extent (since estimates changed when the models were
adjusted for potential confounders for which some values
were missing or likely measured with error). Indeed, the
fact that our negative control outcome analysis suggested
that UPF consumption may be associated with accidental
deaths points to the possibility of residual confounding.

For our mediation analysis, we used data on BMI and WHR collected at baseline. Admittedly, a limitation with this approach is that the exposure data were not gathered prior to the mediator data, so we cannot be certain that the exposure temporally precedes the mediator. Follow-up data on measured BMI and WHR were only available for 5% and 27% of the participants who answered the lifestyle follow-up questionnaire (N = 349,283), respectively. Unfortunately, cancer cases among participants with complete follow-up data were insufficient for us to conduct any sensitivity analyses using follow-up data.

An additional limitation is that we assumed that BMI and WHR mediated the association between UPF consumption and OAC risk through separate pathways. Since BMI and WHR are correlated, it would be incorrect to assume that the proportion mediated via adiposity equals the sum of the proportions mediated through both BMI and WHR. Therefore, in this association, the proportion mediated via adiposity is likely less than 28% (13% via BMI plus 15% via WHR).

Another issue is that the FFQs used at baseline were
not designed to distinguish between NOVA groups [66],
potentially leading to random misclassification bias and
the weakening of our association estimates. Nonetheless,
Huybrechts et al. [31] found positive correlations between
UPF consumption and food processing biomarkers (i.e.
plasma elaidic acid, an unsaturated trans-fatty acid, and
urinary 4-methylsyringol sulphate) in EPIC, suggesting
that UPFs were likely correctly identified in the dataset.
Also, the dietary data used in our analyses were collected

only once, at baseline in the 1990s, when the availability
and consumption of UPFs was relatively lower than today.
Hence, this study relies on the somewhat unrealistic
assumption that UPF intake was rather low and did
not increase over time [67]. When Kliemann et al. [9]
explored the association between the consumption of
UPFs and cancer in EPIC under a hypothetical “upper
bound scenario” (where foods were classified into NOVA
groups based on their highest degree of processing
possible), results were similar to those obtained under
the more conservative “middle bound scenario” used
in our study (where foods were classified into NOVA
groups based on their most likely degree of processing at
the time of dietary data collection). Although the use of
a hypothetical “upper bound scenario” may account for
some of the changes in dietary intake during follow-up,
regression dilution bias was still an issue Kliemann et al.
[9] could not account for in their analyses. Consequently,
dietary questionnaire-related biases may have led to
the underestimation of the association between UPF
consumption and cancer in the EPIC cohort.

In conclusion, we reaffirmed that UPF intake is
associated with an increased risk of HNC and OAC in
the EPIC study. Since BMI and WHR explain little of
the associations between UPF consumption and upper-
aerodigestive tract cancers, further research is required
to investigate other mechanisms that may be at play (if
there is indeed any causal effect of UPF consumption
on these cancers). Our results are likely influenced by
residual confounding, as indicated by the negative control
analysis. Therefore, our findings should be regarded with
caution until they are replicated in other settings (i.e.
in populations with different underlying confounding
structures) or triangulated with evidence obtained using
other methodological approaches.

Appendix

See Table 3
<figure><img src="image_7.png" /><figcaption>**Table 3** STROBE statement—checklist of items that should be included in reports of observational studies</figcaption></figure>

