Food consumption patterns and cardiovascular risk among shift workers: A NOVA-based approach

Food consumption patterns and cardiovascular risk among shift workers: A NOVA-based approach | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Food consumption patterns and cardiovascular risk among shift workers: A NOVA-based approach Andressa Santana Serra Silva, Silvana Mara Luz Turbino Ribeiro, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4479969/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective: To evaluate the association between food consumption, by extent and purpose of processing, and cardiovascular disease (CVD) risk among rotating shift workers. Methods: The cross-sectional study included 213 male shift workers. Dietary intake was assessed using a 24-hour recall method conducted by trained interviewers. Food items were classified using two approaches: the first was based on the quantity consumed from each food group. Fruits and vegetables (FV) intake, is classified as recommended at 400g per day by WHO guidelines. Ultraprocessed foods (UPFs) were analyzed based on tertiles of daily caloric contribution. The second approach, the NOVA dietary diversity score (DDS-NOVA) assessed the variety of consumed items within each food group, assigning points for each unique item consumed, irrespective of quantity or frequency. The CVD risk was evaluated using the Framingham coronary heart disease risk score (FCRS), categorizing participants as low risk ( 5%). Descriptive, univariate, and multivariate logistic regression were used. Results: CVD-risk was classified as high in 43.7%. In the multivariate model, the recommended consumption of FV was associated with a lower chance of high CVD-risk (OR:0.47;95%CI:0.23-0.98), and there was no association between the amount of UPF consumption and CVD-risk. In terms of variety, fresh-food consumption was associated with a lower chance of high CVD-risk (OR:0.67;95%CI:0.23-0.98), and UPF consumption was associated with higher CVD-risk (OR:1.30;95%CI:1.12-1.87). Conclusion: Consumption of both variety and quantity of fresh-foods was associated with a lower chance of CVD-risk, while a variety of consumption of UPF items increased this chance. Circadian rhythm Ultraprocessed food Fruit Vegetables Cardiovascular Disease Figures Figure 1 Introduction Shift work is an increasingly common form of employment, adapted to the needs of a society that operates 24 hours a day. However, this flexibility comes at a cost: recent studies have shown a significant correlation between irregular working hours and several health challenges, including increased cardiovascular risk (CVR). This risk is exacerbated by factors such as the deregulation of circadian rhythms and the resulting chronodisruption, which affect both the physiological and behavioral well-being of workers (Knutsson, 2004 ; Moreno et al., 2003 ). In addition to traditional risk factors such as age and lifestyle habits, diet has emerged as a crucial element in modulating CVR. The NOVA classification of foods, which divides them into categories based on the degree of processing, provides a valuable perspective on how diet can influence cardiovascular health. In particular, high consumption of ultraprocessed foods (UPFs), coupled with low consumption of fresh and minimally processed foods, has been associated with a myriad of health problems, including obesity, diabetes, and hypertension (Martínez Steele et al., 2016 ). Due to the routine of the working day, studies indicate that shift workers tend to adopt inadequate eating patterns, with higher consumption of ultraprocessed foods and lower intake of fresh foods (Souza et al., 2019 ). Evaluating the food consumption of populations is a complex task, and in shift workers, there are significant additional methodological challenges. Variability in work patterns and the availability of food during the night can influence both the calorie intake and the variety of food items consumed (Kelly et al., 2023 ; Souza et al., 2019 ). In addition, the lack of adequate facilities for eating at night can make it difficult to adopt healthy eating patterns (Reeves et al., 2004 ). These difficulties justify a two-pronged approach, assessing food consumption based on the amount of food consumed from each food group, as well as the variety of food items, according to the NOVA classification. Therefore, this study aims to assess the association between food consumption, according to the extent and purpose of processing, and cardiovascular risk in shift workers. Specifically, we intend to investigate whether there is a relationship between the consumption of UPFs and an increase in CVR, as well as whether the consumption of fresh foods can mitigate this risk. Methodology Design and participants The current investigation is a component of an extensive initiative known as the "Fatigue Management" project. Carried out by the Federal University of Ouro Preto, its primary aim is to ascertain the prevalence of cardiovascular risk factors and fatigue among employees at an iron ore extraction firm situated in the Iron Quadrangle region of central Minas Gerais, Brazil. For this research, male participants within the age range of 26 to 60 years, engaged in rotational shifts, were extended invitations to join. The rotational shifts followed a schedule involving morning, afternoon, and night shifts, with a work-to-rest ratio of 6 hours on duty and 12 hours off. The overall weekly working hours amounted to 36, and after every four consecutive shifts, a day off was allotted. The four shift cycles spanned from 7 a.m. to 1 p.m., 1 p.m. to 7 p.m., 7 p.m. to 1 a.m., and 1 a.m. to 7 a.m. The determination of the sample size took into account a previously recorded prevalence of 15.9% for cardiovascular risk in shift workers (Farha & Alefishat, 2018 ), a precision level of 5%, a design effect of 1.0, and a confidence level of 95%, resulting in a stipulated minimum sample size of 188 individuals. All employees with rotating shifts were approached to partake in the study, and initially, an assessment was made of 366 workers. However, factors such as refusal to participate, vacation periods, absences, dismissals, or incomplete food diary entries led to a final sample size of 213 individuals. Data collect Trained teams were responsible for conducting the anthropometric measurements and dietary survey, which took place in the company's outpatient clinics. The collected demographic data included gender, age, self-reported skin color, and education level. Age was categorized as 30 years, 30–40 years, 40–50 years, and 50–60 years; skin color was classified as black, brown, yellow, indigenous, or white; education level was categorized as completed high school, technical education, or university. Clinical evaluation included data on smoking, alcohol consumption, and physical activity. Smoking was divided into non-smokers (individuals who never smoked or quit more than six months ago) and smokers (currently smoking or quitting less than six months ago). Alcohol consumption was classified as yes or no. The International Physical Activity Questionnaire (IPAQ) version 8 - long form was used to assess physical activity levels, with high physical activity defined as > 600 MET-min/week. Regarding anthropometric data, weight was measured using the TANITA body composition monitor model BC-558, with a maximum capacity of 150 kg and precision of 0.1 kg (Tanita Corporation of America, Inc., Arlington Heights, Illinois, USA). Height was measured using the portable AlturExata stadiometer with a centimeter scale and precision of one millimeter (AlturExata, Belo Horizonte, Minas Gerais, Brazil). Procedures were carried out with individuals barefoot, properly positioned, standing straight, with a straight and fixed gaze ahead. From the collected weight and height information, the Body Mass Index (BMI) was calculated using the formula: weight (kg) / height (m)², considering overweight when BMI values were ≥ 25.0 kg/m² (WHO, 2000). Blood pressure was measured using a semi-automatic digital device from Microlife, model BP3AC1-1PC (Microlife, Widnau, Switzerland), following the parameters of the Brazilian Society of Cardiology (Précoma et al., 2019 ). Workers were divided into two groups based on the presence or absence of systemic arterial hypertension (SAH). Blood pressure values were determined by the average of three measurements. SAH was considered for individuals with an average systolic blood pressure equal to or greater than 140 mmHg or diastolic blood pressure equal to or greater than 90 mmHg. Triglyceride, total cholesterol, and high-density lipoprotein cholesterol (HDL-c) levels were determined by enzymatic colorimetry using the Triglycerides Liquicolor Mono®, Cholesterol Liquicolor®, and Cholesterol HDL Direct-Homogeneous Direct Test® kits (Human do Brasil, Itabira, Brazil), respectively, on an automated Chemwell R6® analyzer (Awareness Technology, Palm City, FL). Low-density lipoprotein cholesterol (LDL-c) was obtained by mathematical calculation using the Friedewald formula (1972), with LDL-c (mg/dL) = Total cholesterol - HDL - (Triglycerides/5) when triglyceride concentration was less than or equal to 400 mg/dL. Participants with plasma triglyceride concentrations exceeding 400 mg/dL were evaluated for their LDL levels using a specific LDL Direct-Test kit. Explanatory: Food consumption according to NOVA classification The instrument used to collect data related to dietary intake was the 24-hour dietary recall (R24h), administered by trained interviewers. This method involved gathering information on the timing and location of each meal, the type of food consumed, the preparation method, the quantity in portions, and, if possible, the brand of products. To assist individuals in accurately identifying consumed portions, the book "Food Consumption: Visualizing Portions" by Monteiro (2007) was used as a reference. Food consumption was classified according to the NOVA classification (Monteiro et al., 2019 ). The first group consisted of Fresh foods, which are those that have not undergone any type of alteration, being obtained directly from plants or animals, such as vegetables, fresh fruit, grains, roots, tubers, and others. The second group was made up of minimally processed foods, which are fresh foods that have undergone minimal alterations, such as dried, polished, and packaged grains, flours, washed roots and tubers, chilled or frozen meats, and pasteurized milk. The third group was culinary ingredients, which are those used to season and cook food and preparations (salt, sugar, oils, and fats). The fourth group is processed foods, which are the result of adding salt, sugar, and/or fat to fresh or minimally processed foods, such as canned goods, preserves, processed meat and fish, cheeses, and others. The fifth group is ultraprocessed products, which are industrial products that include additives, colorings, and flavor enhancers that make food more attractive to consumers, such as snacks, sweets, cookies, fatty snacks, hamburgers, ice cream (Supplementary Table 1w). In this study, we