ijcs International Journal of Cardiovascular Sciences Int J Cardiovasc Sci 2359-4802 2359-5647 Sociedade Brasileira de Cardiologia GQBGQSYCcTZ3F8zwyCSL79v S2359-56472023000100227 00227 10.36660/ijcs.20220066 ORIGINAL ARTICLE Food Consumption and Health Outcomes in Women During the COVID-19 Pandemic Lopes Mariana Barbosa Conception and design of the research acquisition of data analysis and interpretation of the data statistical analysis writing of the manuscript 1 0000-0001-7235-9467 Aranha Luciana Nicolau Conception and design of the research acquisition of data analysis and interpretation of the data statistical analysis writing of the manuscript critical revision of the manuscript for intellectual content 1 Pinto Lara Ribeiro Conception and design of the research acquisition of data analysis and interpretation of the data 1 Lino Paula Critina Moreira dos Santos acquisition of data 1 0000-0002-7784-9905 Luiz Ronir Raggio analysis and interpretation of the data statistical analysis 1 0000-0002-0737-6188 Oliveira Gláucia Maria Moraes de Conception and design of the research acquisition of data analysis and interpretation of the data statistical analysis writing of the manuscript critical revision of the manuscript for intellectual content 1 Rosa Glorimar Conception and design of the research acquisition of data analysis and interpretation of the data statistical analysis writing of the manuscript critical revision of the manuscript for intellectual content 1 Universidade Federal do Rio de Janeiro Rio de Janeiro RJ Brazil Universidade Federal do Rio de Janeiro, Rio de Janeiro, RJ – Brazil Mailing Address: Gláucia Maria Moraes de Oliveira Universidade Federal do Rio de Janeiro (UFRJ). Rua Prof. Rodolpho P. Rocco, 255, 8º Andar, Sala 6, Cidade Universitária. Postal code: 21941-913. Rio de Janeiro, RJ – Brazil E-mail: glauciamoraesoliveira@gmail.com

Potential Conflict of Interest

No potential conflict of interest relevant to this article was reported.

 17 07 2023 2023 36 e20220066 23 04 2022 13 02 2023 7 04 2023 This is an Open Access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Abstract Background

The COVID-19 pandemic has changed food consumption.

 Objective

Evaluate the association between metabolic phenotypes, changes in food consumption during the pandemic, and health outcomes in obese women.

 Methods

Cross-sectional observational study including 491 women without previous diagnosis of chronic diseases, evaluated according to metabolic phenotype. During the pandemic, a subsample was re-evaluated by online questionnaires via Google Forms. Analyzed anthropometric, biochemical, and dietary data as well as health outcomes (coronary artery disease, type 2 diabetes, hypertension, dyslipidemia or death). Information on mortality was collected from the Internal Affairs Office of the State of Rio de Janeiro and the Health Department of the State of Rio de Janeiro. Statistical analysis was performed using the statistical program SPSS 21, with Mann-Whitney test, Pearson’s chi-squared, Spearman correlation, and binary logistic regression, at a significance level of 5%.

 Results

The anthropometric, glucose, and lipid profiles showed significant differences between the metabolically healthy and metabolically unhealthy groups (p = 0.00). Before the pandemic, women in the metabolically unhealthy group had higher dietary intakes of lipids (p = 0.01), saturated fat (p = 0.01) and sodium (p = 0.04), during the pandemic, they consumed more energy (p = 0.04), lipids (p = 0.02), saturated fat (p = 0.02), proteins (p = 0.03) and sodium from ultra-processed foods (p = 0.03). Consequently, health outcomes were more prevalent in the metabolically unhealthy group (p = 0.00).

 Conclusion

Observed that metabolically healthy women had qualitatively better food intake and fewer health outcomes throughout the study.

 Metabolic Syndrome Inflammation Eating Obesity Introduction

Faced with the severity of the COVID-19 pandemic, different countries worldwide have adopted measures to contain the spread of the highly transmissible virus.1

With respect to the behavioral practices implemented, social distancing has had a direct impact on food consumption and mental health. The scientific literature emphasizes that, in these situations, individuals tend to regulate their negative moods, caused by anxiety, stress and fear, through consumption of unhealthy foods, in a phenomenon known as “emotional eating”.2 - 4

It is also known that the pandemic has caused financial instability with a direct impact on purchasing power, thus promoting increased consumption of ultra-processed foods, due to lower price and high availability. These factors have significantly contributed to food insecurity, leading to negative effects on quality of life. Accordingly, ultra-processed food consumption leads to the production of new inflammatory compounds, which are associated with negative health impacts.5 - 7 These impacts include weight gain, sedentarism, reduced sleep quality, development of chronic diseases, and mortality.8 , 9 Recent studies have revealed that obesity, type 2 diabetes, and arterial hypertension have a direct relation with the worsening of COVID-19, favoring acute respiratory syndrome, severe viral pneumonia, organ failure, and mortality.10 , 11

In contrast, few studies in the scientific literature have simultaneously evaluated the impacts of changes in eating habits, taking into consideration body composition analysis and changes in food consumption, both quantitatively by means of dietary adequacy and qualitatively by means of the NOVA classification criteria, in addition to considering possible health outcomes during the COVID-19 pandemic.

Accordingly, the objective of this study was to evaluate the influence of dietary intake of processed and ultra-processed foods on dietary adequacy and body composition in women with different metabolic phenotypes, associating them with possible health outcomes and mortality during the COVID-19 pandemic.

 Methods Study group

This was a cross-sectional observational study, characterized, at an initial moment, by evaluation of a series of patients during a period before the pandemic, with socioeconomic, dietary, biochemical, and anthropometric data collected in person by trained professionals from 554 individuals who voluntarily registered at the Center for Research and Extension in Clinical Nutrition of the Clementino Fraga Filho University Hospital, referring to the period before the COVID-19 pandemic. At a second moment, in March 2021, during the second period of social isolation of the COVID-19 pandemic, the volunteers were reevaluated via Google Forms online questionnaires. Inclusion criteria for the study were women with body mass index (BMI) ≥ 30 kg/m2, from 18 years to 59 years and 11 months of age. Exclusion criteria were the following: individuals ≥ 60 years of age, individuals with history or diagnosis of chronic diseases or under previous nutritional monitoring, normal weight or overweight individuals, and male individuals. Given that this was a convenience sample, there was no justification for using sample size calculation to estimate the sample population. After analyzing the inclusion criteria, 491 women were selected for the study, between the years of 2011 and 2019 ( Supplementary figure 1 ).

The research project received approval from the Research Ethics Committee of the Clementino Fraga Filho University Hospital, under CAE number 89033118.1.0000.5257, approved on 07 July 2018. During the pandemic, an amendment was made to the approved project, asking the CEP for an authorization for the online reassessment, approved on July 22, 2020.Voluntary participation was authorized following verbal and written clarification, by means of a free and informed consent form, in accordance with resolution 466/2012.