<table><thead><tr><th></th><th>Item no</th><th>Recommendation</th><th>Page no</th><th>Relevant text from manuscript</th></tr></thead><tbody><tr><td>Title and abstract</td><td>1</td><td>(a) Indicate the study's design with a commonly used term in the title or the abstract<br/>(b) Provide in the abstract an informative and balanced summary of what was done and what was found</td><td>3</td><td>"European Prospective Investigation into Cancer and Nutrition (EPIC) cohort"<br/>"We used Cox regressions to investigate the associations..."</td></tr><tr><td>Introduction</td><td>2</td><td>Explain the scientific background and rationale for the investigation being reported</td><td>4</td><td>"In recent years, there has been growing interest ... this has not been investigated using mediation analysis"</td></tr><tr><td>Background/rationale</td><td>3</td><td>State specific objectives, including any prespecified hypotheses</td><td>4–5</td><td>"The aim of this study was..."</td></tr><tr><td>Objectives</td><td>4</td><td>Present key elements of study design early in the paper</td><td>6</td><td>"The EPIC cohort" subsection</td></tr><tr><td>Methods</td><td>5</td><td>Describe the setting, locations, and relevant dates, including periods of recruitment, exposure, follow-up, and data collection</td><td>6</td><td>"The EPIC cohort" and "Study sample" subsections</td></tr><tr><td>Study design</td><td>6</td><td>(a) Cohort study—Give the eligibility criteria, and the sources and methods of selection of participants. Describe methods of follow-up<br/>Case-control study—Give the eligibility criteria, and the sources and methods of case ascertainment and control selection. Give the rationale for the choice of cases and controls<br/>Cross-sectional study—Give the eligibility criteria, and the sources and methods of selection of participants</td><td>6–7</td><td>"Ascertainment of cancer cases" and "Study sample" subsections</td></tr><tr><td>Setting</td><td>6</td><td>(b) Cohort study—For matched studies, give matching criteria and number of exposed and unexposed<br/>Case-control study—For matched studies, give matching criteria and the number of controls per case</td><td>6–8</td><td>"Dietary data and food processing variables", "Ascertainment of cancer cases", "Covariates" and "Potential mediators" subsections</td></tr><tr><td>Participants</td><td>6</td><td>Clearly define all outcomes, exposures, predictors, potential confounders, and effect modifiers. Give diagnostic criteria, if applicable<br/>For each variable of interest, give sources of data and details of methods of assessment (measurement). Describe comparability of assessment methods if there is more than one group</td><td>6–8</td><td>"Dietary data and food processing variables", "Ascertainment of cancer cases", "Covariates" and "Potential mediators" subsections</td></tr><tr><td>Variables</td><td>7</td><td>Clearly define all outcomes, exposures, predictors, potential confounders, and effect modifiers. Give diagnostic criteria, if applicable<br/>Describe any efforts to address potential sources of bias</td><td>6–8</td><td>"Model 1 was stratified by age at recruitment in 1-year categories, sex, and sub-centre. Model 2 was additionally adjusted for education, physical activity, height, and smoking status. Model 3 was additionally adjusted for alcohol intake in g/d to reflect the association between the consumption of foods by their degree of processing and cancer, regardless of alcohol intake"</td></tr><tr><td>Data sources/ measurement</td><td>8*</td><td></td><td>9</td><td>"Study sample" subsection<br/>"Covariates", "Potential mediators" and "Statistical analysis" subsections</td></tr><tr><td>Bias</td><td>9</td><td></td><td>9</td><td></td></tr><tr><td>Study size</td><td>10</td><td>Explain how the study size was arrived at</td><td>6</td><td></td></tr><tr><td>Quantitative variables</td><td>11</td><td>Explain how quantitative variables were handled in the analyses. If applicable, describe which groupings were chosen and why</td><td>8–12</td><td></td></tr></tbody></table>
<table><caption>**Table 3 (continued)**</caption><thead><tr><th></th><th>Item no</th><th>Recommendation</th><th>Page no</th><th>Relevant text from manuscript</th></tr></thead><tbody><tr><td>Statistical methods</td><td>12</td><td>(a) Describe all statistical methods, including those used to control for confounding<br/>(b) Describe any methods used to examine subgroups and interactions<br/>(c) Explain how missing data were addressed<br/>(d) Cohort study—If applicable, explain how loss to follow-up was addressed<br/>Case-control study—If applicable, explain how matching of cases and controls was addressed<br/>Cross-sectional study—If applicable, describe analytical methods taking account of sampling strategy<br/>(e) Describe any sensitivity analyses</td><td>8–12</td><td>“Statistical analysis” subsection</td></tr><tr><td>Results</td><td></td><td></td><td>11</td><td></td></tr><tr><td>Participants</td><td>13*</td><td>(a) Report numbers of individuals at each stage of study—e.g. numbers potentially eligible, examined for eligibility, confirmed eligible, included in the study, completing follow-up, and analysed<br/>(b) Give reasons for non-participation at each stage<br/>(c) Consider use of a flow diagram</td><td>6</td><td>“Study sample” subsection in “Methods” section</td></tr><tr><td>Descriptive data</td><td>14*</td><td>(a) Give characteristics of study participants (e.g. demographic, clinical, social and information on exposures and potential confounders<br/>(b) Indicate number of participants with missing data for each variable of interest<br/>(c) Cohort study—Summarise follow-up time (e.g. average and total amount)<br/>Cohort study—Report numbers of outcome events or summary measures over time<br/>Case-control study—Report numbers in each exposure category, or summary measures of exposure<br/>Cross-sectional study—Report numbers of outcome events or summary measures</td><td>13–14</td><td>Supplement NA<br/>Supplementary Fig. 1. Flowchart of European Prospective Investigation into Cancer and Nutrition (EPIC) participants included in the study<br/>“Descriptive characteristics” subsection</td></tr><tr><td>Outcome data</td><td>15*</td><td></td><td>14</td><td>“During a mean follow-up of 14.13±3.98 years (6,358,569 person-years, median follow-up = 14.95 years; range 1 day to 22.79 years)”<br/>“During a mean follow-up of 14.13±3.98 years (6,358,569 person-years, median follow-up = 14.95 years; range 1 day to 22.79 years)”</td></tr></tbody></table>