adopted a dual approach to classifying workers' food consumption, focusing on both the quantity and diversity of food consumed, according to the extent and purpose of processing. Quantitative classification of food consumption This classification was based on the daily intake of food from each food group, following the NOVA classification. Therefore, for fresh foods, the intake of fruit and vegetables (FV) was considered, and classified according to the guidelines of the World Health Organization, which recommends the consumption of 400g per day of these foods (WHO, 2020 ), as they act in the protection and prevention of chronic non-communicable diseases, cardiovascular diseases, and neoplasms (X. Wang et al., 2014 ). This recommendation serves as a reference for assessing the adequacy of FVL consumption among the study participants. As for ultraprocessed foods, due to the lack of a specific minimum recommendation for UPF consumption, we opted for an analysis based on tertiles of daily caloric intake. The proportion of daily caloric value coming from UPFs was calculated using the formula [(total kcal from UPFs)×100], and the participants were classified into three groups: tercile 1 (T1), with the lowest percentage of caloric intake coming from UPFs; tercile 2 (T2), with intermediate values; and tercile 3 (T3), with the highest percentage of UPF consumption. This classification allows for a differentiated analysis of the possible impact of UPF consumption on the participants. NOVA dietary diversity score (DDS-NOVA) The DDS-NOVA is an indicator developed to assess the diversity of workers' diets, focusing on the variety of food items consumed. This score is based on the premise that greater dietary diversity is associated with a more balanced diet and, potentially, a better state of health. Diversity is measured by the presence of different types of food within each food group, as defined by the NOVA classification. To construct the DDS-NOVA, we used data from 24-hour recall records. Each type of food consumed within a specific food group gives a point to the score. For example, if a worker consumes two different types of fruit, one type of vegetable, and two types of vegetable throughout the day, they will receive a total of five points for the fresh food group. This methodology is applied in the same way to the other food groups: minimally processed, culinary ingredients, processed, and ultraprocessed. The DDS-NOVA does not take into account the frequency or quantity of consumption, but rather the variety of food items. This allows for a qualitative analysis of the diet, complementing other quantitative measures of food consumption. The choice of this method is justified by the literature, which suggests an association between dietary diversity and a reduced risk of chronic diseases. In addition, the DDS-NOVA is inspired by a study carried out in the city of São Paulo, which validated the use of a similar score (Nova24h screener) to assess the variety of unprocessed or minimally processed whole plant foods (WPF, 33 food items) and ultraprocessed foods (UPF, 23 food items) consumed by a sample of 812 adults. Two scores are obtained from this tool by summing the number of items checked, the Nova-WPF and the Nova-UPF. The scores obtained reflected the number of subgroups of whole plant foods and ultraprocessed foods reported, providing insights into food consumption patterns in the population studied (Costa et al., 2023 ; dos Santos Costa et al., 2021 ). Outcome: Cardiovascular Disease Risk To estimate cardiovascular risk, we employed the Framingham Global Risk Score (FRS), which calculates the 10-year risk of coronary events, cerebrovascular events, peripheral arterial disease, or heart failure. Recommended by the Atherosclerosis Department of the Brazilian Society of Cardiology (SBC-DA), the FRS is a scoring system where each characteristic (gender, age, total cholesterol, HDL-c, systolic blood pressure, diabetes, and smoking) is assigned a corresponding score. The sum of points for all variables yields a percentage indicating the 10-year risk of cardiovascular disease. The risk categories were further divided into two groups: low cardiovascular risk (< 5%) and intermediate to high cardiovascular risk (≥ 5%) (Précoma et al., 2019 ). Statistical analysis The statistical analysis of the dataset was conducted using Stata/MP software (version 15.0). The description of variables included presenting absolute frequency values (n) and relative percentages (%), with cardiovascular risk analyzed through the Pearson chi-square test. To assess the association of food consumption variables with cardiovascular risk, both univariate and multivariate logistic regression analyses were conducted to determine the odds ratio (OR) values and their corresponding 95% confidence intervals (CI). The explanatory variables were classified as follows: Quantitative consumption of fruit and vegetables was classified dichotomously as low and high consumption, based on the recommendations of the World Health Organization. Quantitative consumption of UPFs is classified into three categories according to the tertiles of the daily calorie percentage from UPFs. The DDS-NOVA score was assessed ordinally, keeping the original score as a continuous ordinal variable in the models. The multivariate model was designed to incorporate covariates considered as confounding factors in the analysis, following the literature (Martinez-Gonzalez & Bes-Rastrollo, 2014 ; Qu et al., 2024 ; Santiago et al., 2017 ). This encompassed sociodemographic, behavioral, and dietary factors that impact the relationships between dietary intake and cardiovascular risk. Thus, the multivariate model was adjusted for the following variables: age, skin color, schooling, time working shifts, physical activity, body mass index, and total caloric intake. Collinearity among the covariates was assessed by calculating the variance inflation factor (VIF), with the "subsetByVIF" package in Stata, considering a maximum cutoff point of 10 (VIF < 10). Results The study encompassed 213 participants aged 26 to 60, with the majority (62.4%) falling between 30 and 40 years. Demographically, 49.3% identified as brown, 56.8% had completed high school, and 79.3% were married. Professionally, 51.6% had over a decade of experience in alternate shifts. Lifestyle factors included 72.3% engaging in regular physical activity and 62.9% consuming alcohol, while smokers constituted 21.1%. Nearly half (49.8%) were classified as overweight based on their BMI. Cardiovascular risk assessments using the Framingham Global Risk Score indicated that 56.2% of workers were at low risk (< 5%), and 43.7% were at intermediate to high risk (≥ 5%) (Table 1 ). Table 1 Sociodemographic. clinical. and behavioral characteristics of shift workers according to cardiovascular disease risk Cardiovascular Disease Risk Characteristics Total (n = 213) Low risk (n = 117) Intermediate to high risk (n = 91) p V Age, years < 30 17 (7.8%) 15 (12.6%) 2 (2.1%) < 0.001 0.596 30–40 133 (62.4% 97 (81.5% 36 (38.3%) 41–50 43 (20.2%) 7 (5.9%) 36 (38.3%) 51–60 20 (9.4%) 0 (0.0%) 20 (21.3%) Skin color White 80 (37.6%) 42 (35.3%) 38 (40.4%) 0.555 0.074 Brown 105 (49.3%) 59 (49.6%) 46 (48.9%) Black 28 (13.1%) 18 (15.1%) 10 (10.6%) Education High school 121 (56.8%) 67 (56.3%) 54 (57.4%) 0.867 0.011 Technical or higher education 92 (43.2%) 52 (43.7%) 40 (42.5%) Marital status Married 169 (79.3%) 90 (75.6% ) 79 (84.0%) Single 44 (20. 7%) 29 (24.4%) 15 (16.0%) 0.132 0.103 Working time, years < 5 24 (11.3%) 19 (16.0%) 5 (5.3%) 0.001 0.251 5 to 9.9 years 79 (37.1%) 51 (42.9%) 28 (29.8%) 10 to 15 years 110 (51.6%) 49 (41.2%) 61 (64.9%) Physical Activity No 59 (27.7%) 31 (26.0%) 28 (29.8%) 0.545 0.041 Yes 154 (72.3%) 88 (74.0%) 66 (70.2%) Alcohol consumption No 79 (37.1%) 48 (40.3%) 31 (33.0%) 0.270 0.075 Yes 134 (62.9%) 71 (59.7%) 63 (67.0%) Smoking No 151 (70.9%) 103 (86.6%) 48 (51.1%) < 0.001 0.387 Yes 62 (21.1%) 16 (13.5%) 46 (48.9%) Body mass index Eutrophic 55 (25.8%) 38 (31.9%) 17 (18.1%) 0.065 0.160 Overweight 106 (49.8%) 53 (44.5%) 53 (56.4%) Obesity 52 (24.4%) 28 (23.5%) 24 (25.5%) Legend: p: p-value from Pearson's chi-square test; V: value from Cramer's V test Dietary analysis using the DDS-NOVA score showed an average consumption of 3.46 items from fresh foods, with a range of up to nine items. Minimally processed foods averaged 3.74 items, processed foods at 2.54 items, and ultraprocessed foods at 3.39 items, with respective ranges up to six, eight, and nine items. Culinary ingredients had the lowest average at 1.27 items, with a maximum of five items consumed. The caloric intake from ultraprocessed foods was categorized into tertiles, with T1 ranging from 0–14.2%, T2 from 14.3–27.7%, and T3 from 27.8–66.9% (Fig. 1 ). The average intake of fruits and vegetables was 272.32g (± 350.43), with 70.4% meeting the recommended intake of at least 400g per day (Table 2 ). Table 2 Association of food consumption according to extent and purpose of processing and cardiovascular risk in rotating shift workers. Frequency Univariate Multivariate Variables n (%) OR (95%CI) p-valor OR (95%CI) p-valor Quantitative food consumption Ultraprocessed food (% kcal/day) Tercil 1 (0.0-14.2% of UPF) 71 (33.3%) 1.00 - 1.00 - Tercil 2 (14.3–27.7% of UPF) 72 (33.8%) 0.73 (0.31–1.16) 0.320 0.93 (0.37–2.31) 0.874 Tercile 3 (27.8–66.9% of UPF) 70 (32.9%) 0.83 (0.33–1.23) 0.565 0.60 (0.26–1.37) 0.224 Fruit and vegetables (g/day) < 400g 63 (29.6) 1.00 1.00 ≥ 400g 150 (70.4) 0.67 (0.38–1.17) 0.160 0.47 (0.23–0.98) 0.046 Dietary diversity score NOVA Fresh food items 3.46 (± 1.55) 0.78 (0.65–0.94) 0.009 0.67 (1.01–1.66) 0.003 Minimally processed food items 3.78 (± 1.09) 1.09 (0.85–1.40) 0.502 1.15 (0.80–1.64) 0.432 Culinary food ingredients 1.31 (± 0.93) 1.02 (0.76–1.36) 0.884 0.93 (0.63–1.40) 0.743 Processed food items 2.45 (± 1.22) 0.84 (0.67–1.06) 0.150 0.81 (0.60–1.10) 0.176 Ultraprocessed food items 3.37 (± 1.73) 0.96 (0.82–1.13) 0.641 1.30 (1.22–1.87) 0.039 Legend: OR: Odds ratio; CI: Confidence Interval; UPF: Ultraprocessed foods Multivariate logistic regression analysis to estimate the odds ratio of high cardiovascular risk in workers according to variables related to food consumption. The model is adjusted for the following variables: age, skin color, schooling, time working shifts, physical activity, body mass index, and total caloric intake. The adjusted multivariate model revealed that consuming ≥ 400g/day of fruits, vegetables, and legumes correlated with a 2.12-fold decrease in the likelihood of cardiovascular risk ≥ 5% (OR: 0.47; 95%CI: 0.23–0.98). No significant association was found between the caloric contribution of ultraprocessed foods and cardiovascular risk. However, each additional item of fresh food consumed was linked to a 49% reduction in cardiovascular risk > 5% (OR: 0.67; 95%CI: 1.01–1.66), while each additional ultraprocessed food item increased this risk by 30% (OR: 1.30; 95% CI: 0.52–0.87). The variety of minimally processed, processed foods, and culinary ingredients showed no significant association with