nutrients Article Use of Different Food Classification Systems to Assess the Association between Ultra-Processed Food Consumption and Cardiometabolic Health in an Elderly Population with Metabolic Syndrome (PREDIMED-Plus Cohort) Celia Martinez-Perez 1 , Rodrigo San-Cristobal 2, * , Pilar Guallar-Castillon 3,4,5,6 , Miguel Ángel Martínez-González 7,8,9 , Jordi Salas-Salvadó 7,10,11 , Dolores Corella 7,12 , Olga Castañer 7,13 , Jose Alfredo Martinez 2,7,14 , Ángel M. Alonso-Gómez 7,15 , Julia Wärnberg 7,16 , Jesús Vioque 5,17 , Dora Romaguera 7,18 , José López-Miranda 7,19 , Ramon Estruch 7,20 , Francisco J. Tinahones 7,21 , José Lapetra 7,22 , Lluis Serra-Majem 7,23 , Aurora Bueno-Cavanillas 5,24 , Josep A. Tur 7,25 , Vicente Martín Sánchez 5,26 , Xavier Pintó 7,27 , José J. Gaforio 5,28 , Pilar Matía-Martín 29 , Josep Vidal 30,31 , Clotilde Vázquez 7,32 , Emilio Ros 7,31 , Maira Bes-Rastrollo 7,8 , Nancy Babio 7,10,11 , Jose V. Sorlí 7,12 , Camille Lassale 7,13 , Beatriz Pérez-Sanz 14 , Jessica Vaquero-Luna 7,15 , María Julia Ajejas Bazán 16,33 , María Concepción Barceló-Iglesias 34 , Jadwiga Konieczna 7,18 , Antonio García Ríos 7,19 , María Rosa Bernal-López 35 , José Manuel Santos-Lozano 7,22 , Estefanía Toledo 7,8 , Nerea Becerra-Tomás 7,10,11,12 , Olga Portoles 7,12 , María Dolores Zomeño 13,36 , Itziar Abete 7,14 , Anai Moreno-Rodriguez 7,15 , Oscar Lecea-Juarez 37 , Stephanie K. Nishi 10 , Júlia Muñoz-Martínez 13 , José M. Ordovás 1,38 and Lidia Daimiel 1  1 Nutritional Genomics and Epigenomics Group, Precision Nutrition and Obesity Program, IMDEA Food,  CEI UAM + CSIC, 28049 Madrid, Spain; celia.martinez@imdea.org (C.M.-P.); jose.ordovas@tufts.edu (J.M.O.); Citation: Martinez-Perez, C.; San- lidia.daimiel@imdea.org (L.D.) Cristobal, R.; Guallar-Castillon, P.; 2 Cardiometabolic Nutrition Group, Precision Nutrition and Cardiometabolic Health Program, IMDEA Food, Martínez-González, M.Á.; Salas-Salvadó, J.; Corella, D.; CEI UAM + CSIC, 28049 Madrid, Spain; jalfmtz@unav.es Castañer, O.; Martinez, J.A.; 3 Cardiovascular and Nutritional Epidemiology Group, IMDEA Food, CEI UAM + CSIC, 28049 Madrid, Spain; Alonso-Gómez, Á.M.; Wärnberg, J.; mpilar.guallar@uam.es et al. Use of Different Food 4 Classification Systems to Assess the Department of Preventive Medicine and Public Health, School of Medicine, Universidad Autónoma de Association between Ultra-Processed Madrid—IdiPaz Hospital, 28046 Madrid, Spain Food Consumption and 5 Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Cardiometabolic Health in an Elderly Instituto de Salud Carlos III, 28029 Madrid, Spain; vioque@umh.es (J.V.); abueno@ugr.es (A.B.-C.); Population with Metabolic Syndrome (PREDIMED-Plus Cohort). Nutrients vicente.martin@unileon.es (V.M.S.); jgaforio@ujaen.es (J.J.G.) 2021, 13, 2471. https://doi.org/ 6 Welch Center for Prevention, Epidemiology and Clinical Research, Johns Hopkins University, Baltimore, MD 21218, USA 10.3390/nu13072471 7 Centro de Investigación Biomédica en Red Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III (ISCIII), 28029 Madrid, Spain; mamartinez@unav.es (M.Á.M.-G.); Academic Editor: Licia Iacoviello jordi.salas@urv.cat (J.S.-S.); dolores.corella@uv.es (D.C.); ocastaner@imim.es (O.C.); Received: 28 May 2021 angelmago13@gmail.com (Á.M.A.-G.); jwarnberg@uma.es (J.W.); mariaadoracion.romaguera@ssib.es (D.R.); Accepted: 13 July 2021 jlopezmir@gmail.com (J.L.-M.); restruch@clinic.cat (R.E.); fjtinahones@hotmail.com (F.J.T.); Published: 20 July 2021 joselapetra543@gmail.com (J.L.); lserra@dcc.ulpgc.es (L.S.-M.); pep.tur@uib.es (J.A.T.); xpinto@bellvitgehospital.cat (X.P.); cvazquezma@gmail.com (C.V.); eros@clinic.cat (E.R.); Publisher’s Note: MDPI stays neutral mbes@unav.es (M.B.-R.); nancy.babio@urv.cat (N.B.); sorli@uv.es (J.V.S.); classale@imim.es (C.L.); with regard to jurisdictional claims in luna_jess_@hotmail.com (J.V.-L.); jadzia.konieczna@gmail.com (J.K.); angarios2004@yahoo.es (A.G.R.); josemanuel.santos.lozano@gmail.com (J.M.S.-L.); etoledo@unav.es (E.T.); nerea.becerra@urv.cat (N.B.-T.); published maps and institutional affil- olga.portoles@uv.es (O.P.); iabetego@unav.es (I.A.); anai.m.rodriguez@gmail.com (A.M.