<figure><img src="image_8.png" /></figure>
<figure><img src="image_9.png" /></figure>

<table><caption>**Table 3 (continued)**</caption><thead><tr><th></th><th>Item no</th><th>Recommendation</th><th>Page no</th><th>Relevant text from manuscript</th></tr></thead><tbody><tr><td>Main results</td><td>16</td><td>(a) Give unadjusted estimates and, if applicable, confounder-adjusted estimates and their precision (e.g. 95% confidence interval). Make clear which confounders were adjusted for and why they were included<br/>(b) Report category boundaries when continuous variables were categorised</td><td>14–18</td><td>“Associations between the consumption of ultra-processed foods and upper-aerodigestive tract cancers” and “Mediating role of adiposity in the associations between ultra-processed food consumption and upper-aerodigestive tract cancers” subsections<br/>“Furthermore, we stratified Model 3 (for every exposure-outcome combination) by alcohol intake (as defined by Wozniak et al. [45], i.e. no/light alcohol intake [0.1–6 g/d (men); 0.1–3 g/d (women)], moderate alcohol intake [6.1–24 g/d (men); 3.1–24 g/d (women)], heavy alcohol intake (&gt;24 g/d])”</td></tr><tr><td>Other analyses</td><td>17</td><td>(c) If relevant, consider translating estimates of relative risk into absolute risk for a meaningful time period<br/>Report other analyses done—e.g. analyses of subgroups and interactions, and sensitivity analyses</td><td>NA</td><td>NA</td></tr><tr><td>Discussion</td><td></td><td></td><td></td><td></td></tr><tr><td>Key results</td><td>18</td><td>Summarise key results with reference to study objectives</td><td>19</td><td>“In this large prospective cohort, UPF consumption (in %g/d) was associated with...”</td></tr><tr><td>Limitations</td><td>19</td><td>Discuss limitations of the study, taking into account sources of potential bias or imprecision. Discuss both direction and magnitude of any potential bias</td><td>20–21</td><td>“We acknowledge that this study has several limitations... dietary questionnaire-related biases may have led to the underestimation of the association between UPF consumption and cancer in the EPIC cohort.”</td></tr><tr><td>Interpretation</td><td>20</td><td>Give a cautious overall interpretation of results considering objectives, limitations, multiplicity of analyses, results from similar studies, and other relevant evidence</td><td>19–21</td><td>“Discussion” section</td></tr><tr><td>Generalisability</td><td>21</td><td>Discuss the generalisability (external validity) of the study results</td><td>19–21</td><td>“Discussion” section</td></tr><tr><td>Other information</td><td></td><td></td><td></td><td></td></tr><tr><td>Funding</td><td>22</td><td>Give the source of funding and the role of the funders for the present study and, if applicable, for the original study on which the present article is based</td><td>23</td><td>“Funding” subsection in the “Statements & Declarations” section</td></tr></tbody></table>

Note: An Explanation and Elaboration article discusses each checklist item and gives methodological background and published examples of transparent reporting. The STROBE checklist is best used in conjunction with this article (freely available on the Web sites of PLoS Medicine at http://www.plosmedicine.org/, Annals of Internal Medicine at http://www.annals.org/, and Epidemiology at http://www.epidem.com/). Information on the STROBE Initiative is available at www.strobe-statement.org

*Give information separately for cases and controls in case-control studies and, if applicable, for exposed and unexposed groups in cohort and cross-sectional studies
**Supplementary Information** The online version contains supplementary material available at https://doi.org/10.1007/s00394-023-03270-1.

**Acknowledgements** We acknowledge the use of data from the EPIC cohort and thank the participants and investigators for their contributions to the EPIC resource. We also thank the Asturias Government and the National Institute for Public Health and the Environment (RIVM), Bilthoven in the Netherlands, for their contribution and ongoing support to the EPIC study. We additionally thank Richard Wilkinson for proofreading the manuscript.

**Author contributions** FMB, IH, CB, CC, MG, AA, PB, NK, AGS and VV contributed to the study conception and design. CC, CB and BH prepared the dataset. FMB conducted the analysis and wrote the first draft of the manuscript. IH, CLR, GDS, TGR, MCB, VV and AGS provided comments on the first draft of the manuscript. All authors commented on other versions of the manuscript. All authors read and approved the final version of the manuscript.