cardiovascular risk (Table 2 ). Discussion This study aimed to assess the association of food consumption, according to the degree and purpose of processing, with the risk of cardiovascular disease (CVD) in shift workers. The results indicate a significant association between the consumption of fresh foods and a reduced risk of CVD, while the consumption of ultraprocessed foods showed an increased risk. Shift work's disruption of the circadian cycle may impact lifestyle choices, contributing to cardiovascular risk. CVDs represent one of the leading causes of morbidity and mortality worldwide, with estimates indicating that approximately 17.9 million people died from CVDs in 2019, which represents 32% of all global deaths (WHO, 2020 ). In Brazil, CVDs are also the leading cause of death, responsible for a significant proportion of deaths and hospital admissions, impacting the Unified Health System and contributing to early work inactivity due to disability (de Oliveira et al., 2022 ). Among the risk factors for CVDs, working hours, especially shift work, have been associated with an increased risk of these diseases. Studies suggest that shift workers have a 40% higher risk of developing CVDs compared to those who work regular hours (D. Wang et al., 2018 ). The hypotheses for this relationship include the disruption of circadian rhythms, changes in physical and mental well-being, and negative impacts on family life and lifestyle habits, such as physical activity and diet (Ho et al., 2022 ; Wong et al., 2023 ). The nutrition of individuals is another crucial factor in modulating cardiovascular risk. Consumption of ultraprocessed foods has been associated with an increased risk of CVD, with studies indicating that a diet rich in these foods can lead to a higher risk of all-cause mortality and cardiovascular disease (Qu et al., 2024 ; Srour et al., 2019 ). On the other hand, the consumption of fresh foods, such as fruit and vegetables, has been consistently linked to a lower risk of CVDs (Cordova et al., 2023 ; Dai et al., 2024 ; Rauber & Levy, 2024 ). In this respect, it is also important to consider both the quantity and variety of food consumed. In our study, when considering the consumption of ultraprocessed foods, we did not find a direct association with cardiovascular risk when analyzing the caloric contribution of these foods. One possible explanation for this finding is that the most frequently consumed ultraprocessed food was bread (81.0%) (Table 3 ), which corresponds to light bread, white pita bread, sweet bread, whole meal bread, and cheese bread. However, when comparing the ultraprocessed foods consumed, bread is less processed and has a lower calorie content. It is also important to note that most of the ultraprocessed foods consumed by the workers were offered by the company, meaning that there was a great deal of similarity in the ultraprocessed foods consumed, leading to a homogeneity of the findings. On the other hand, the diversity in the consumption of ultraprocessed foods was relevant, suggesting that a greater variety in this category of food is associated with an increase in cardiovascular risk. Table 3 Characterization of the ultraprocessed foods consumed by rotating shift workers Foods items Percentage of workers that consume (%) Caloric contribution (kcal/day) Percentage (%) of total energy intake (kcal/day) Ultraprocessed breads 1 81.0 243.4 10.9 Ultraprocessed meat 2 46.8 89.0 2.1 Margarine 46.8 131.5 1.7 Cookies 45.9 251.3 5.2 Sweetened beverages 3 45.7 157.6 3.7 Cake and bakery UPF 21.6 299.5 2.7 Dairy drinks 13.4 203.4 1.5 Ultraprocessed cheese 7.6 116.2 0.4 Ready sauces 4 6.7 172.9 0.6 Vegetable-based UPF 5 3.1 51.6 0.1 The ultraprocessed foods considered here refer to foods that undergo a high degree of industrial processing, including adding artificial or extracted ingredients, such as emulsifiers, colorings, flavorings, and hydrogenated fat. For descriptive purposes, we present only the most frequent UPFs, not all of those consumed 1 Including light bread, white/pita bread, whole grain/rye bread, Brazilian cheese bread 2 Including soft drinks, processed juice, and artificial juice 3 Including sausage/chorizo/Vienna sausage, hamburger (beef), ham/mortadella/salami. 4 Including mayonnaise, ketchup, and mustard. 5 Including instant mashed potatoes, soup powder, tomato sauce with artificial additives, and ready-to-eat meats (burgers, meatballs, and others) of vegetable origin In contrast, consumption of whole foods, fruit, vegetables, and legumes, both in quantity and variety, was associated with a lower cardiovascular risk. The World Health Organization recommends a daily intake of 400g of these foods, equivalent to five portions, to prevent chronic diseases (Srour et al., 2019 ). Studies corroborate those diets rich in fresh foods are associated with a reduced risk of CVDs and other morbidities (Lichtenstein et al., 2021 ). These findings can be explained by the nutritional components present in these foods. In the case of ultraprocessed foods, the higher cardiovascular risk can be attributed to the high energy density, and high sodium and lipid content present in ultraprocessed foods, which are linked to the development of obesity and metabolic disorders such as dyslipidemia, diabetes, and hypertension, known risk factors for CVDs (Qu et al., 2024 ). In addition, ultraprocessed foods have been associated with pathogenic mechanisms such as oxidative stress and subclinical inflammation, which can accelerate the process of atherosclerosis (Qu et al., 2023 ). On the other hand, higher consumption of unprocessed foods can protect against CVD, due to higher levels of soluble fiber. Soluble fiber reduces serum LDL concentrations improves glucose tolerance, and controls type 2 diabetes. Insoluble fibers improve insulin sensitivity and decrease the expression of inflammatory markers, which increase oxidative stress in the body (Howarth et al., 2009 ). In addition, fibers also act in weight control, due to their low energy content and the satiety that is provided for longer (Clark & Slavin, 2013 ). Therefore, the results of this study reinforce the importance of diet quality and suggest that promoting a diet rich in fresh foods and limiting the variety of ultraprocessed foods can be effective strategies for reducing the risk of CVDs. These findings are in line with current public health guidelines that emphasize the need for a balanced and diverse diet for the prevention of cardiovascular diseases and other chronic conditions. A strength of this study is its specific population of shift workers, who are often under-represented in nutritional research. In addition, the food consumption assessment method employed in this study, included both quantitative analysis and a variety of consumption scores, allowing for a comprehensive assessment of the participants' diet. The use of the DDS-NOVA score was particularly useful for differentiating between the types of food consumed and associating them with CVD risk. This proposal is in line with recent studies that propose a similar analysis, focusing on the variety of food items consumed, such as Nova-WPF, which includes unprocessed or minimally processed whole plant foods (33 food items) and Nova-UPF, which includes ultraprocessed foods (23 food items) (Costa et al., 2023 ; dos Santos Costa et al., 2021 ). However, some limitations should be mentioned, which include the cross-sectional design, which prevents the inference of causality, and the possibility of recall bias in the collection of dietary data. Furthermore, a population restricted to males, which makes it difficult to generalize to a heterogeneous population, environmental factors not considered but capable of influencing the quantity and quality of consumption of shift workers, such as unsuitable meal locations and the availability of healthy foods in their context. However, the results presented are considered important to contribute to the topic and future studies, which should explore the mechanisms underlying the observed associations and evaluate specific dietary interventions to reduce the risk of CVD in shift workers. Longitudinal studies are needed to confirm the direction of the associations found. Conclusion In conclusion, the findings of this study add valuable insights to the existing literature, emphasizing the significance of consuming fresh and minimally processed foods while limiting ultraprocessed foods to mitigate cardiovascular risk. The distinct association between dietary patterns and cardiovascular health underscores the necessity for comprehensive public health policies and workplace health initiatives that promote nutritious dietary habits, particularly among shift workers who may be more vulnerable to cardiovascular diseases. Further research into the long-term health impacts on this demographic and the implementation of dietary interventions tailored to the degree and purpose of food processing could play a pivotal role in enhancing the overall health and well-being of these workers. This study’s outcomes advocate for a paradigm shift in dietary recommendations, focusing not just on the nutritional content but also on the quality and processing level of the foods consumed. Declarations Ethical Approval This study received approval from the Research Ethics Committee of the Federal University of Ouro Preto (CAAE: 39,682,014.7.0000.5150) and adhered to the principles outlined in the Helsinki Declaration. Before their participation, all workers were provided with detailed information about the research objectives, procedures involved, and the potential risks and benefits. Those who voluntarily agreed to participate in the study expressed their consent by signing an informed consent form. Consent for Publication Informed consent was obtained from all individual participants included in the study Funding This study was supported by the Brazilian Council for Scientific and Technological Development (CNPq, Distrito Federal, Brazil) and Coordination for the Improvement of Higher Education Personnel-Brazil (CAPES); finance code 001 for Ph.D. student scholarship. Availability of data and materials The datasets generated and/or analyzed as part of the current study are not publicly available due to confidentiality agreements with subjects. However, they can be made available solely for re- view and not for publication from the corresponding author upon reasonable request Authors' contribution LAAMJ, SNF, FAPP, GLLMC, FLPO, and RMNN contributed to the conception and design of the work, to the acquisition, analysis, and interpretation of data, and the draft of the manuscript. ASSS, LAAMJ, and SMLTR contributed to the analysis, interpretation of data, and draft of the manuscript. All authors revised it critically for important intellectual content and approved the submitted version. References Clark MJ, Slavin JL. The effect of fiber on satiety and food intake: A systematic review. J Am Coll Nutr. 2013;32(3):200–11. https://doi.org/10.1080/07315724.2013.791194 . Cordova R, Viallon V, Fontvieille E, Peruchet-Noray L, Jansana A, Wagner K-H, Kyrø C, Tjønneland A, Katzke V, Bajracharya R, Schulze MB, Masala G, Sieri S, Panico S, Ricceri F, Tumino R, Boer JMA, Verschuren WMM, van der Schouw YT, Freisling H. 