-R.) iations. 8 Department of Preventive Medicine and Public Health, University of Navarra, IdiSNA, 31009 Pamplona, Spain 9 Department of Nutrition, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA 10 Unitat de Nutrició Humana, Departament de Bioquímica i Biotecnologia, Universitat Rovira i Virgili, 43201 Reus, Spain; stephanie.nishi@urv.cat 11 Human Nutrition Unit, Institut d’Investigació Sanitària Pere Virgili (IISPV), 43204 Reus, Spain Copyright: © 2021 by the authors. 12 Department of Preventive Medicine, University of Valencia, 46010 Valencia, Spain Licensee MDPI, Basel, Switzerland. 13 Cardiovascular Risk and Nutrition Research Group (CARIN), Hospital del Mar Medical This article is an open access article Research Institute (IMIM), 08003 Barcelona, Spain; mzomeno@imim.es (M.D.Z.); distributed under the terms and juliamm02@gmail.com (J.M.-M.) conditions of the Creative Commons 14 Department of Nutrition, Food Sciences and Physiology, University of Navarra, 31009 Pamplona, Spain; Attribution (CC BY) license (https:// beatriz.perez.sanz@navarra.es 15 Bioaraba Health Research Institute, Osakidetza Basque Health Service, Araba University Hospital, creativecommons.org/licenses/by/ 4.0/). University of the Basque Country UPV/EHU, 01009 Vitoria-Gasteiz, Spain Nutrients 2021, 13, 2471. https://doi.org/10.3390/nu13072471 https://www.mdpi.com/journal/nutrients Nutrients 2021, 13, 2471 2 of 18 16 Department of Nursing, School of Health Sciences, Instituto de Investigación Biomédica de Málaga (IBIMA), University of Málaga, 29016 Málaga, Spain; majejas@ucm.es 17 Instituto de Investigación Sanitaria y Biomédica de Alicante (ISABIAL-UMH), 03010 Alicante, Spain 18 Research Group on Nutritional Epidemiology & Cardiovascular Physiopathology (NUTRECOR), Health Research Institute of the Balearic Islands (IdISBa), University Hospital Son Espases (HUSE), 07120 Palma de Mallorca, Spain 19 Lipids and Atherosclerosis Unit, Department of Internal Medicine, Maimonides Biomedical Research Institute of Cordoba (IMIBIC), Reina Sofia University Hospital, University of Cordoba, 14071 Córdoba, Spain 20 Department of Internal Medicine, IDIBAPS, Hospital Clinic, University of Barcelona, 08007 Barcelona, Spain 21 Department of Endocrinology, Instituto de Investigación Biomédica de Málaga (IBIMA), Virgen de la Victoria Hospital, University of Málaga, 29016 Málaga, Spain 22 Department of Family Medicine, Research Unit, Distrito Sanitario Atención Primaria Sevilla, 41013 Sevilla, Spain 23 Research Institute of Biomedical and Health Sciences (IUIBS), University of Las Palmas de Gran Canaria and Service of Preventive Medicine, Complejo Hospitalario Universitario Insular Materno Infantil (CHUIMI), Canary Health Service, 35001 Las Palmas de Gran Canaria, Spain 24 Department of Preventive Medicine and Public Health, University of Granada, 18011 Granada, Spain 25 Research Group on Community Nutrition & Oxidative Stress, University of Balearic Islands-IUNICS & IDISBA, 07122 Palma de Mallorca, Spain 26 Institute of Biomedicine (IBIOMED), University of León, 24071 León, Spain 27 Lipids and Vascular Risk Unit, Internal Medicine, Hospital Universitario de Bellvitge, Hospitalet de Llobregat, 08907 Barcelona, Spain 28 Departamento de Ciencias de la Salud, Centro de Estudios Avanzados en Olivar y Aceites de Oliva, Universidad de Jaén, 23071 Jaén, Spain 29 Department of Endocrinology and Nutrition, Instituto de Investigación Sanitaria Hospital Clínico San Carlos (IdISSC), 28040 Madrid, Spain; pilar.matia@gmail.com 30 Biomedical Research Centre for Diabetes and Metabolic Diseases Network (CIBERDEM), Instituto de Salud Carlos III (ISCIII), 28029 Madrid, Spain; jovidal@clinic.cat 31 Endocrinology and Nutrition Service, IDIBAPS, Hospital Clinic, University of Barcelona, 08007 Barcelona, Spain 32 Department of Endocrinology and Nutrition, Hospital Fundación Jiménez Díaz, Instituto de Investigaciones Biomédicas IISFJD, University Autónoma, 28015 Madrid, Spain 33 Department of Nursing, Faculty of Nursing, Physiotherapy and Podiatry, Universidad Complutense de Madrid, 28040 Madrid, Spain 34 Centro Salud Cabo