 Metabolic groups, anthropometry, blood pressure, and physical activity

The scientific literature suggests that obesity represents a heterogeneous condition, with differentiated risks related to metabolic alterations. The characterization of metabolic groups was performed according to the NCEP-ATP III classification, based on the metabolically healthy obese (MHO) phenotype with BMI greater than 30 kg/m2 and without hyperglycemia, dyslipidemia, hypertension, and/or inflammatory processes; and the metabolically unhealthy obese (MUHO) phenotype with the presence of at least 3 of the following criteria: waist circumference ≥ 88 cm; triglycerides ≥ 150 mg/dL; HDL < 50 mg/dL; blood glucose > 110 mg/dL; blood pressure: ≥ 130/85 mmHg.5 , 12 - 15 Anthropometric evaluation considered data on body weight, height, waist circumference in cm, and BMI in kg/m2. Classification of systemic blood pressure followed the cutoff points established by the NCEP-ATP III in 2002.15

Degree of physical activity was evaluated by applying the short version of the International Physical Activity Questionnaire (IPAQ), with only 2 categories: sedentary (including women who were irregularly active and sedentary) and active (including women who were active and very active).16

 Biochemical evaluation

Blood collection was carried out before the pandemic, following the protocol of a 14-hour fast for analysis of blood glucose and plasma insulin; evaluation of blood lipid profile followed the cut-off point established by the NCEP-ATP III in 2002. Insulin resistance was estimated using the HOMA-IR method.15

Biochemical analyses were performed in duplicate by means of an automated method (Automatic Analyzer A25, BioSystems), using commercial BioSystems kits. Serum concentrations of glucose, triglycerides, total cholesterol, and HDL were evaluated. LDL values were calculated following the formula by Friedewald et al (1972), which is valid only if the triglyceride concentration is less than 400 mg/dL. Insulin was obtained from blood samples and analyzed by the ELISA method (Ultra Sensitive Insulin ELISA Kit, DRG) on a BRIO 2 Radim device.17

 Evaluation of dietary adequacy and food consumption

Dietary adequacy was evaluated by analyzing the mean values of food consumption from 3-day food records, which were self-completed by the study volunteers, referring to 2 weekdays and 1 weekend day. Volunteers were instructed to fill in food records based on food consumption; when the records were complete, they were delivered in person before the pandemic and sent via email during the pandemic.

The calculation for the analysis of dietary adequacy was performed using the software Food Processor, version 7.2. Evaluation of dietary adequacy was based on the estimated average requirement (EAR) to meet the nutritional needs of a group and the recommended acceptable macronutrient distribution range (AMDR) from the Institute of Medicine (IOM).18 - 20

Qualitative evaluation of food consumption followed the NOVA classification criteria, which subdivides foods into the following 4 categories: unprocessed or minimally processed foods, processed culinary ingredients, processed foods, and ultra-processed foods.21

 Evaluation of health outcomes during the COVID-19 pandemic and death

Evaluation of health outcomes was carried out by trained professionals, by telephone, with a standardized approach and online questionnaires that were self-filled via Google Forms by volunteers during the pandemic, during the period corresponding to the second lockdown in the city of Rio de Janeiro, evaluating whether they showed any changes in body weight or waist circumference during the pandemic as well as the presence of occurrences such as hospitalization and surgical procedures. New diagnoses were confirmed by analysis of medications taken by the volunteers; the most commonly reported were rosuvastatin, enalapril, atorvastatin, and captopril. We also evaluated whether the volunteers had health complications during the last 2 years.

During reevaluation, volunteers were virtually instructed to measure body weight on a home scale and waist circumference by passing the measuring tape at the height of the narrowest waist.21 On the online questionnaire, volunteers were also asked about COVID-19 diagnosis by PCR, presence of symptoms, and hospitalization due to COVID-19; these data were self-reported.

Data on all-cause mortality were collected from the website of the Judicial Internal Affairs Office of the State of Rio de Janeiro, and multiple-cause mortality data were collected from death certificates made available on the Mortality Information System (SIM, acronym in Portuguese) of the Health Department of the State of Rio de Janeiro. 22 - 24

 Statistical analysis

Statistical analyses were conducted using IBM® SPSS® Statistics software, version 21. Categorical variables were expressed as percentages and analyzed using the chi-square test. The normality of continuous variables was evaluated using the Kolmogorov-Smirnov method. The continuous variables did not present a normal distribution and, therefore, were expressed as median and interquartile range. Mann-Whitney test, Spearman correlation, and binary logistic regression were also used (with the aim of predicting the values taken during reevaluation of volunteers as a function of the independent variables related to the beginning of the study). P values < 0.05 were considered significant.

 Results

We evaluated 491 women without any disease diagnosis or previous nutritional follow-up. Their characteristics are shown in Table 1 . The MHO group accounted for 51.73% of the sample (n = 254), with a median age of 38 years (20 to 59), and the MUHO group accounted for 48.27% of the sample (n = 237), with a median age of 46 years (20 to 59). The MHO group had higher level of education, lower per capita income, and higher level of physical activity.

– Anthropometric characteristics, blood pressure, and biochemical evaluation according to metabolic classification during the pre-pandemic and pandemic periods.
Pre-pandemic period
Variables Total (n = 491) Metabolically healthy (n = 254) Metabolically unhealthy (n = 237) P value Reevaluated (n = 29) Not reevaluated (n = 462) P value
Age (years) 43 (20 – 59) 38 (20 – 59) 46 (20 – 59) 0.00 40 (24 – 53) 43 (20 – 59) 0.231
Weight (kg) 88 (57.4 – 165) 82.62 (57.4 – 145.2) 92.50 (59.3 – 165) 0.01 93.65 (70.2 – 145.2) 87.5 (67.4 – 165) 0.408
BMI (kg/m 2 ) 34.52 (26 – 64.65) 32.80 (27.11 – 51.72) 35.85 (26 – 64.65) 0.00 36.78 (26.01 – 51.45) 33.44 (26.84 – 49.61) 0.148
Waist circumference (cm) 103.0 (84.5 – 160) 99.1 (84.5 -146) 106.5 (89 – 160) 0.00 108.5 (83 – 131.7) 102.5 (84.5 – 160) 0.276
SBP (mmHg) 120 (90 – 200) 115 (90 – 200) 127 (90 – 200) 0.00 120 (90 – 160) 120 (90 – 200) 0.132
DBP (mmHg) 80 (50 – 130) 80 (50 – 120) 80 (50 – 130) 0.00 80 (50 – 100) 80 (50 – 130) 0.132
Blood glucose (mg/dL) 92 (57 – 406) 88 (57 – 228) 98 (63 – 406) 0.000 95 (65 – 189) 92 (57 – 406) 0.461
Total cholesterol (mg/dL) 201 (118 – 362) 195.5 (122 – 335) 207 (118 – 362) 0.009 202 (149 – 291) 201 (118 – 362) 0.306
LDL cholesterol (mg/dL) 122 (42 – 291) 121 (48 – 150) 126 (42 – 291) 0.185 122 (72 – 227) 122.5 (42 – 291) 0.583
HDL cholesterol (mg/dL) 46 (18 – 119) 51 (26 – 119) 42 (18 – 105) 0.000 47 (34 – 83) 46 (18 – 119) 0.480
Triglycerides (mg/dL) 126 (34 – 1463) 99 (34 – 635) 180.8 (63 – 1463) 0.000 114 (34 – 275) 126.5 (36 – 1463) 0.291
HOMA-IR 1.90 (0.17 – 31.37) 2.00 (0.17 – 15) 2.85 (0.19 – 31.37) 0.000 0.83 (0.18 – 3.00) 1.91 (0.17 – 3.38) 0.340
Insulin 8.27 (1 – 95.48) 7.98 (1 – 63) 8.37 (1 – 95.48) 0.000 4.49 (1 – 20) 8.38 (1 – 95) 0.744
Level of education