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Clin Nutr. 2024. https://doi.org/10.1016/j.clnu.2024.04.016 . de Oliveira GMM, Brant LCC, Polanczyk CA, Malta DC, Biolo A, Nascimento BR, de Fatima M, De Lorenzo AR, de Paiva Fagundes Júnior AA, Schaan BD, de Castilho FM, Cesena FHY, Soares GP, Junior GFX, Barreto-Filho JAS, Passaglia LG, Pinto-Filho MM, Machline-Carrion MJ, Bittencourt MS, Ribeiro ALP. (2022). Cardiovascular Statistics - Brazil 2021. Arquivos Brasileiros de Cardiologia, 118(1), 115. https://doi.org/10.36660/ABC.20211012 . dos Costa S, de Faria C, Gabe FR, Sattamini KT, Khandpur IF, Leite N, Steele FHM, Costa Louzada EM, Levy ML, R. B., Monteiro CA. (2021). Nova score for the consumption of ultraprocessed foods: description and performance evaluation in Brazil. Revista de Saude Publica , 55 , 1–9. https://doi.org/10.11606/S1518-8787.2021055003588 . Farha RA, Alefishat E. Shift Work and the Risk of Cardiovascular Diseases and Metabolic Syndrome Among Jordanian Employees. Oman Med J. 2018;33(3):235. https://doi.org/10.5001/OMJ.2018.43 . Ho FK, Celis-Morales C, Gray SR, Demou E, Mackay D, Welsh P, Katikireddi SV, Sattar N, Pell JP. Association and pathways between shift work and cardiovascular disease: a prospective cohort study of 238 661 participants from UK Biobank. Int J Epidemiol. 2022;51(2):579–90. https://doi.org/10.1093/IJE/DYAB144 . Howarth NC, Saltzman E, Roberts SB. Dietary Fiber and Weight Regulation. Nutr Rev. 2009;59(5):129–39. https://doi.org/10.1111/j.1753-4887.2001.tb07001.x . Kelly C, Nea FM, Pourshahidi LK, Kearney JM, O’Brien V, Livingstone MBE, Corish CA. Adherence to dietary and physical activity guidelines among shift workers: associations with individual and work-related factors. BMJ Nutr Prev Health. 2023;3(2). https://doi.org/10.1136/BMJNPH-2020-000091 . bmjnph-2020-000091. Knutsson A. Methodological aspects of shift-work research. Chronobiol Int. 2004;21(6):1037–47. https://doi.org/10.1081/CBI-200038525 . Lichtenstein AH, Appel LJ, Vadiveloo M, Hu FB, Kris-Etherton PM, Rebholz CM, Sacks FM, Thorndike AN, Van Horn L, Wylie-Rosett J. 2021 Dietary Guidance to Improve Cardiovascular Health: A Scientific Statement From the American Heart Association. Circulation. 2021;144(23):E472–87. https://doi.org/10.1161/CIR.0000000000001031 . Martínez Steele E, Baraldi LG, Louzada ML da, Moubarac C, Mozaffarian J-C, Monteiro D, LG CAEMS. B., ML, L., JC, M., D, M., & CA, M. (2016). Ultraprocessed foods and added sugars in the US diet: evidence from a nationally representative cross-sectional study. BMJ Open , 6 (3), e009892. https://doi.org/10.1136/bmjopen-2015-009892 . Martinez-Gonzalez MA, Bes-Rastrollo M. Dietary patterns, Mediterranean diet, and cardiovascular disease. Curr Opin Lipidol. 2014;25(1):20–6. https://doi.org/10.1097/MOL.0000000000000044 . Monteiro CA, Cannon G, Lawrence M, Costa Louzada ML, Machado PP. (2019). The NOVA food classification system and its four food groups. In Ultraprocessed foods, diet quality, and health using the NOVA classification system . http://www.wipo.int/amc/en/mediation/rules . Moreno CR, de Fischer C, F. M., Rotenberg L. A saúde do trabalhador na sociedade 24 horas. São Paulo Em Perspectiva. 2003;17(1):34–46. http://www.scielo.br/scielo.php?script=sci_arttext . &pid=S0102-88392003000100005&lng=pt&tlng=pt. Précoma DB, de Oliveira GMM, Simão AF, Dutra OP, Coelho OR, Izar MC, de Póvoa O, Giuliano RMDS, Filho IdeCB, de Machado AC, Scherr CA, Fonseca C, Filho FAH, de Carvalho RDDS, Avezum T, Esporcatte Á, Nascimento R, Brasil BR, de Soares D, Mourilhe-Rocha GP, R. Updated cardiovascular prevention guideline of the Brazilian society of cardiology – 2019. Arquivos brasileiros de cardiologia. 2019;113(4):787–891. https://doi.org/10.5935/abc.20190204 . Qu Y, Hu W, Huang J, Tan B, Ma F, Xing C, Yuan L. (2024). Ultraprocessed food consumption and risk of cardiovascular events: a systematic review and dose-response meta-analysis . https://doi.org/10.1016/j.eclinm.2024.102484 . Qu Y, Hu W, Xing C, Yuan L, Huang J. Ultraprocessed food consumption and cardiovascular events risk. Eur Heart J. 2023;44(Supplement2). https://doi.org/10.1093/EURHEARTJ/EHAD655.2389 . Rauber F, Levy RB. (2024). Ultraprocessed foods and cardiovascular disease . 21 . https://doi.org/10.1038/s41569-024-00990-7 . Reeves SL, Newling-Ward E, Gissane C. The effect of shift-work on food intake and eating habits. Nutr Food Sci. 2004;34(5):216–21. https://doi.org/10.1108/00346650410560398/FULL/PDF . Santiago S, Zazpe I, Gea A, de la Rosa PA, Ruiz-Canela M, Martinez-Gonzalez MA. Healthy-eating attitudes and the incidence of cardiovascular disease: the SUN cohort. Int J Food Sci Nutr. 2017;68(5):595–604. https://doi.org/10.1080/09637486.2016.1265100 . Souza RV, Sarmento RA, de Almeida JC, Canuto R. The effect of shift work on eating habits: a systematic review. Scand J Work Environ Health. 2019;45(1):7–21. https://doi.org/10.5271/sjweh.3759 . Srour B, Fezeu LK, Kesse-Guyot E, Allès B, Méjean C, Andrianasolo RM, Chazelas E, Deschasaux M, Hercberg S, Galan P, Monteiro CA, Julia C, Touvier M. (2019). Ultraprocessed food intake and risk of cardiovascular disease: prospective cohort study (NutriNet-Santé). BMJ , 365 . https://doi.org/10.1136/BMJ.L1451 . Wang D, Ruan W, Chen Z, Peng Y, Li W. Shift work and risk of cardiovascular disease morbidity and mortality: A dose–response meta-analysis of cohort studies. Eur J Prev Cardiol. 2018;25(12):1293–302. https://doi.org/10.1177/2047487318783892 . Wang X, Ouyang Y, Liu J, Zhu M, Zhao G, Bao W, Hu FB. Fruit and vegetable consumption and mortality from all causes, cardiovascular disease, and cancer: Systematic review and dose-response meta-analysis of prospective cohort studies. BMJ (Online). 2014;349. https://doi.org/10.1136/bmj.g4490 . WHO. (2020). Cardiovascular diseases (CVDs) . https://www.who.int/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds ). WHO, W. H. O. Obesity: preventing and managing the global epidemic : report of a WHO consultation. World Health Organization; 2000. Wong R, Crane A, Sheth J, Mayrovitz HN, Wong R, Crane A, Sheth J, Mayrovitz HN. Shift Work as a Cardiovascular Disease Risk Factor: A Narrative Review. Cureus. 2023;15(6). https://doi.org/10.7759/CUREUS.41186 . Additional Declarations No competing interests reported. Supplementary Files Supplementarytable1.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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12:12:47","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4479969/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4479969/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":58230041,"identity":"f3ff8d96-2fa2-43fb-a8c6-a08413cfa310","added_by":"auto","created_at":"2024-06-12 19:17:42","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":44896,"visible":true,"origin":"","legend":"\u003cp\u003eHistogram of the NOVA dietary diversity score (DDS-NOVA), according to processing purpose\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4479969/v1/105741d70e4ec6dabdac8b5a.png"},{"id":59857136,"identity":"03ecd389-379f-48f7-8bdc-e1d9012221b9","added_by":"auto","created_at":"2024-07-08 13:40:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":848711,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4479969/v1/8dce49fd-12cf-474e-bf08-b79305a7f4ca.pdf"},{"id":58231603,"identity":"23b481b4-925b-476c-849f-a9dc5df3acf6","added_by":"auto","created_at":"2024-06-12 19:25:42","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":14725,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-4479969/v1/e11cd9f567eb1be29933c217.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Food consumption patterns and cardiovascular risk among shift workers: A NOVA-based approach","fulltext":[{"header":"Introduction","content":"\u003cp\u003eShift work is an increasingly common form of employment, adapted to the needs of a society that operates 24 hours a day. However, this flexibility comes at a cost: recent studies have shown a significant correlation between irregular working hours and several health challenges, including increased cardiovascular risk (CVR). This risk is exacerbated by factors such as the deregulation of circadian rhythms and the resulting chronodisruption, which affect both the physiological and behavioral well-being of workers (Knutsson, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Moreno et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2003\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn addition to traditional risk factors such as age and lifestyle habits, diet has emerged as a crucial element in modulating CVR. The NOVA classification of foods, which divides them into categories based on the degree of processing, provides a valuable perspective on how diet can influence cardiovascular health. In particular, high consumption of ultraprocessed foods (UPFs), coupled with low consumption of fresh and minimally processed foods, has been associated with a myriad of health problems, including obesity, diabetes, and hypertension (Mart\u0026iacute;nez Steele et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Due to the routine of the working day, studies indicate that shift workers tend to adopt inadequate eating patterns, with higher consumption of ultraprocessed foods and lower intake of fresh foods (Souza et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEvaluating the food consumption of populations is a complex task, and in shift workers, there are significant additional methodological challenges. Variability in work patterns and the availability of food during the night can influence both the calorie intake and the variety of food items consumed (Kelly et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Souza et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In addition, the lack of adequate facilities for eating at night can make it difficult to adopt healthy eating patterns (Reeves et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). These difficulties justify a two-pronged approach, assessing food consumption based on the amount of food consumed from each food group, as well as the variety of food items, according to the NOVA classification.\u003c/p\u003e \u003cp\u003eTherefore, this study aims to assess the association between food consumption, according to the extent and purpose of processing, and cardiovascular risk in shift workers. Specifically, we intend to investigate whether there is a relationship between the consumption of UPFs and an increase in CVR, as well as whether the consumption of fresh foods can mitigate this risk.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eDesign and participants\u003c/h2\u003e \u003cp\u003eThe current investigation is a component of an extensive initiative known as the \"Fatigue Management\" project. Carried out by the Federal University of Ouro Preto, its primary aim is to ascertain the prevalence of cardiovascular risk factors and fatigue among employees at an iron ore extraction firm situated in the Iron Quadrangle region of central Minas Gerais, Brazil.