Huertas, 03540 Alicante, Spain; conchibarcelo@hotmail.com 35 Internal Medicine Department, Instituto de Investigación Biomédica de Málaga (IBIMA), Regional University Hospital of Malaga, 29010 Malaga, Spain; rosa.bernal@ibima.eu 36 School of Health Sciences, Blanquerna-Ramon Llull University, 08001 Barcelona, Spain 37 Atención Primaria, Osasunbidea, Servicio Navarro de Salud, 31003 Pamplona, Spain; oscar.lecea.juarez@navarra.es 38 Nutrition and Genomics Laboratory, JM_USDA Human Nutrition Research Center on Aging, Tufts University, Boston, MA 02155, USA * Correspondence: rodrigo.sancristobal@imdea.org; Tel.: +34-917-278-100 (ext. 309) Abstract: The association between ultra-processed food (UPF) and risk of cardiometabolic disorders is an ongoing concern. Different food processing-based classification systems have originated discrepancies in the conclusions among studies. To test whether the association between UPF consumption and cardiometabolic markers changes with the classification system, we used baseline data from 5636 participants (48.5% female and 51.5% male, mean age 65.1 ± 4.9) of the PREDIMED- Plus (“PREvention with MEDiterranean DIet”) trial. Subjects presented with overweight or obesity and met at least three metabolic syndrome (MetS) criteria. Food consumption was classified using a 143-item food frequency questionnaire according to four food processing-based classifications: NOVA, International Agency for Research on Cancer (IARC), International Food Information Council (IFIC) and University of North Carolina (UNC). Mean changes in nutritional and cardiometabolic markers were assessed according to quintiles of UPF consumption for each system. The association between UPF consumption and cardiometabolic markers was assessed using linear regression analysis. The concordance of the different classifications was assessed with intra-class correlation coefficients (ICC3, overall = 0.51). The highest UPF consumption was obtained with the IARC classification (45.9%) and the lowest with NOVA (7.9%). Subjects with high UPF consumption showed a poor dietary profile. We detected a direct association between UPF consumption and BMI (p = 0.001) when using Nutrients 2021, 13, 2471 3 of 18 the NOVA system, and with systolic (p = 0.018) and diastolic (p = 0.042) blood pressure when using the UNC system. Food classification methodologies markedly influenced the association between UPF consumption and cardiometabolic risk markers. Keywords: cardiometabolic risk; classification systems; diet; food processing; IARC; IFIC; NOVA; PREDIMED-Plus; ultra-processed food; UNC 1. Introduction Food processing and its relationship with health is a significant concern due to the impact of this processing on the nutritional food profile [1–5]. This is partly due to the increased consumption of highly industrially processed foods worldwide [6–11]. In line with this, the term “ultra-processed food” (UPF) has been introduced in the context of epidemiological studies [12–18]. Cohort studies have indicated an association between UPF consumption and risk of cardiovascular disease [19,20], type 2 diabetes [21], hyper- tension [14], irritable bowel syndrome [22], dyslipidemia [23], obesity [13,15,24–27] and cancer [18]. This association with disease may explain the greater all-cause mortality risk associated with UPF consumption shown by longitudinal studies worldwide [17,28–30]. This is relevant since diet in general, and UPF consumption in particular, is a modifiable risk factor for non-communicable diseases; thus, these diseases could be prevented through public health policies that promote healthier food choices and limit UPF consumption [28]. Food processing emerged from our ancestors’ needs to preserve food, giving rise to many beneficial qualities. These include the reduction in microbiological risk, creating new products such as bread or fermented foods, fortifying foods, and improving accessibility to nutrients [31,32]. Food processing can also have detrimental effects, as it may involve the loss of essential nutrients, addition of excessive amounts of sugar, salt, fats [8,33–36], increase in glycemic index [37], additives or even acrylamide [38,39]. This has prompted the negative connotations and perceived poor nutritional profile associated with the term UPF, although this is not always the case [40,41]. Therefore, a clear distinction between UPF and “processed foods” in studies and health reports is critical. Significant controversy has arisen around the effect of food processing on health, given the lack of a standard UPF definition [42]. As a result, studies classify processed foods using different criteria, hence rendering the comparability of their outcomes impractical and somewhat ambiguous. This lack of consistency hinders the identification of UPFs by consumers when following public health guidelines. In response to the need to characterize foods according to their processing level, multiple classification systems have been proposed [42]. The most commonly used in nutrition research is produced by NOVA [16,33,43], the International Agency for Research on Cancer (IARC) [11,44], the International Food Information Council (IFIC) [45,46] and the University of North Carolina (UNC) [47] systems. Some are based on the type and level of processing, whereas others focus on formulation and composition criteria. These differences have generated discrepancies in conclusions obtained from studies examining UPF consumption [46,48,49], highlighting the need for a commonly accepted system across studies. Here, we aimed to assess the impact of the food classification system on the cross- sectional association between UPF consumption and cardiometabolic health using the same data set. We hypothesized that applying different food processing-based classification systems to a data set would result in different associations between UPF consumption and cardiometabolic markers. 2. Materials and Methods Study population: The PREDIMED-Plus trial (from the Spanish “PREvention with MEDiterranean DIet”) is an ongoing study launched in Spain in 2013 to assess the effect 2. Materials and Methods Nutrients 2021, 13, 2471 Study population: The PREDIMED-Plus trial (from the Spanish “PREvention4 of with 18 MEDiterranean DIet”) is an ongoing study launched in Spain in 2013 to assess the effect of an intensive lifestyle intervention—energy-restricted Mediterranean diet, increased physical activity and behavioral support—on the incidence of cardiovascular events and ofweight an intensive loss andlifestyle intervention—energy-restricted its long-term maintenance. Details aboutMediterraneanthe cohort havediet, beenincreased described physical previously activity and the [50] and behavioral support—on study protocol, the incidence including study design of cardiovascular eventscan and data collection, andbe weight loss and its long-term maintenance. Details about the cohort found at the PREDIMED-Plus website (https://www.predimedplus.com/en/ , accessed on: have been described previously [50] and 20th May 2020). the study Briefly, protocol, the trial involves including study design 6874 participants and female (48.5% data collection, and 54.5% can be male) found at the PREDIMED-Plus website (https://www.predimedplus.com/en/, between 55–75 years old (mean age and SD 65.0 ± 4.9) who presented with overweight or accessed on 20 May 2020). obesity (27 ≤ Briefly, BMI <≥the 40 trial kg/minvolves 2) and met 6874atparticipants (48.5% female least three criteria and 54.5% for metabolic male) syndrome between (MetS) as 55–75 years old previously (mean age described [51].andTheSDtrial ± 4.9) 65.0was who presented designed with as a 6-year, overweight parallel-group, orrandomized ≤ BMI ≤ obesity (27 clinical trial kg/m2 ) and 40 conducted inmet at least three 23 Spanish studycriteria centers.for metabolic The syndrome study protocol was (MetS) as previously described [51]. The trial was designed as a 6-year, approved according to the ethical standards of the Declaration of Helsinki by the Institu- parallel-group, randomized tional Review clinical Boardstrial conducted (IRBs) in 23 Spanish of all participating study centers and centers. The study all participants protocol provided writ- was approved according to the ethical standards of