Completed primary = 31.05%

Completed secondary = 48.75 %

Tertiary or higher = 20.2%

Completed primary = 27.9 %

Completed secondary = 51.2 %

Tertiary or higher = 20.9 %

Completed primary = 34.2%

Completed secondary = 46.3 %

Tertiary or higher = 19.5 %

 		0.01

Completed primary = 34.5%

Completed secondary = 41.4 %

Tertiary or higher = 24.1%

Completed primary = 32 %

Completed secondary = 49.4%

Tertiary or higher = 18.6%

 		0.880
Skin color

White = 26.2%

Non-white = 73.8%

White = 28.3 %

Non-white = 71.7%

White = 24.1% Non-

white = 75.9%

 		0.35

White = 20.7%

Non-white =79.3 %

White = 29.6%

Non-white =70.4 %

 		0.555
Tobacco use

Non-smokers = 95.15%

Smokers = 0.8%

Ex-smokers = 4.05%

Non-smokers = 94.5%

Smokers = 0.8%

Ex-smokers = 4.7%

Non-smokers = 95.8%

Smokers = 0.8%

Ex-smokers = 3.4%

 		0.75

Non-smokers = 96.6 %

Smokers = 0%

Ex-smokers = 3.4%

Non-smokers = 95 % Smokers = 0.8%

Ex-smokers = 4.2%

 		0.920
Physical activity (pre-pandemic)

Sedentary = 71.1%

Active = 28.9%

Sedentary = 65.5%

Active = 34.5 %

Sedentary = 81.3%

Active = 18.7 %

 		0.22

Sedentary = 96.6%

Active =3.4 %

Sedentary = 72.7 %

Active =27.3 %

 		0.124
Physical activity (pandemic)

Sedentary = 59.2%

Active = 40.8%

Sedentary = 62.1

Active = 37.9%

Sedentary = 56.3%

Active = 43.8%

 		0.03
Marital status

Single = 44.3 %

In a relationship = 55.7 %

Single = 46.2 %

In a relationship = 53.8 %

Single = 42.2%

In a relationship = 57.8 %

 		0.75

Single = 48.3%

In a relationship = 51.7%

Single = 44.4%

In a relationship = 55.6%

 		0.667
Per capita income (Brazilian reals) 979 (110 – 15180) 742.50 (110 – 7150) 1210 (231 – 15180) 0.02 759 (220 – 7150) 1001 (110 – 15180) 0.590
Pandemic period
Variables Total (n = 29) Metabolically healthy (n = 14) Metabolically unhealthy (n = 15) P value
Current weight (kg) 87 (62 – 135) 84 (62 – 127) 90 (74 – 135) 0.17
BMI (kg/m 2 ) 33.29 (29.44 – 49.61) 31.46 (29.44 – 49.61) 33.66 (30.12 – 45.61) 0.29
Waist circumference (cm) 104.50 (76 – 146) 103 (76 – 146) 109 (85 – 130) 0.63

Results are expressed as percentages, median, and interquartile range. Abbreviations: BMI, body mass index; DBP, diastolic blood pressure; SBP, systolic blood pressure. Statistical analysis: For the variables of age and per capita income, Mann Whitney test independent samples t test. For other variables from the pre-pandemic period, Pearson’s chi-squared test. For comparative analysis during the pandemic period, Mann Whitney test was used. Significant values correspond to p < 0.05.

Reevaluation was carried out in March 2021, during the second period of social isolation. Less than 30% of contacts from the initial sample were retrieved (n = 120). Among the volunteers contacted, only 24.16% (n = 29) completed the reevaluation questionnaires. The other volunteers did not respond to contact attempts via e-mail and/or changed their telephone numbers. It was thus necessary to perform a re-characterization of this subsample of the study population, in order to assess its representativeness in relation to the other volunteers in the study. Of the volunteers who completed the reevaluation (n = 29), 51.73% belonged to the MUHO group (n = 15). Subsequently, binary logistic regression was carried out in order to observe whether the women who were reevaluated (n = 29) had any similarity in relation to the rest of the sample that was not reevaluated (n = 462). We were able to conclude that there was no evidence that the women who completed the reevaluation were different from the women who did not, and they were good representatives of the study population ( Table 1 ).

In comparative evaluation, the MUHO group had worse anthropometric and biochemical profile when compared to the MHO group. There were no statistically significant differences in the variables reevaluated throughout the pandemic ( Table 1 ).

Among all women followed up in the study (n = 491), 2% died (n = 10), with 0.6% (n = 3) in the MHO group and 1.4% in the MUHO group (p = 0.10). Deaths were related to neoplasms (n = 4), cardiovascular diseases (n = 2, both in the MUHO group), unspecified respiratory failure (n = 1), pulmonary embolism (n = 1), and causes that were not identified in the databases (n = 2).

We observed that the MUHO group had more harmful health outcomes. This evaluation was confirmed by the medications used and self-reported by the volunteers. Diagnoses of other diseases were reported, such as polycystic ovary syndrome, endometriosis, and non-alcoholic steatohepatitis, which stood out to the extent that it affected approximately 31.3% of metabolically unhealthy women and 3.44% of metabolically healthy women. The other reported diseases affected less than 6% of the total sample. The percentage of hospital admissions due to aesthetic reasons and for cesarean section was higher in MHO (13.79% versus 6.25%) than in MUHO (p = 0.18) ( Figure 1 ).

– Characterization of health outcomes in the sample. Health outcomes: death, CAD, DM2, SAH, and dyslipidemia. Diagnosis of CAD was confirmed by evaluating the medications used by the study volunteers, the most reported being: rosuvastatin, enalapril, atorvastatin, and captopril (p = 0.00). Results are displayed in percentages. Data were self-reported via questionnaire. Abbreviations: CAD: coronary artery disease; DM2: diabetes mellitus type 2; SAH: systemic arterial hypertension. Statistical analysis: Pearson’s chi-square test, where significant values correspond to p < 0.05, represented by the asterisk (*).