\u003c/p\u003e \u003cp\u003eFor this research, male participants within the age range of 26 to 60 years, engaged in rotational shifts, were extended invitations to join. The rotational shifts followed a schedule involving morning, afternoon, and night shifts, with a work-to-rest ratio of 6 hours on duty and 12 hours off. The overall weekly working hours amounted to 36, and after every four consecutive shifts, a day off was allotted. The four shift cycles spanned from 7 a.m. to 1 p.m., 1 p.m. to 7 p.m., 7 p.m. to 1 a.m., and 1 a.m. to 7 a.m.\u003c/p\u003e \u003cp\u003eThe determination of the sample size took into account a previously recorded prevalence of 15.9% for cardiovascular risk in shift workers (Farha \u0026amp; Alefishat, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), a precision level of 5%, a design effect of 1.0, and a confidence level of 95%, resulting in a stipulated minimum sample size of 188 individuals. All employees with rotating shifts were approached to partake in the study, and initially, an assessment was made of 366 workers. However, factors such as refusal to participate, vacation periods, absences, dismissals, or incomplete food diary entries led to a final sample size of 213 individuals.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eData collect\u003c/h2\u003e \u003cp\u003eTrained teams were responsible for conducting the anthropometric measurements and dietary survey, which took place in the company's outpatient clinics. The collected demographic data included gender, age, self-reported skin color, and education level. Age was categorized as 30 years, 30\u0026ndash;40 years, 40\u0026ndash;50 years, and 50\u0026ndash;60 years; skin color was classified as black, brown, yellow, indigenous, or white; education level was categorized as completed high school, technical education, or university. Clinical evaluation included data on smoking, alcohol consumption, and physical activity. Smoking was divided into non-smokers (individuals who never smoked or quit more than six months ago) and smokers (currently smoking or quitting less than six months ago). Alcohol consumption was classified as yes or no. The International Physical Activity Questionnaire (IPAQ) version 8 - long form was used to assess physical activity levels, with high physical activity defined as \u0026gt;\u0026thinsp;600 MET-min/week.\u003c/p\u003e \u003cp\u003eRegarding anthropometric data, weight was measured using the TANITA body composition monitor model BC-558, with a maximum capacity of 150 kg and precision of 0.1 kg (Tanita Corporation of America, Inc., Arlington Heights, Illinois, USA). Height was measured using the portable AlturExata stadiometer with a centimeter scale and precision of one millimeter (AlturExata, Belo Horizonte, Minas Gerais, Brazil). Procedures were carried out with individuals barefoot, properly positioned, standing straight, with a straight and fixed gaze ahead. From the collected weight and height information, the Body Mass Index (BMI) was calculated using the formula: weight (kg) / height (m)\u0026sup2;, considering overweight when BMI values were \u0026ge;\u0026thinsp;25.0 kg/m\u0026sup2; (WHO, 2000).\u003c/p\u003e \u003cp\u003eBlood pressure was measured using a semi-automatic digital device from Microlife, model BP3AC1-1PC (Microlife, Widnau, Switzerland), following the parameters of the Brazilian Society of Cardiology (Pr\u0026eacute;coma et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Workers were divided into two groups based on the presence or absence of systemic arterial hypertension (SAH). Blood pressure values were determined by the average of three measurements. SAH was considered for individuals with an average systolic blood pressure equal to or greater than 140 mmHg or diastolic blood pressure equal to or greater than 90 mmHg.\u003c/p\u003e \u003cp\u003eTriglyceride, total cholesterol, and high-density lipoprotein cholesterol (HDL-c) levels were determined by enzymatic colorimetry using the Triglycerides Liquicolor Mono\u0026reg;, Cholesterol Liquicolor\u0026reg;, and Cholesterol HDL Direct-Homogeneous Direct Test\u0026reg; kits (Human do Brasil, Itabira, Brazil), respectively, on an automated Chemwell R6\u0026reg; analyzer (Awareness Technology, Palm City, FL). Low-density lipoprotein cholesterol (LDL-c) was obtained by mathematical calculation using the Friedewald formula (1972), with LDL-c (mg/dL)\u0026thinsp;=\u0026thinsp;Total cholesterol - HDL - (Triglycerides/5) when triglyceride concentration was less than or equal to 400 mg/dL. Participants with plasma triglyceride concentrations exceeding 400 mg/dL were evaluated for their LDL levels using a specific LDL Direct-Test kit.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eExplanatory: Food consumption according to NOVA classification\u003c/h2\u003e \u003cp\u003eThe instrument used to collect data related to dietary intake was the 24-hour dietary recall (R24h), administered by trained interviewers. This method involved gathering information on the timing and location of each meal, the type of food consumed, the preparation method, the quantity in portions, and, if possible, the brand of products. To assist individuals in accurately identifying consumed portions, the book \"Food Consumption: Visualizing Portions\" by Monteiro (2007) was used as a reference.\u003c/p\u003e \u003cp\u003eFood consumption was classified according to the NOVA classification (Monteiro et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The first group consisted of Fresh foods, which are those that have not undergone any type of alteration, being obtained directly from plants or animals, such as vegetables, fresh fruit, grains, roots, tubers, and others. The second group was made up of minimally processed foods, which are fresh foods that have undergone minimal alterations, such as dried, polished, and packaged grains, flours, washed roots and tubers, chilled or frozen meats, and pasteurized milk. The third group was culinary ingredients, which are those used to season and cook food and preparations (salt, sugar, oils, and fats). The fourth group is processed foods, which are the result of adding salt, sugar, and/or fat to fresh or minimally processed foods, such as canned goods, preserves, processed meat and fish, cheeses, and others. The fifth group is ultraprocessed products, which are industrial products that include additives, colorings, and flavor enhancers that make food more attractive to consumers, such as snacks, sweets, cookies, fatty snacks, hamburgers, ice cream (Supplementary Table\u0026nbsp;1w). In this study, we adopted a dual approach to classifying workers' food consumption, focusing on both the quantity and diversity of food consumed, according to the extent and purpose of processing.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eQuantitative classification of food consumption\u003c/h2\u003e \u003cp\u003eThis classification was based on the daily intake of food from each food group, following the NOVA classification. Therefore, for fresh foods, the intake of fruit and vegetables (FV) was considered, and classified according to the guidelines of the World Health Organization, which recommends the consumption of 400g per day of these foods (WHO, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), as they act in the protection and prevention of chronic non-communicable diseases, cardiovascular diseases, and neoplasms (X. Wang et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). This recommendation serves as a reference for assessing the adequacy of FVL consumption among the study participants. As for ultraprocessed foods, due to the lack of a specific minimum recommendation for UPF consumption, we opted for an analysis based on tertiles of daily caloric intake. The proportion of daily caloric value coming from UPFs was calculated using the formula [(total kcal from UPFs)\u0026times;100], and the participants were classified into three groups: tercile 1 (T1), with the lowest percentage of caloric intake coming from UPFs; tercile 2 (T2), with intermediate values; and tercile 3 (T3), with the highest percentage of UPF consumption. This classification allows for a differentiated analysis of the possible impact of UPF consumption on the participants.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eNOVA dietary diversity score (DDS-NOVA)\u003c/h2\u003e \u003cp\u003eThe DDS-NOVA is an indicator developed to assess the diversity of workers' diets, focusing on the variety of food items consumed. This score is based on the premise that greater dietary diversity is associated with a more balanced diet and, potentially, a better state of health. Diversity is measured by the presence of different types of food within each food group, as defined by the NOVA classification.\u003c/p\u003e \u003cp\u003eTo construct the DDS-NOVA, we used data from 24-hour recall records. Each type of food consumed within a specific food group gives a point to the score. For example, if a worker consumes two different types of fruit, one type of vegetable, and two types of vegetable throughout the day, they will receive a total of five points for the fresh food group. This methodology is applied in the same way to the other food groups: minimally processed, culinary ingredients, processed, and ultraprocessed.\u003c/p\u003e \u003cp\u003eThe DDS-NOVA does not take into account the frequency or quantity of consumption, but rather the variety of food items. This allows for a qualitative analysis of the diet, complementing other quantitative measures of food consumption. The choice of this method is justified by the literature, which suggests an association between dietary diversity and a reduced risk of chronic diseases. In addition, the DDS-NOVA is inspired by a study carried out in the city of S\u0026atilde;o Paulo, which validated the use of a similar score (Nova24h screener) to assess the variety of unprocessed or minimally processed whole plant foods (WPF, 33 food items) and ultraprocessed foods (UPF, 23 food items) consumed by a sample of 812 adults. Two scores are obtained from this tool by summing the number of items checked, the Nova-WPF and the Nova-UPF. The scores obtained reflected the number of subgroups of whole plant foods and ultraprocessed foods reported, providing insights into food consumption patterns in the population studied (Costa et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; dos Santos Costa et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eOutcome: Cardiovascular Disease Risk\u003c/h2\u003e \u003cp\u003eTo estimate cardiovascular risk, we employed the Framingham Global Risk Score (FRS), which calculates the 10-year risk of coronary events, cerebrovascular events, peripheral arterial disease, or heart failure. Recommended by the Atherosclerosis Department of the Brazilian Society of Cardiology (SBC-DA), the FRS is a scoring system where each characteristic (gender, age, total cholesterol, HDL-c, systolic blood pressure, diabetes, and smoking) is assigned a corresponding score. The sum of points for all variables yields a percentage indicating the 10-year risk of cardiovascular disease. The risk categories were further divided into two groups: low cardiovascular risk (\u0026lt;\u0026thinsp;5%) and intermediate to high cardiovascular risk (\u0026ge;\u0026thinsp;5%) (Pr\u0026eacute;coma et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe statistical analysis of the dataset was conducted using Stata/MP software (version 15.0). The description of variables included presenting absolute frequency values (n) and relative percentages (%), with cardiovascular risk analyzed through the Pearson chi-square test.