the Declaration ten consent of their participation in the study. This trial was retrospectively registered of Helsinki by theat Institutional Review the International Boards (IRBs) Standard of all participating Randomized Controlled centers and all with Trial Registry participants numberprovided 89898870 written consent of their participation in the study. This trial was retrospectively on 24th July 2014. In the present analysis, baseline data from the PREDIMED-Plus study registered atdata the set International dated 26thStandard June 2020 Randomized was used. These Controlled Trial Registry data correspond with at to data number 89898870of the beginning on 24th July 2014. In the present analysis, baseline data from the PREDIMED-Plus study the study, before the dietary intervention took place. Participants with missing values for data set dated 26th June 2020 was used. These data correspond to data at the beginning cardiometabolic variables were not included in the analysis. In addition, participants with of the study, before the dietary intervention took place. Participants with missing values implausible intakes (< 500 or > 3500 kcal for females and < 800 or > 4000 kcal for males) for cardiometabolic variables were not included in the analysis. In addition, participants were excluded. The final number of participants included in the analysis was 5636 (48.5% with implausible intakes (<500 or >3500 kcal for females and <800 or >4000 kcal for female and 51.5% male, mean age and SD: 65.1 ± 4.9) (Figure 1). This study adhered to the males) were excluded. The final number of participants included in the analysis was 5636 STROBE cross-sectional reporting guidelines [52]. (48.5% female and 51.5% male, mean age and SD: 65.1 ± 4.9) (Figure 1). This study adhered to the STROBE cross-sectional reporting guidelines [52]. Figure1.1.Flowchart Figure Flowchartofofthe thePREDIMED-Plus PREDIMED-Plusparticipants. participants.Number Numberofofsubjects subjectsshown shownininbold. bold. Anthropometric and blood measurements: The weight and waist circumference mea- surements were taken from participants in light clothing with no shoes or accessories, using an electronic calibrated scale and an anthropometric tape, respectively. Waist circumference was measured midway between the lowest rib and the iliac crest. Height measurements were taken using a wall-mounted stadiometer. Body Mass Index (BMI) was calculated as Nutrients 2021, 13, 2471 5 of 18 the weight in kilograms divided by the square of height in meters. Blood pressure was measured in triplicate with a sphygmomanometer in a sitting position and after 5 min rest, and the mean systolic and diastolic blood pressure was calculated. Blood samples were collected after overnight fasting by trained nurses at the recruiting centers or primary health care centers associated with the study. Plasma glucose, triglycerides, glycosylated hemoglobin (HbA1c), creatinine, total and HDL cholesterol levels were measured following standard enzymatic methods. LDL cholesterol concentration was calculated using the Friedewald formula [53]. Dietary, nutritional and lifestyle measurements: A semi-quantitative 143-item food frequency questionnaire (FFQ) was used to assess the diet of participants over the last year at baseline, before the dietary intervention. For each food or beverage item, participants were asked the average frequency of consumption of a commonly used portion size for that item (e.g., glass, cup, and slice) over the previous year. Nine options for frequency of consumption are given, ranging from “never or hardly ever” to “more than six times a day.” This FFQ has been repeatedly validated in the Spanish population [54–56] and considers variations in dietary patterns among seasons, weekdays and weekends. To estimate the daily consumption for each food or beverage item, the portion size was multiplied by the frequency of consumption and then expressed as grams per day. In the case of nutrient variables, data were estimated from FFQ responses considering all items that contribute to that nutrient intake, using food composition tables explicitly developed for