With respect to evaluation of health complications associated with COVID-19, 55.2% of MHO and 50% of MUHO reported positive PCR for COVID-19 (p = 0.09), and 51.7% of MHO and 75% of MUHO reported COVID-19 symptoms (p = 0.05). The most commonly reported was dyspnea on minor exertion, which was present in 44% of MHO and 31.3% of MUHO (p = 0.61). Additionally, 17.2% of MHO and 37.5% of MUHO reported that they sought nutritional counseling (with a professional nutritionist) to change their eating habits during the pandemic (p = 0.13).

Evaluation of adequacy of dietary composition during the pre-pandemic period showed a significant difference in relation to intake of lipids (p = 0.01), saturated fat (p = 0.01), and total dietary sodium (p = 0.04). Analysis of caloric intake from ultra-processed foods showed no significant differences between the groups ( Table 2 ). However, during the pandemic period, there was a significant difference in intake of energy (p = 0.04), lipids (p = 0.02), saturated fat (p = 0.02), and proteins (p = 0.03). Evaluation of dietary composition and adequacy are displayed in Table 2 . Quantitative analysis of caloric intake from ultra-processed foods showed a significant difference in sodium intake from ultra-processed foods between the groups during the pandemic period (p = 0.03) ( Table 2 ).

– Evaluation of dietary adequacy and composition in the sample during the pre-pandemic and pandemic periods
Pre-pandemic period
Dietary intake EAR/AMDR Total (n=491) Metabolically healthy (n=254) Metabolically unhealthy (n=237) P value
Energy (kcal/day) - 1670.79 (137.40 – 7468.96) 1594.93 (137.40 –5216.91) 1721.94 (372.10 –7468.94) 0.10
Lipids (g) 37.13 g – 64.97 g (20 – 35% of TEV) 48.80 (0.92 – 322.93) 44 (0.92 – 169.72) 52.41 (3.36 – 322.93) 0.01
Saturated fat (g) ≤ 13 g (≤ 7% of TEV) 16.32 (0.10 – 104.96) 13.87 (0.10 – 71.19) 17.16 (0.75 – 104.96) 0.01
MUFA (g) 5.57 g (3% of TEV) 9.49 (0 – 86.91) 8.70 (0 – 57.97) 10.14 (0.13 – 86.91) 0.18
PUFA (g) 7.42 g (4% of TEV) 4.29 (0 – 85.64 ) 4.2 (0 – 50.23) 4.46 (0.11 – 85.64) 0.14
Carbohydrates (g/day) 188 g – 271.50 g (45 – 65% of TEV) 223.88 (8.86 – 1501) 215.83 (8.86 – 1210.85) 232.24 (43 – 1501) 0.26
Fibers (g/day) 25 – 30 16 (0.5 – 79.18) 16 (1.5 – 79.18) 15.94 (0.5 – 70.57) 0.75
Proteins (g/kg/day) 42 g – 146 g 10 – 35% of TEV 75.10 (7.13 - 219.37) 72.65 (11.22 – 219.37) 79 (7.13 – 213.17) 0.07
Total sodium (mg/day) 2000 1887.35 (0 – 13342.45) 1983.00 (0 – 12158.84) 1731.52 (123.54 – 13342.45) 0.04
Vitamin E (mg/day) 12 1.53 (0 – 22.35) 1.35 (0 – 19.32) 1.68 (0.02 – 22.35) 0.14
Vitamin C (mg/day) 60 58.57 (0 – 7875.81) 64.06 (0 – 7875.81) 54.43 (0 – 4096.43) 0.43
Zinc (mg/day) 6.8 6.15 (0 – 38.28) 6.67 (0.23 – 38.28) 5.71 (0 – 26.56) 0.15
Selenium (mcg/day) 45 45.37 (0 – 375.15) 47.38 (0.06 – 296.35) 44.94 (0 – 375.15) 0.38
Evaluation of ultra-procesed food consumption
Dietary intake from ultra-processed foods Total (n=435) Metabolically healthy (n=227) Metabolically unhealthy (n=208) P value
Energy (kcal) 382.21 (0 – 5046.98) 363.38 (28.57 – 2845.87) 387.20 (0 – 5046.98) 0.33
Total fats (g) 16.42 (0 – 338.53) 16.30 (0 – 338.53) 17 (0 – 147.17) 0.77
Saturated fat (g) 4.60 (0 – 82.30) 4.32 (0 – 82.30) 5.0 (0 – 50.44 ) 0.38
Carbohydrates (g) 43.85 (0 – 1161. 11) 40.74 (0 – 564.80) 46.72 (0 – 1161. 11) 0.37
Sugars (g) 18.81 (0 – 1074.50) 18.32 (0 – 492.33) 19.44 (0 – 1074.50) 0.89
Sodium (mg) 474.66 (0 – 5214.50) 471.76 (0.68-3775.25) 489.56 (0 – 5214.50) 0.05
Pandemic period
Dietary intake EAR/AMDR Total (n=29) Metabolically healthy (n=14) Metabolically unhealthy (n=15) P value
Energy (kcal/day) - 1441.80 (733 – 4239.40) 1325.06 (856.32 – 1918.42) 1721.65 (733.00 – 4239.40) 0.04
Lipids (g) 32 g – 56 g (20 – 35% of TEV) 32 (5.6 – 98.85) 23.75 (7.23 – 56.81) 35.09 (5.6 – 98.85) 0.02
Saturated fat (g) ≤ 11.1 g (≤ 7% of TEV) 9.52 (1.51 – 43.81) 8.52 (1.61 – 19.02) 13.22 (1.51 – 43.81) 0.02
MUFA (g) 4.8g (3% of TEV) 8.32 (0.16 – 40.05) 5.57 (0.16 – 16.30) 9.65 (0.65 – 40.05) 0.05
PUFA (g) 6.4 g (4% of TEV) 2.63 (0.70 – 10.19) 2.60 (0.70 -10.19) 2.71 (1.39 – 8.89) 0.88
Carbohydrates (g/day) 162.20 g – 234.29 g (45 – 65% of TEV) 218.67 (93.25 – 914.50) 202.7 (129.83 – 312.20) 237.04 (93.25 – 914.50) 0.19
Fibers (g/day) 25 – 30 20.40 (5.86 – 61.10) 18.18 (5.86 -42.26) 23.26 (9.20 – 61.10) 0.12
Proteins (g/kg/day) 36 g – 126.16 g (10 – 35% of TEV) 74.02 (22.40 – 139.60) 73.16 (22.40 – 109.60) 89.27 (43 – 139.60) 0.03
Total sodium (mg/day) 2000 1365.80 (115.20 – 3269.90) 1139.36 (64.70 – 2563.47) 1446.63 (115.20 – 3269.90) 0.15
Vitamin E (mg/day) 12 1.75 (0 – 6.81) 2.0 (0.13 – 6.81) 1.66 (0 – 4.62) 0.76
Vitamin C (mg/day) 60 193.23 (3.64 – 552.10) 162.09 (11.70 – 407.10) 293.99 (3.64 – 552.10) 0.28
Zinc (mg/day) 6.8 5.0 (1.72 – 25) 3.99 (1.72 – 11.21) 5.91 (2.70 – 25) 0.06
Evaluation of ultra-procesed food consumption
Dietary intake from ultra-processed foods Total (n=29) Metabolically healthy (n=14) Metabolically unhealthy (n=15) P value
Energy (kcal) 225.70 (0 – 1043.80) 208.82 (0 – 1043.80) 265.85 (0 – 861.33) 0.41
Total fats (g) 10.20 (0 – 45.51) 10.18 (0 – 45.51) 11.57 (0 – 42.53) 0.35
Saturated fat (g) 2.68 (0 – 24.49) 2.68 (0 – 13.22) 3.1 (0 – 24.49) 0.26
Carbohydrates (g) 29.70 (0 – 137.90) 27.90 (0 – 137.90) 35.64 (0 – 107.97) 0.44
Sugars (g) 12.45 (0 – 85.05) 12.45 (0 – 53.89) 13.95 (0 – 85.05) 0.49
Sodium (mg) 618.40 (59.41 – 1278.44) 346.22 (64.70 – 1278.44) 769.67 (59.41 – 1148) 0.03