\u003c/p\u003e \u003cp\u003eTo assess the association of food consumption variables with cardiovascular risk, both univariate and multivariate logistic regression analyses were conducted to determine the odds ratio (OR) values and their corresponding 95% confidence intervals (CI). The explanatory variables were classified as follows: Quantitative consumption of fruit and vegetables was classified dichotomously as low and high consumption, based on the recommendations of the World Health Organization. Quantitative consumption of UPFs is classified into three categories according to the tertiles of the daily calorie percentage from UPFs. The DDS-NOVA score was assessed ordinally, keeping the original score as a continuous ordinal variable in the models.\u003c/p\u003e \u003cp\u003eThe multivariate model was designed to incorporate covariates considered as confounding factors in the analysis, following the literature (Martinez-Gonzalez \u0026amp; Bes-Rastrollo, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Qu et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Santiago et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). This encompassed sociodemographic, behavioral, and dietary factors that impact the relationships between dietary intake and cardiovascular risk. Thus, the multivariate model was adjusted for the following variables: age, skin color, schooling, time working shifts, physical activity, body mass index, and total caloric intake. Collinearity among the covariates was assessed by calculating the variance inflation factor (VIF), with the \"subsetByVIF\" package in Stata, considering a maximum cutoff point of 10 (VIF\u0026thinsp;\u0026lt;\u0026thinsp;10).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThe study encompassed 213 participants aged 26 to 60, with the majority (62.4%) falling between 30 and 40 years. Demographically, 49.3% identified as brown, 56.8% had completed high school, and 79.3% were married. Professionally, 51.6% had over a decade of experience in alternate shifts. Lifestyle factors included 72.3% engaging in regular physical activity and 62.9% consuming alcohol, while smokers constituted 21.1%. Nearly half (49.8%) were classified as overweight based on their BMI. Cardiovascular risk assessments using the Framingham Global Risk Score indicated that 56.2% of workers were at low risk (\u0026lt;\u0026thinsp;5%), and 43.7% were at intermediate to high risk (\u0026ge;\u0026thinsp;5%) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e Sociodemographic. clinical. and behavioral characteristics of shift workers according to cardiovascular disease risk\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eCardiovascular Disease Risk\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;213)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow risk\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;117)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIntermediate to high risk\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eV\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge, years\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt; 30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17 (7.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (12.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (2.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.596\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u0026ndash;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e133 (62.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e97 (81.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36 (38.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e41\u0026ndash;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43 (20.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (5.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36 (38.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e51\u0026ndash;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 (9.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20 (21.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSkin color\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80 (37.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42 (35.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38 (40.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.555\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.074\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBrown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e105 (49.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59 (49.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46 (48.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28 (13.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (15.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 (10.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e121 (56.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67 (56.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54 (57.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTechnical or higher education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e92 (43.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52 (43.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40 (42.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e169 (79.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90 (75.6%\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e79 (84.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44 (20. 7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29 (24.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15 (16.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.103\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWorking time, years\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt; 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24 (11.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (16.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (5.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003e0.251\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5 to 9.9 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79 (37.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51 (42.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28 (29.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10 to 15 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e110 (51.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49 (41.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61 (64.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePhysical Activity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e59 (27.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31 (26.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28 (29.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.545\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e154 (72.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e88 (74.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e66 (70.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAlcohol consumption\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79 (37.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48 (40.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31 (33.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.270\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e134 (62.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71 (59.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e63 (67.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoking\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e151 (70.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e103 (86.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48 (51.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003e0.387\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62 (21.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 (13.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46 (48.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBody mass index\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEutrophic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55 (25.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38 (31.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17 (18.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.160\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e106 (49.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53 (44.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e53 (56.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObesity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52 (24.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28 (23.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24 (25.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eLegend: p: p-value from Pearson's chi-square test; V: value from Cramer's V test\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eDietary analysis using the DDS-NOVA score showed an average consumption of 3.46 items from fresh foods, with a range of up to nine items. Minimally processed foods averaged 3.74 items, processed foods at 2.54 items, and ultraprocessed foods at 3.39 items, with respective ranges up to six, eight, and nine items. Culinary ingredients had the lowest average at 1.27 items, with a maximum of five items consumed. The caloric intake from ultraprocessed foods was categorized into tertiles, with T1 ranging from 0\u0026ndash;14.2%, T2 from 14.3\u0026ndash;27.7%, and T3 from 27.8\u0026ndash;66.9% (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The average intake of fruits and vegetables was 272.32g (\u0026plusmn;\u0026thinsp;350.43), with 70.4% meeting the recommended intake of at least 400g per day (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation of food consumption according to extent and purpose of processing and cardiovascular risk in rotating shift workers.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFrequency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eUnivariate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eMultivariate\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-valor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep-valor\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eQuantitative food consumption\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUltraprocessed food (% kcal/day)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTercil 1 (0.0-14.2% of UPF)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e71 (33.