**Funding** FMB was supported by a Wellcome Trust PhD studentship in Molecular, Genetic and Lifecourse Epidemiology (224982/Z/22/Z). RCR was supported by a Cancer Research UK grant (C18281/A29019). AGS is supported by the study of Dynamic longitudinal exposome trajectories in cardiovascular and metabolic non-communicable diseases [H2020-SC1-2019-Single-Stage-RTD, project ID 874739]. MCB is supported by a University of Bristol Vice Chancellor's Fellowship, the British Heart Foundation (AA/18/1/34219) and the UK Medical Research Council (MC_UU_00011/6). GDS works within the MRC Integrative Epidemiology Unit at the University of Bristol, which is supported by the Medical Research Council (MC_UU_00011/1). CLR was supported by the Medical Research Council (MC_UU_00011/5) and by a Cancer Research UK (C18281/A29019) programme grant (the Integrative Cancer Epidemiology Programme). This work was supported by Cancer Research UK (C33493/A29678), World Cancer Research Fund International (IIG_FULL_2020_033) and the Institut National du Cancer (INCa number 2021-138). The coordination of EPIC is financially supported by the International Agency for Research on Cancer (IARC) and by the Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London which has additional infrastructure support provided by the NIHR Imperial Biomedical Research Centre (BRC). The national cohorts are supported by: Danish Cancer Society (Denmark); Ligue Contre le Cancer, Institut Gustave Roussy, Mutuelle Générale de l'Éducation Nationale, Institut National de la Santé et de la Recherche Médicale (INSERM) (France); German Cancer Aid, German Cancer Research Center (DKFZ), German Institute of Human Nutrition Potsdam-Rehbruecke (DIfE), Federal Ministry of Education and Research (BMBF) (Germany); Associazione Italiana per la Ricerca sul Cancro-AIRC-Italy, Compagnia di SanPaolo and National Research Council (Italy); Dutch Ministry of Public Health, Welfare and Sports (VWS), Netherlands Cancer Registry (NKR), LK Research Funds, Dutch Prevention Funds, Dutch ZON (Zorg Onderzoek Nederland), World Cancer Research Fund (WCRF), Statistics Netherlands (The Netherlands); Health Research Fund (FIS)—Instituto de Salud Carlos III (ISCIII), Regional Governments of Andalucía, Asturias, Basque Country, Murcia and Navarra, and the Catalan Institute of Oncology—ICO (Spain); Swedish Cancer Society, Swedish Research Council and County Councils of Skåne and Västerbotten (Sweden); Cancer Research UK (14136 to EPIC-Norfolk; C8221/A29017 to EPIC-Oxford), Medical Research Council (1000143 to EPIC-Norfolk; MR/M012190/1 to EPIC-Oxford). (United Kingdom).

**Data availability and materials** The data described in the manuscript will be made available upon request pending application and approval. The analytic code will be made publicly and freely available without restriction at https://github.com/fernandam93/UPF_adiposity_UADT.

## Declarations

**Conflict of interest** Financial interests: TGR is an employee of GlaxoSmithKline outside of this research. The rest of the authors have no financial interests to disclose. Non-financial interests: The authors have no non-financial interests to declare. The authors declare that no funds, grants or other support were received during the preparation of this manuscript.

**Ethical approval** The EPIC study was approved by the IARC Ethics Committee and all local ethics committees. This project was conducted under approval number 21–50.

**Consent to participate** Written informed consent was obtained from all individual participants included in the study.

**IARC disclaimer** Where authors are identified as personnel of the International Agency for Research on Cancer/World Health Organization, the authors alone are responsible for the views expressed in this article and they do not necessarily represent the decisions, policy or views of the International Agency for Research on Cancer/World Health Organization.

**Open Access** This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

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## Authors and Affiliations

Fernanda Morales-Berstein¹,² ID · Carine Biessy³ · Vivian Viallon³ · Ana Goncalves-Soares¹,² · Corinne Casagrande³ ·

Bertrand Hémon³ · Nathalie Kliemann³,⁴ · Manon Cairat³,⁵ · Jessica Blanco Lopez³ · Aline Al Nahas³ · Kiara Chang⁶ ·

Eszter Vamos⁶ · Fernanda Rauber⁷,⁸ · Renata Bertazzi Levy⁷,⁸ · Diana Barbosa Cunha⁹ · Paula Jakszyn¹⁰,¹¹,¹² ·

Pietro Ferrari³ · Paolo Vineis¹³,¹⁴ · Giovanna Masala¹⁵ · Alberto Catalano¹⁶ ID · Emily Sonestedt¹⁷ ·