Calculations were performed using Food-Processor and SPSS version 21 software. Results are expressed as median and minimum and maximum values. Abbreviations: AMDR, macronutrient distribution range; EAR, estimated average requirement; MUFA, monounsaturated fatty acids; PUFA, polyunsaturated fatty acids. Statistical analysis: Mann Whitney test was used where significant values correspond to p <0.05.

Qualitative dietary evaluation was divided into 3 parts. The first part comprised comparative analysis between the groups, where the frequency of ultra-processed foods was higher in the MUHO group before (p = 0.10) and during the pandemic (p = 0.05). The second analysis evaluated the percentage of intake of ultra-processed foods in relation to total energy value (TEV), dividing the groups into 4 intake ranges. We observed that the MUHO group consumed more ultra-processed foods, especially in the ranges “30% to 45% of TEV” and “≥ 45% of TEV”, before (p = 0.29) and during the pandemic (p = 0.12). Finally, the third analysis evaluated food consumption using the NOVA classification. We did not observe a significant difference in the consumption of ultra-processed foods (%) in relation to the total energy value during the pre-pandemic and pandemic periods and in the ranking of the 15 most consumed foods according to metabolic phenotypes. We observed that the food base of the population in both groups consisted mainly of processed and ultra-processed foods that were rich in simple carbohydrates and saturated fat ( Figures 2 and 3 ).

– Ultra-processed food consumption (%) in relation to total energy value during the pre-pandemic (p = 0.29) and pandemic (p = 0.12) periods per person according to classification of metabolic phenotypes. The lighter shades represent the percentage intake in the MHO group, and the darker shades represent percentage intake in the MUHO group. Data are expressed as percentage of intake per person. Calculations were performed using Food-Processor and SPSS version 21. Abbreviations: MHO: metabolically healthy obese; MUHO: metabolically unhealthy obese; TEV: total energy value. Statistical analysis: chi-square test, where significant values correspond to p <0.05.

– Ranking of the 15 foods most consumed by the study sample according to metabolic phenotypes and NOVA classification. Above shows the ranking of the most consumed foods during the pre-pandemic period, and below shows the ranking of the most consumed foods during the pandemic period, according to the NOVA classification (Monteiro, 2019). Results are displayed as percentages. Data were self-reported via questionnaire. Abbreviations: MHO: metabolically healthy obese; MUHO: metabolically unhealthy obese.

The correlation test between health outcomes and food consumption during the pre-pandemic period demonstrated that dietary intake of ultra-processed foods between the groups was positively associated with the amount of total carbohydrates (ρ = 0.878; p = 0.00), total lipids (ρ = 0.766; p = 0.00), and saturated fat (ρ = 0.670; p = 0.00) in the diet. In contrast, during the pandemic period, dietary intake of ultra-processed foods between the groups was positively associated with the amount of total carbohydrates (ρ = 0.844; p = 0.00), total dietary sodium (ρ = 0.511; p = 0.00), and saturated fat (ρ = 0.520; p = 0.00) in the diet. The remaining variables did not show statistically significant results during either of study periods.

 Discussion

This was a pilot study that evaluated the dietary adequacy of Brazilian women with different metabolic phenotypes, using the NOVA classification to categorize food consumption before and during the COVID-19 pandemic, correlating it with anthropometric and biochemical data, as well as adverse health outcomes and mortality. 21

It has already been well established in the scientific literature that individuals who have a diet with a higher proportion of fibers, fruits, vegetables, and legumes, as well as lower consumption of saturated fat, dietary cholesterol, and refined carbohydrates have lower levels of total cholesterol, triglycerides, and blood glucose, in addition to lower body weight, waist circumference, and blood pressure, thus presenting a better metabolic profile. On the other hand, it has been observed that the pandemic provoked risk behaviors and changes in dietary patterns, which increase risk factors for chronic non-communicable diseases, the worsening of COVID-19 and fatal outcomes associated with comorbidities.22 , 23 , 25

Our study stands out from others insofar as we carried out an evaluation that simultaneously encompassed quantitative and qualitative dietary characteristics, considering the processing steps of the foods consumed by the sample, in order to associate them with harmful health outcomes and mortality. In general, the studies present in the literature, such as the Brazilian studies by Malta (2020) and Moraes Lamounier (2021), evaluated dietary intake, taking into consideration analysis of dietary adequacy, exploring only the qualitative characteristics of ultra-processed food consumption, in comparison to daily energy intake. It is worth underscoring that both studies associated ultra-processed food consumption with greater risks of developing cancer, diabetes mellitus, and arterial hypertension.26 , 27

Similarly, studies carried out during the pandemic associated changes in food consumption with excess weight during isolation, reinforcing the impacts of social distancing on diet, body weight, and health outcomes.28 , 29 We also observed the maintenance of excess weight in the women in our study, especially in the MUHO group. Other studies conducted in Italy and the United Kingdom have correlated ultra-processed food consumption with increased variety of unhealthy food consumption, as well as increased binge eating during social isolation, suggesting an increase in the practice of emotional eating during the pandemic.30 , 31