Spanish dietary habits [57,58]. Adherence to an energy-restricted Mediterranean Diet (MedDiet) was assessed using a 17-item questionnaire specially developed and validated for the PREDIMED-Plus trial [59]. Participants were asked about the use or frequency of consump- tion of traditional Mediterranean food items and scored one point when the answer was in agreement with specific criteria defining energy-restricted MedDiet. Therefore, the higher the score, the better adherence to this diet. The glycemic index (GI) for each participant’s diet was estimated from the calculated glycemic load (GL) for that participant, as the GL multiplied by 100 and divided by the grams of carbohydrates consumed per day. Physical activity and smoking data were collected through the general PREDIMED-Plus question- naire. Participants were asked about the frequency and intensity of physical activities, and three levels of PA were defined as follows: low (frequent sitting and little walking and/or frequent sitting and moderate sustained efforts), medium (frequent walking with no vigor- ous efforts), and high (frequent walking and vigorous efforts and/or frequent vigorous efforts). Smoking habits were divided into three groups: never, former or current smoker. UPF consumption and food processing-based classification systems: The four systems most described in the literature (NOVA, IARC, IFIC and UNC) were selected based on their relatively broad geographical applicability, the scope of use and availability of food lists. The NOVA system developed by the Public Health Faculty of the University of São Paulo (Brazil) classifies food according to the extent and purpose of their industrial process- ing [16,33,43]. The IARC system was established in Europe by the International Agency for Research on Cancer (IARC) using the methodology design for the European Prospective Investigation into Cancer and Nutrition (EPIC), and it classifies foods according to their level of physical processing [11,44]. The IFIC system was developed by the International Food Information Council Foundation (IFIC) in the United States (US), and the classifying criteria are based on the increasing complexity of food processing and chemical composi- tion [45,46]. The UNC system was also developed in the US by the University of North Carolina (UNC) and classifies food according to the extent of physicochemical changes produced by food processing [47]. A total of 136 food and beverage items from the FFQ were allocated to processing groups according to food lists and descriptions found in the references cited above for each classification system (Figure 2 and Table S1). The UPF group was defined for each classification system as follows: Group 4 for NOVA, Group 3 for IARC, Groups 4 and 5 for IFIC, and Groups 4.1 and 4.2 for UNC. For each participant, UPF consumption was estimated as the sum of grams per day consumed from foods in the UPF group, divided by the total grams of food consumed per day and multiplied by 100. items from the FFQ were allocated to processing groups according to food lists and de- scriptions found in the references cited above for each classification system (Figure 2 and Nutrients 2021, 13, 2471 Table S1). The UPF group was defined for each classification system as follows: Group 4 6 of 18 for NOVA, Group 3 for IARC, Groups 4 and 5 for IFIC, and Groups 4.1 and 4.2 for UNC. For each participant, UPF consumption was estimated as the sum of grams per day con- sumed from foods in the UPF group, divided by the total grams of food consumed per UPF consumption day and multipliedwas calculated by 100. according towas UPF consumption thecalculated items listed in the UPF according group to the of listed items each classification system in the UPF group (Table of each S1). classification system (Table S1). Figure 2. Figure 2. Processing Processing groups groups defined defined by by each each classification classification system. system. Darker Darker shades shades indicate indicate higher higher processing group. UPF groups as defined in this study shown in bold. IARC: International processing group. UPF groups as defined in this study