Yan Borné¹⁷ · Verena Katzke¹⁸ · Rashmita Bajracharya¹⁸ · Claudia Agnoli¹⁹ · Marcela Guevara²⁰,²¹,²² ·

Alicia Heath²³ · Loredana Radoi⁵ · Francesca Mancini⁵ · Elisabete Weiderpass²⁴ · José María Huerta²¹,²⁵ ·

María-José Sánchez²¹,²⁶,²⁷,²⁸ · Anne Tjønneland²⁹,³⁰ · Cecilie Kyrø²⁹ · Matthias B. Schulze³¹,³² · Guri Skeie³³ ·

Marko Lukic³³ · Tonje Braaten³³ · Marc Gunter³,²³ · Christopher Millett⁶,³⁴ · Antonio Agudo¹⁰,¹¹ · Paul Brennan³⁵ ·

M. Carolina Borges¹,² · Rebecca C. Richmond¹,² · Tom G. Richardson¹,² · George Davey Smith¹,² · Caroline L. Relton¹,² ·

Inge Huybrechts³ · on behalf of the EPIC Network
<figure><img src="image_10.png" /></figure>

Fernanda Morales-Berstein
dy20206@bristol.ac.uk

1 MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK

2 Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK

3 Nutrition and Metabolism Branch, International Agency for Research on Cancer/World Health Organization, Lyon, France

4 Cancer Research Center of Santa Catarina, CEPON, Florianópolis, Brazil

5 Paris-Saclay University, UVSQ, Inserm "Exposome, Heredity, Cancer and Health" Team, CESP U1018, Gustave Roussy, Villejuif, France

6 Public Health Policy Evaluation Unit, School of Public Health, Imperial College London, London, UK

7 Preventive Medicine Department of the Medical School, University of São Paulo, São Paulo, Brazil

8 Center for Epidemiological Research in Nutrition and Health, University of São Paulo, São Paulo, Brazil

9 Hésio Cordeiro Institute of Social Medicine, Department of Epidemiology, Rio de Janeiro State University, Rio de Janeiro, RJ, Brazil

10 Unit of Nutrition and Cancer, Catalan Institute of Oncology-ICO, L'Hospitalet de Llobregat, Spain

11 Nutrition and Cancer Group; Epidemiology, Public Health, Cancer Prevention and Palliative Care Program, Bellvitge Biomedical Research Institute-IDIBELL, L'Hospitalet de Llobregat, Spain

12 Blanquerna Faculty of Health Sciences, Ramon Llull University, Barcelona, Spain

13 MRC Centre for Environment and Health, School of Public Health, Imperial College London, Norfolk Place, London W2 1PG, UK

14 Italian Institute of Technology, Genoa, Italy

15 Institute for Cancer Research, Prevention and Clinical Network (ISPRO), Florence, Italy

16 Centre for Biostatistics, Epidemiology, and Public Health, Department of Clinical and Biological Sciences, University of Turin, 10043 Orbassano, TO, Italy

17 Nutrition Epidemiology, Department of Clinical Sciences Malmö, Faculty of Medicine, Lund University, Lund, Sweden

18 Department of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany

19 Epidemiology and Prevention Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy

20 Instituto de Salud Pública y Laboral de Navarra, 31003 Pamplona, Spain

21 Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), 28029 Madrid, Spain

22 Navarra Institute for Health Research (IdiSNA), 31008 Pamplona, Spain

23 Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK

24 International Agency for Research on Cancer, World Health Organization, Lyon, France

25 Department of Epidemiology, Murcia Regional Health Council-IMIB, Murcia, Spain

26 Escuela Andaluza de Salud Pública (EASP), 18011 Granada, Spain

27 Instituto de Investigación Biosanitaria ibs.GRANADA, 18012 Granada, Spain

28 Department of Preventive Medicine and Public Health, University of Granada, 18071 Granada, Spain

29 Danish Cancer Society Research Center, Diet, Cancer and Health, Strandboulevarden 49, DK-2100 Copenhagen, Denmark

30 Department of Public Health, University of Copenhagen, DK-2200 Copenhagen, Denmark

31 Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany

32 Institute of Nutritional Science, University of Potsdam, Nuthetal, Germany

33 Department of Community Medicine, Faculty of Health Sciences, UiT The Arctic University of Norway, Tromsø, Norway

34 NOVA National School of Public Health, Public Health Research Centre, Comprehensive Health Research Center, CHRC, NOVA University Lisbon, Lisbon, Portugal

35 Genetic Epidemiology Group, International Agency for Research on Cancer, World Health Organization, Lyon, France

<figure><img src="image_11.png" /></figure>