Health outcomes and mortality were more recurrent in the MUHO group, which simultaneously presented higher ultra-processed food consumption and a clear deterioration in the quality of food consumption during the pandemic period.21 When observing dietary intake, we found that the MUHO group had higher intake of total lipids, saturated fat, and monounsaturated fatty acids. These results were also associated with dietary inadequacy and higher ultra-processed food consumption in the same group. In the literature, other studies have associated the health impacts caused by confinement to feelings of boredom and stress, justifying the worse quality of food, bringing the concept of emotional eating and changes in eating patterns back into the debate.29 , 32

Regarding the qualitative context of diet, we found that the pandemic intensified changes in food consumption in the study population, especially in the MUHO group. Malta et al found reduced consumption of healthy foods and increased consumption of ultra-processed foods during the pandemic. These behavioral changes were negatively correlated with diet quality and positively correlated with risk of chronic diseases.26

In our study, we observed that energy intake from ultra-processed foods in MUHO individuals intensified and oscillated between 30% and 45% of daily energy intake during the pandemic. This behavior differed from the characteristics shown by the MHO group, where ultra-processed food consumption was less than 15% and was limited to 30% of daily energy intake, suggesting that there was a difference in ultra-processed food consumption between the groups, especially during the pandemic period. In 2021, a study associated ultra-processed food consumption with increased risk of cardiovascular events and mortality in overweight individuals, regardless of factors such as sex and age, concluding that, for each additional daily serving of ultra-processed foods, there was a 9% increase in mortality.33

In the literature, it has not described cut-off points that establish a safe amount for the intake of ultra-processed foods. It has been observed that the higher the consumption of these products, the greater the negative impact on health.34 The studies cited in this article have shown that consumption of these foods reached very wide intake ranges, oscillating between 15%, 20%, and 60% of daily energy intake It is becoming increasingly evident that behavioral factors can interfere with diet and cause increased inflammation and development of diseases in the long or short term. In our study, we observed that the MUHO group had higher intake of energy, lipids, and saturated fat in their diet, which is concerning, considering the characteristics of the sample and the worsening of the pandemic. Therefore, public health measures and actions that discourage the consumption of ultra-processed foods are necessary to minimize possible adverse effects of the pandemic.

The greatest limitation to our study was the loss of volunteers for reevaluation of food consumption. The vast majority of volunteers changed their telephone number or did not respond to attempts to contact them via e-mail and/or telephone. This is added to the fact that a large number of the participants in the MUHO group reported that, during the pandemic, they sought nutritional support in order to change their lifestyle. One of hypothesis for this was the wide dissemination of risk factors that associated excess weight with worse health outcomes in COVID-19. We believe that concerns regarding being overweight led these women to seek professional help. Nonetheless, we observed that the volunteers did not adhere to the changes in eating behavior, seeing that dietary inadequacy was maintained throughout the pandemic.

Our study’s strengths include the analysis of the impacts of changes in food consumption in the context of the COVID-19 pandemic, considering metabolic phenotypes and the current NOVA classification of foods. Furthermore, this allowed thorough evaluation of the influence of dietary composition and food consumption during the pandemic on the occurrence of health outcomes and mortality.

 Conclusion

We observed an increase in the frequency of consumption of processed and ultra-processed foods during the pandemic period, especially among MUHO women. Accordingly, increased consumption of these foods negatively influenced dietary quality and the worsening of behavioral risk factors during the COVID-19 pandemic. Therefore, the negative effects of social isolation may have medium- and long-term health consequences.