shown in bold. IARC: International Agency Agency for Research for Research on on Cancer, Cancer, IFIC: IFIC: International International Food Food Information Information Council Council Foundation, Foundation, UNC: UNC: University University of North Carolina. of North Carolina. The following The followingassumptions assumptions werewere mademade for particular for particular food food items items to ensureto ensure con- consistency sistency and avoid bias when applying different classifying criteria. and avoid bias when applying different classifying criteria. These were based on the These were based on the foods and cooking habits of the traditional Mediterranean diet foods and cooking habits of the traditional Mediterranean diet in the Spanish population. in the Spanish popula- tion. Therefore, Therefore, milk wasmilkassumed was assumedto be to be processed processed usingusing ultra-heated ultra-heated temperature temperature (UHT), (UHT), and and milkshakes, milkshakes, soft cheese soft cheese wedges,wedges, custardcustard and ice-cream and ice-cream were assumed were assumed to be industri- to be industrially pro- ally produced. duced. Meat, fish, Meat, eggsfish, andeggs and legumes legumes were assumed were assumed to be rawtoorbecooked raw orincooked a basicinmanner; a basic manner; lettuce waslettuce was to assumed assumed to be consumed be consumed raw; cabbage, raw;carrot, cabbage,peas,carrot, peas,zucchini, pumpkin, pumpkin,garlic zuc- chini, and garlicwere onion and assumed onion were to assumed be cookedtoin beacooked in a basic basic manner. manner. When fish When was not fish was not explicitly explicitly described, described, the same assumptions the same assumptions as for meatas for made. were meat were made. was Asparagus Asparagus assumed was to as- be canned sumed and to beofcanned the white andtype. of theRaisins whitewere type.assumed Raisins to be without were assumedadded to be sugar; without almonds added were sugar;assumed almondstowere be toasted assumed without to be salt; toastedwalnuts without were assumed salt; walnutstowere be natural assumed with to no be additions; natural with pistachios were assumed no additions; pistachiostowerebe salted assumedand other nuts toand to be salted be salted and/or other nuts to beroasted. salted Bread and/orwas assumed roasted. to be Bread wasfreshly assumed bakedto and not packaged. be freshly baked and Muesli not included packaged.wholegrain Muesli in- cereals cluded wholegrain cereals with no added sugar; butter was considered aswas with no added sugar; butter was considered as salted, cocoa powder assumed salted, cocoa to contain powder wasadded sugar,tocoffee assumed contain was considered added roasted sugar, coffee wasin the form of roasted considered ground in beans (not the form soluble) of ground and tea (not beans was soluble) includedand in the same tea was category included inastheother sameinfusions category (e.g., chamomile, as other infusions green tea). (e.g., chamomile, green tea). Statistical Statistical analyses.Data analyses. Dataanalysis analysis waswasconducted conducted using R prograrmming using R programming language [60] language in RStudio [61] and with the following statistical packages: “DescTools” [60] in RStudio [61] and with the following statistical packages: “DescTools” [62], “psych” [62], “psych” [63], “tableone” [64], “emmeans” [63], “tableone” [64], “emmeans” [65] and “irr”“irr” [65] and [66].[66]. Participants Participants were classified were according classified according to quintiles of UPF consumption for each classification system (Q1—lowest to quintiles of UPF consumption for each classification system (Q1—lowest UPF con- UPF consumption, Q5—highest UPF consumption). sumption, Q5—highest Data shown UPF consumption). in tables Data shown are inpresented tables are as “mean (standard presented as “mean deviation, SD)” for continuous variables and as percentages for (standard deviation, SD)” for continuous variables and as percentages for categorical var- categorical variables. Statistically significant iables. Statistically differences significant (p < 0.05) differences (p