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTercil 2 (14.3\u0026ndash;27.7% of UPF)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72 (33.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.73 (0.31\u0026ndash;1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.93 (0.37\u0026ndash;2.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.874\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTercile 3 (27.8\u0026ndash;66.9% of UPF)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70 (32.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.83 (0.33\u0026ndash;1.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.565\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.60 (0.26\u0026ndash;1.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.224\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFruit and vegetables (g/day)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt; 400g\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63 (29.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge; 400g\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e150 (70.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.67 (0.38\u0026ndash;1.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.47 (0.23\u0026ndash;0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.046\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDietary diversity score NOVA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFresh food items\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.46 (\u0026plusmn;\u0026thinsp;1.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.78 (0.65\u0026ndash;0.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.009\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.67 (1.01\u0026ndash;1.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMinimally processed food items\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.78 (\u0026plusmn;\u0026thinsp;1.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.09 (0.85\u0026ndash;1.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.502\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.15 (0.80\u0026ndash;1.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.432\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCulinary food ingredients\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.31 (\u0026plusmn;\u0026thinsp;0.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.02 (0.76\u0026ndash;1.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.884\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.93 (0.63\u0026ndash;1.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.743\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProcessed food items\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.45 (\u0026plusmn;\u0026thinsp;1.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.84 (0.67\u0026ndash;1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.81 (0.60\u0026ndash;1.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.176\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUltraprocessed food items\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.37 (\u0026plusmn;\u0026thinsp;1.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.96 (0.82\u0026ndash;1.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.641\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.30 (1.22\u0026ndash;1.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.039\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eLegend: OR: Odds ratio; CI: Confidence Interval; UPF: Ultraprocessed foods\u003c/p\u003e \u003cp\u003eMultivariate logistic regression analysis to estimate the odds ratio of high cardiovascular risk in workers according to variables related to food consumption. The model is adjusted for the following variables: age, skin color, schooling, time working shifts, physical activity, body mass index, and total caloric intake.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe adjusted multivariate model revealed that consuming\u0026thinsp;\u0026ge;\u0026thinsp;400g/day of fruits, vegetables, and legumes correlated with a 2.12-fold decrease in the likelihood of cardiovascular risk\u0026thinsp;\u0026ge;\u0026thinsp;5% (OR: 0.47; 95%CI: 0.23\u0026ndash;0.98). No significant association was found between the caloric contribution of ultraprocessed foods and cardiovascular risk. However, each additional item of fresh food consumed was linked to a 49% reduction in cardiovascular risk\u0026thinsp;\u0026gt;\u0026thinsp;5% (OR: 0.67; 95%CI: 1.01\u0026ndash;1.66), while each additional ultraprocessed food item increased this risk by 30% (OR: 1.30; 95% CI: 0.52\u0026ndash;0.87). The variety of minimally processed, processed foods, and culinary ingredients showed no significant association with cardiovascular risk (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study aimed to assess the association of food consumption, according to the degree and purpose of processing, with the risk of cardiovascular disease (CVD) in shift workers. The results indicate a significant association between the consumption of fresh foods and a reduced risk of CVD, while the consumption of ultraprocessed foods showed an increased risk. Shift work's disruption of the circadian cycle may impact lifestyle choices, contributing to cardiovascular risk.\u003c/p\u003e \u003cp\u003eCVDs represent one of the leading causes of morbidity and mortality worldwide, with estimates indicating that approximately 17.9\u0026nbsp;million people died from CVDs in 2019, which represents 32% of all global deaths (WHO, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In Brazil, CVDs are also the leading cause of death, responsible for a significant proportion of deaths and hospital admissions, impacting the Unified Health System and contributing to early work inactivity due to disability (de Oliveira et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAmong the risk factors for CVDs, working hours, especially shift work, have been associated with an increased risk of these diseases. Studies suggest that shift workers have a 40% higher risk of developing CVDs compared to those who work regular hours (D. Wang et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The hypotheses for this relationship include the disruption of circadian rhythms, changes in physical and mental well-being, and negative impacts on family life and lifestyle habits, such as physical activity and diet (Ho et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Wong et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe nutrition of individuals is another crucial factor in modulating cardiovascular risk. Consumption of ultraprocessed foods has been associated with an increased risk of CVD, with studies indicating that a diet rich in these foods can lead to a higher risk of all-cause mortality and cardiovascular disease (Qu et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Srour et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). On the other hand, the consumption of fresh foods, such as fruit and vegetables, has been consistently linked to a lower risk of CVDs (Cordova et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Dai et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Rauber \u0026amp; Levy, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In this respect, it is also important to consider both the quantity and variety of food consumed.\u003c/p\u003e \u003cp\u003eIn our study, when considering the consumption of ultraprocessed foods, we did not find a direct association with cardiovascular risk when analyzing the caloric contribution of these foods. One possible explanation for this finding is that the most frequently consumed ultraprocessed food was bread (81.0%) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), which corresponds to light bread, white pita bread, sweet bread, whole meal bread, and cheese bread. However, when comparing the ultraprocessed foods consumed, bread is less processed and has a lower calorie content. It is also important to note that most of the ultraprocessed foods consumed by the workers were offered by the company, meaning that there was a great deal of similarity in the ultraprocessed foods consumed, leading to a homogeneity of the findings. On the other hand, the diversity in the consumption of ultraprocessed foods was relevant, suggesting that a greater variety in this category of food is associated with an increase in cardiovascular risk.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacterization of the ultraprocessed foods consumed by rotating shift workers\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFoods items\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePercentage of workers that consume (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCaloric contribution (kcal/day)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePercentage (%) of total energy intake (kcal/day)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUltraprocessed breads\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e81.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e243.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUltraprocessed meat\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e89.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMargarine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e131.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCookies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e251.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSweetened beverages\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e157.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCake and bakery UPF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e299.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDairy drinks\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e203.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUltraprocessed cheese\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e116.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReady sauces\u003csup\u003e4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e172.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVegetable-based UPF\u003csup\u003e5\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eThe ultraprocessed foods considered here refer to foods that undergo a high degree of industrial processing, including adding artificial or extracted ingredients, such as emulsifiers, colorings, flavorings, and hydrogenated fat.\u003c/p\u003e \u003cp\u003eFor descriptive purposes, we present only the most frequent UPFs, not all of those consumed\u003c/p\u003e \u003cp\u003e\u003csup\u003e1\u003c/sup\u003e Including light bread, white/pita bread, whole grain/rye bread, Brazilian cheese bread\u003c/p\u003e \u003cp\u003e\u003csup\u003e2\u003c/sup\u003e Including soft drinks, processed juice, and artificial juice\u003c/p\u003e \u003cp\u003e\u003csup\u003e3\u003c/sup\u003e Including sausage/chorizo/Vienna sausage, hamburger (beef), ham/mortadella/salami.