 * Supplemental Materials

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 References 1. World Health Organization. Novel Coronavirus (2019-nCoV): Situation Report, 12. Geneva: World Health Organization; 2020. World Health Organization Novel Coronavirus (2019-nCoV): Situation Report, 12 Geneva World Health Organization 2020 2. Haushofer J, Metcalf CJE. Which Interventions Work Best in a Pandemic? Science. 2020;368(6495):1063-5. doi: 10.1126/science.abb6144. Haushofer J Metcalf CJE Which Interventions Work Best in a Pandemic? Science 2020 368 6495 1063 1065 10.1126/science.abb6144. 3. Lima ER, Silva TSS, Vilela ABA, Rodrigues VP, Boery RNS. Implications of the COVID-19 Pandemic in Brazilian Food Habits: Integrative Review. Res Soc Dev. 2021;10(14):e29810414125. doi: 10.33448/rsd-v10i4.14125. Lima ER Silva TSS Vilela ABA Rodrigues VP Boery RNS Implications of the COVID-19 Pandemic in Brazilian Food Habits: Integrative Review Res Soc Dev 2021 10 14 e29810414125 10.33448/rsd-v10i4.14125. 4. Antiporta DA, Bruni A. Emerging Mental Health Challenges, Strategies, and Opportunities in the Context of the COVID-19 Pandemic: Perspectives from South American Decision-Makers. Rev Panam Salud Publica. 2020;44:e154. doi: 10.26633/RPSP.2020.154. Antiporta DA Bruni A Emerging Mental Health Challenges, Strategies, and Opportunities in the Context of the COVID-19 Pandemic: Perspectives from South American Decision-Makers Rev Panam Salud Publica 2020 44 e154 10.26633/RPSP.2020.154. 5. De Lorenzo A, Soldati L, Sarlo F, Calvani M, Di Lorenzo N, Di Renzo L. New Obesity Classification Criteria as a Tool for Bariatric Surgery Indication. World J Gastroenterol. 2016;22(2):681-703. doi: 10.3748/wjg.v22.i2.681. De Lorenzo A Soldati L Sarlo F Calvani M Di Lorenzo N Di Renzo L New Obesity Classification Criteria as a Tool for Bariatric Surgery Indication World J Gastroenterol 2016 22 2 681 703 10.3748/wjg.v22.i2.681. 6. Kim MK, Cho SW, Park YK. Long-Term Vegetarians Have Low Oxidative Stress, Body Fat, and Cholesterol Levels. Nutr Res Pract. 2012;6(2):155-61. doi: 10.4162/nrp.2012.6.2.155. Kim MK Cho SW Park YK Long-Term Vegetarians Have Low Oxidative Stress, Body Fat, and Cholesterol Levels Nutr Res Pract 2012 6 2 155 161 10.4162/nrp.2012.6.2.155. 7. Bonaccio M, Costanzo S, Ruggiero E, Persichillo M, Esposito S, Olivieri M, et al. Changes in Ultra-Processed Food Consumption During the First Italian Lockdown Following the COVID-19 Pandemic and Major Correlates: Results from Two Population-Based Cohorts. Public Health Nutr. 2021;24(12):3905-15. doi: 10.1017/S1368980021000999. Bonaccio M Costanzo S Ruggiero E Persichillo M Esposito S Olivieri M et al Changes in Ultra-Processed Food Consumption During the First Italian Lockdown Following the COVID-19 Pandemic and Major Correlates: Results from Two Population-Based Cohorts Public Health Nutr 2021 24 12 3905 3915 10.1017/S1368980021000999. 8. Mattioli AV, Pinti M, Farinetti A, Nasi M. Obesity Risk During Collective Quarantine for the COVID-19 Epidemic. Obes Med. 2020;20:100263. doi: 10.1016/j.obmed.2020.100263. Mattioli AV Pinti M Farinetti A Nasi M Obesity Risk During Collective Quarantine for the COVID-19 Epidemic Obes Med 2020 20 100263 10.1016/j.obmed.2020.100263. 9. López-Moreno M, López MTI, Miguel M, Garcés-Rimón M. Physical and Psychological Effects Related to Food Habits and Lifestyle Changes Derived from Covid-19 Home Confinement in the Spanish Population. Nutrients. 2020;12(11):3445. doi: 10.3390/nu12113445. López-Moreno M López MTI Miguel M Garcés-Rimón M Physical and Psychological Effects Related to Food Habits and Lifestyle Changes Derived from Covid-19 Home Confinement in the Spanish Population Nutrients 2020 12 11 3445 10.3390/nu12113445. 10. Lu R, Zhao X, Li J, Niu P, Yang B, Wu H, et al. Genomic Characterisation and Epidemiology of 2019 Novel Coronavirus: Implications for Virus Origins and Receptor Binding. Lancet. 2020;395(10224):565-74. doi: 10.1016/S0140-6736(20)30251-8. Lu R Zhao X Li J Niu P Yang B Wu H et al Genomic Characterisation and Epidemiology of 2019 Novel Coronavirus: Implications for Virus Origins and Receptor Binding Lancet 2020 395 10224 565 574 10.1016/S0140-6736(20)30251-8. 11. Sosa MAM, Valdés RAÁ, Molina FJR. COVID-19: Deadly Disease for Patients with Chronic Non-Communicable Diseases. Rev Cient Estud Cienfuegos. 2020;3(2):64-70. Sosa MAM Valdés RAÁ Molina FJR COVID-19: Deadly Disease for Patients with Chronic Non-Communicable Diseases Rev Cient Estud Cienfuegos 2020 3 2 64 70 12. Brandão I, Martins MJ, Monteiro R. Metabolically Healthy Obesity-Heterogeneity in Definitions and Unconventional Factors. Metabolites. 2020;10(2):48. doi: 10.3390/metabo10020048. Brandão I Martins MJ Monteiro R Metabolically Healthy Obesity-Heterogeneity in Definitions and Unconventional Factors Metabolites 2020 10 2 48 10.3390/metabo10020048. 13. Lin H, Zhang L, Zheng R, Zheng Y. The Prevalence, Metabolic Risk and Effects of Lifestyle Intervention for Metabolically Healthy Obesity: A Systematic Review and Meta-Analysis: A PRISMA-Compliant Article. Medicine. 2017;96(47):e8838. doi: 10.1097/MD.0000000000008838. Lin H Zhang L Zheng R Zheng Y The Prevalence, Metabolic Risk and Effects of Lifestyle Intervention for Metabolically Healthy Obesity: A Systematic Review and Meta-Analysis: A PRISMA-Compliant Article Medicine 2017 96 47 e8838 10.1097/MD.0000000000008838. 14. Luiten CM, Steenhuis IH, Eyles H, Ni Mhurchu C, Waterlander WE. Ultra-Processed Foods Have the Worst Nutrient Profile, Yet They are the Most Available Packaged Products in a Sample of New Zealand Supermarkets--CORRIGENDUM. Public Health Nutr. 2016;19(3):539. doi: 10.1017/S1368980015002840. Luiten CM Steenhuis IH Eyles H Ni Mhurchu C Waterlander WE Ultra-Processed Foods Have the Worst Nutrient Profile, Yet They are the Most Available Packaged Products in a Sample of New Zealand Supermarkets--CORRIGENDUM Public Health Nutr 2016 19 3 539 10.1017/S1368980015002840. 15. Expert Panel on Detection, Evaluation, Treatment of High Blood Cholesterol in Adults. Executive Summary of The Third Report of The National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, And Treatment of High Blood Cholesterol In Adults (Adult Treatment Panel III). JAMA. 2001;285(19):2486-97. doi: 10.1001/jama.285.19.2486. Expert Panel on Detection, Evaluation, Treatment of High Blood Cholesterol in Adults Executive Summary of The Third Report of The National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, And Treatment of High Blood Cholesterol In Adults (Adult Treatment Panel III) JAMA 2001 285 19 2486 2497 10.1001/jama.285.19.2486. 16. Matsudo S, Araújo T, Matsudo V, Andrade D, Andrade E, Oliveira LC, et al. Questionário Internacional de Atividade Física (IPAQ): Estudo de Validade e Reprodutibilidade no Brasil. Rev Bras Ativ Fís Saúde. 