\u003c/p\u003e \u003cp\u003e\u003csup\u003e4\u003c/sup\u003e Including mayonnaise, ketchup, and mustard.\u003c/p\u003e \u003cp\u003e\u003csup\u003e5\u003c/sup\u003e Including instant mashed potatoes, soup powder, tomato sauce with artificial additives, and ready-to-eat meats (burgers, meatballs, and others) of vegetable origin\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn contrast, consumption of whole foods, fruit, vegetables, and legumes, both in quantity and variety, was associated with a lower cardiovascular risk. The World Health Organization recommends a daily intake of 400g of these foods, equivalent to five portions, to prevent chronic diseases (Srour et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Studies corroborate those diets rich in fresh foods are associated with a reduced risk of CVDs and other morbidities (Lichtenstein et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThese findings can be explained by the nutritional components present in these foods. In the case of ultraprocessed foods, the higher cardiovascular risk can be attributed to the high energy density, and high sodium and lipid content present in ultraprocessed foods, which are linked to the development of obesity and metabolic disorders such as dyslipidemia, diabetes, and hypertension, known risk factors for CVDs (Qu et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In addition, ultraprocessed foods have been associated with pathogenic mechanisms such as oxidative stress and subclinical inflammation, which can accelerate the process of atherosclerosis (Qu et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). On the other hand, higher consumption of unprocessed foods can protect against CVD, due to higher levels of soluble fiber. Soluble fiber reduces serum LDL concentrations improves glucose tolerance, and controls type 2 diabetes. Insoluble fibers improve insulin sensitivity and decrease the expression of inflammatory markers, which increase oxidative stress in the body (Howarth et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). In addition, fibers also act in weight control, due to their low energy content and the satiety that is provided for longer (Clark \u0026amp; Slavin, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTherefore, the results of this study reinforce the importance of diet quality and suggest that promoting a diet rich in fresh foods and limiting the variety of ultraprocessed foods can be effective strategies for reducing the risk of CVDs. These findings are in line with current public health guidelines that emphasize the need for a balanced and diverse diet for the prevention of cardiovascular diseases and other chronic conditions.\u003c/p\u003e \u003cp\u003eA strength of this study is its specific population of shift workers, who are often under-represented in nutritional research. In addition, the food consumption assessment method employed in this study, included both quantitative analysis and a variety of consumption scores, allowing for a comprehensive assessment of the participants' diet. The use of the DDS-NOVA score was particularly useful for differentiating between the types of food consumed and associating them with CVD risk. This proposal is in line with recent studies that propose a similar analysis, focusing on the variety of food items consumed, such as Nova-WPF, which includes unprocessed or minimally processed whole plant foods (33 food items) and Nova-UPF, which includes ultraprocessed foods (23 food items) (Costa et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; dos Santos Costa et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, some limitations should be mentioned, which include the cross-sectional design, which prevents the inference of causality, and the possibility of recall bias in the collection of dietary data. Furthermore, a population restricted to males, which makes it difficult to generalize to a heterogeneous population, environmental factors not considered but capable of influencing the quantity and quality of consumption of shift workers, such as unsuitable meal locations and the availability of healthy foods in their context. However, the results presented are considered important to contribute to the topic and future studies, which should explore the mechanisms underlying the observed associations and evaluate specific dietary interventions to reduce the risk of CVD in shift workers. Longitudinal studies are needed to confirm the direction of the associations found.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, the findings of this study add valuable insights to the existing literature, emphasizing the significance of consuming fresh and minimally processed foods while limiting ultraprocessed foods to mitigate cardiovascular risk. The distinct association between dietary patterns and cardiovascular health underscores the necessity for comprehensive public health policies and workplace health initiatives that promote nutritious dietary habits, particularly among shift workers who may be more vulnerable to cardiovascular diseases. Further research into the long-term health impacts on this demographic and the implementation of dietary interventions tailored to the degree and purpose of food processing could play a pivotal role in enhancing the overall health and well-being of these workers. This study\u0026rsquo;s outcomes advocate for a paradigm shift in dietary recommendations, focusing not just on the nutritional content but also on the quality and processing level of the foods consumed.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study received approval from the Research Ethics Committee of the Federal University of Ouro Preto (CAAE: 39,682,014.7.0000.5150) and adhered to the principles outlined in the Helsinki Declaration. Before their participation, all workers were provided with detailed information about the research objectives, procedures involved, and the potential risks and benefits. Those who voluntarily agreed to participate in the study expressed their consent by signing an informed consent form.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from all individual participants included in the study\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the Brazilian Council for Scientific and Technological Development (CNPq, Distrito Federal, Brazil) and Coordination for the Improvement of Higher Education Personnel-Brazil (CAPES); finance code 001 for Ph.D. student scholarship.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analyzed as part of the current study are not publicly available due to confidentiality agreements with subjects. However, they can be made available solely for re- view and not for publication from the corresponding author upon reasonable request\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLAAMJ, SNF, FAPP, GLLMC, FLPO, and RMNN contributed to the conception and design of the work, to the acquisition, analysis, and interpretation of data, and the draft of the manuscript. ASSS, LAAMJ, and SMLTR contributed to the analysis, interpretation of data, and draft of the manuscript. All authors revised it critically for important intellectual content and approved the submitted version.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eClark MJ, Slavin JL. The effect of fiber on satiety and food intake: A systematic review. 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(2020). \u003cem\u003eCardiovascular diseases (CVDs)\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.who.int/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds\u003c/span\u003e\u003cspan address=\"https://www.who.int/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWHO, W. H. O. Obesity: preventing and managing the global epidemic : report of a WHO consultation. World Health Organization; 2000.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWong R, Crane A, Sheth J, Mayrovitz HN, Wong R, Crane A, Sheth J, Mayrovitz HN. Shift Work as a Cardiovascular Disease Risk Factor: A Narrative Review. Cureus. 2023;15(6). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.7759/CUREUS.41186\u003c/span\u003e\u003cspan address=\"10.7759/CUREUS.41186\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Circadian rhythm, Ultraprocessed food, Fruit, Vegetables, Cardiovascular Disease","lastPublishedDoi":"10.21203/rs.3.rs-4479969/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4479969/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective:\u003c/strong\u003e To evaluate the association between food consumption, by extent and purpose of processing, and cardiovascular disease (CVD) risk among rotating shift workers.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e The cross-sectional study included 213 male shift workers. Dietary intake was assessed using a 24-hour recall method conducted by trained interviewers. Food items were classified using two approaches: the first was based on the quantity consumed from each food group. Fruits and vegetables (FV) intake, is classified as recommended at 400g per day by WHO guidelines. Ultraprocessed foods (UPFs) were analyzed based on tertiles of daily caloric contribution. The second approach, the NOVA dietary diversity score (DDS-NOVA) assessed the variety of consumed items within each food group, assigning points for each unique item consumed, irrespective of quantity or frequency. The CVD risk was evaluated using the Framingham coronary heart disease risk score (FCRS), categorizing participants as low risk (\u0026lt;5%) or intermediate to high risk (\u003cu\u003e\u0026gt;\u003c/u\u003e5%). Descriptive, univariate, and multivariate logistic regression were used.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e CVD-risk was classified as high in 43.7%. In the multivariate model, the recommended consumption of FV was associated with a lower chance of high CVD-risk (OR:0.47;95%CI:0.23-0.98), and there was no association between the amount of UPF consumption and CVD-risk. In terms of variety, fresh-food consumption was associated with a lower chance of high CVD-risk (OR:0.67;95%CI:0.23-0.98), and UPF consumption was associated with higher CVD-risk (OR:1.30;95%CI:1.12-1.87).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eConsumption of both variety and quantity of fresh-foods was associated with a lower chance of CVD-risk, while a variety of consumption of UPF items increased this chance.\u003c/p\u003e","manuscriptTitle":"Food consumption patterns and cardiovascular risk among shift workers: A NOVA-based approach","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-12 19:17:38","doi":"10.21203/rs.3.rs-4479969/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8347b6ba-f5c0-46c0-bbdb-a2cca6da9c49","owner":[],"postedDate":"June 12th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-07-08T13:32:14+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-12 19:17:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4479969","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4479969","identity":"rs-4479969","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}