2012;6(2):5-18. Matsudo S Araújo T Matsudo V Andrade D Andrade E Oliveira LC et al Questionário Internacional de Atividade Física (IPAQ): Estudo de Validade e Reprodutibilidade no Brasil Rev Bras Ativ Fís Saúde 2012 6 2 5 18 17. Friedewald WT, Levy RI, Fredrickson DS. Estimation of the Concentration of Low-Density Lipoprotein Cholesterol in Plasma, without Use of the Preparative Ultracentrifuge. Clin Chem. 1972;18(6):499-502. Friedewald WT Levy RI Fredrickson DS Estimation of the Concentration of Low-Density Lipoprotein Cholesterol in Plasma, without Use of the Preparative Ultracentrifuge Clin Chem 1972 18 6 499 502 18. Institute of Medicine. Dietary Reference Intakes: Applications in Dietary Assessment. Washington: Institute of Medicine; 2000. Institute of Medicine Dietary Reference Intakes: Applications in Dietary Assessment Washington Institute of Medicine 2000 19. Institute of Medicine of the National Academies. Dietary Reference Intakes for Energy, Carbohydrates, Protein, Protein, Protein, and Amino Acids (Macronutrients). Washington: The National Academies Press; 2005. Institute of Medicine of the National Academies Dietary Reference Intakes for Energy, Carbohydrates, Protein, Protein, Protein, and Amino Acids (Macronutrients) Washington The National Academies Press 2005 20. Institute of Medicine. Dietary Reference Intakes: applications in dietary assessment. Washington: Institute of Medicine; 2000. Institute of Medicine Dietary Reference Intakes: applications in dietary assessment Washington Institute of Medicine 2000 21. Monteiro CA, Cannon G, Levy RB, Moubarac JC, Louzada ML, Rauber F, et al. Ultra-Processed Foods: What they are and How to Identify them. Public Health Nutr. 2019;22(5):936-41. doi: 10.1017/S1368980018003762. Monteiro CA Cannon G Levy RB Moubarac JC Louzada ML Rauber F et al Ultra-Processed Foods: What they are and How to Identify them Public Health Nutr 2019 22 5 936 941 10.1017/S1368980018003762. 22. Centers for Disease Control and Prevention. National Health and Nutrition Examination Survey (NHANES). Anthropometry Procedures Manual. Atlanta: Centers for Disease Control and Prevention; 2007. Centers for Disease Control and Prevention National Health and Nutrition Examination Survey (NHANES) Anthropometry Procedures Manual Atlanta Centers for Disease Control and Prevention 2007 23. Rio de Janeiro. Corregedoria Geral de Justiça. Portal Extrajudicial. Consulta de Nascimento e Óbitos [Internet]. Rio de Janeiro: Corregedoria Geral de Justiça; 2021 [cited 2021 Sep 30]. Available from: http://www4.tjrj.jus.br/SEIDEWEB/default.aspx. Rio de Janeiro Corregedoria Geral de Justiça Portal Extrajudicial Consulta de Nascimento e Óbitos Internet Rio de Janeiro Corregedoria Geral de Justiça 2021 cited 2021 Sep 30 http://www4.tjrj.jus.br/SEIDEWEB/default.aspx 24. Brasil. Ministério da Saúde. DATASUS; Sistema de Informação sobre Mortalidade [Internet]. Brasília: Ministério da Saúde; 2021 [cited 2021 Out 12]. Available from: https://opendatasus.saude.gov.br/dataset/sistema-de-informacao-sobre-mortalidade. Brasil Ministério da Saúde DATASUS; Sistema de Informação sobre Mortalidade Internet Brasília Ministério da Saúde 2021 cited 2021 Out 12 https://opendatasus.saude.gov.br/dataset/sistema-de-informacao-sobre-mortalidade 25. Fernández-Niño JA, Guerra-Gómez JA, Idrovo AJ. Multimorbidity Patterns among COVID-19 Deaths: Proposal for the Construction of Etiological Models. Rev Panam Salud Publica. 2020;44:e166. doi: 10.26633/RPSP.2020.166. Fernández-Niño JA Guerra-Gómez JA Idrovo AJ Multimorbidity Patterns among COVID-19 Deaths: Proposal for the Construction of Etiological Models Rev Panam Salud Publica 2020 44 e166 10.26633/RPSP.2020.166. 26. Malta DC, Szwarcwald CL, Barros MBA, Gomes CS, Machado ÍE, Souza PRB Jr, et al. The COVID-19 Pandemic and Changes in Adult Brazilian Lifestyles: A Cross-Sectional Study, 2020. Epidemiol Serv Saude. 2020;29(4):e2020407. doi: 10.1590/S1679-49742020000400026. Malta DC Szwarcwald CL Barros MBA Gomes CS Machado ÍE Souza PRB Jr et al The COVID-19 Pandemic and Changes in Adult Brazilian Lifestyles: A Cross-Sectional Study, 2020 Epidemiol Serv Saude 2020 29 4 e2020407 10.1590/S1679-49742020000400026. 27. Lamounier AM, Frões BTG, Barbosa LMR, Oliveira LF, Torres MF, Macedo MS, et al. Consumo de Alimentos Ultraprocessados em Profissionais de Saúde Durante o COVID-19. Biol & Saúde. 2021;11(37):62-70. doi: 10.25242/8868113720212291. Lamounier AM Frões BTG Barbosa LMR Oliveira LF Torres MF Macedo MS et al Consumo de Alimentos Ultraprocessados em Profissionais de Saúde Durante o COVID-19 Biol & Saúde 2021 11 37 62 70 10.25242/8868113720212291. 28. Barbosa TP, Costa FBP, Ramos ACV, Berra TZ, Arroyo LH, Alves YM, et al. COVID-19 Morbidity and Mortality Associated with chronic Disorders, Healthcare Services, and Inequity: Evidence for a Syndemic. Rev Panam Salud Publica. 2022;46:e6. doi: 10.26633/RPSP.2022.6. Barbosa TP Costa FBP Ramos ACV Berra TZ Arroyo LH Alves YM et al COVID-19 Morbidity and Mortality Associated with chronic Disorders, Healthcare Services, and Inequity: Evidence for a Syndemic Rev Panam Salud Publica 2022 46 e6 10.26633/RPSP.2022.6. 29. Santana JDM, Milagres MP, Santos CS, Brazil JM, Lima ER, Pereira M. Dietary Intake of University Students During COVID-19 Social Distancing in the Northeast of Brazil and Associated Factors. Appetite. 2021;162:105172. doi: 10.1016/j.appet.2021.105172. Santana JDM Milagres MP Santos CS Brazil JM Lima ER Pereira M Dietary Intake of University Students During COVID-19 Social Distancing in the Northeast of Brazil and Associated Factors Appetite 2021 162 105172 10.1016/j.appet.2021.105172. 30. Cecchetto C, Aiello M, Gentili C, Ionta S, Osimo SA. Increased Emotional Eating During COVID-19 Associated with Lockdown, Psychological and Social Distress. Appetite. 2021;160:105122. doi: 10.1016/j.appet.2021.105122. Cecchetto C Aiello M Gentili C Ionta S Osimo SA Increased Emotional Eating During COVID-19 Associated with Lockdown, Psychological and Social Distress Appetite 2021 160 105122 10.1016/j.appet.2021.105122. 31. McAtamney K, Mantzios M, Egan H, Wallis DJ. Emotional Eating During COVID-19 in the United Kingdom: Exploring the Roles of Alexithymia and Emotion Dysregulation. Appetite. 2021;161:105120. doi: 10.1016/j.appet.2021.105120. McAtamney K Mantzios M Egan H Wallis DJ Emotional Eating During COVID-19 in the United Kingdom: Exploring the Roles of Alexithymia and Emotion Dysregulation Appetite 2021 161 105120 10.1016/j.appet.2021.105120. 32. Campagnaro R, Collet GO, Andrade MP, Salles JPDSL, Fracasso MLC, Scheffel DLS, et al. COVID-19 Pandemic and Pediatric Dentistry: Fear, Eating Habits and Parent's Oral Health Perceptions. Child Youth Serv Rev. 2020;118:105469. doi: 10.1016/j.childyouth.2020.105469. Campagnaro R Collet GO Andrade MP Salles JPDSL Fracasso MLC Scheffel DLS et al COVID-19 Pandemic and Pediatric Dentistry: Fear, Eating Habits and Parent's Oral Health Perceptions Child Youth Serv Rev 2020 118 105469 10.1016/j.childyouth.2020.105469. 33. Juul F, Vaidean G, Lin Y, Deierlein AL, Parekh N. Ultra-Processed Foods and Incident Cardiovascular Disease in the Framingham Offspring Study. J Am Coll Cardiol. 2021;77(12):1520-31. doi: 10.1016/j.jacc.2021.01.047. Juul F Vaidean G Lin Y Deierlein AL Parekh N Ultra-Processed Foods and Incident Cardiovascular Disease in the Framingham Offspring Study J Am Coll Cardiol 2021 77 12 1520 1531 10.1016/j.jacc.2021.01.047. 34. Núcleo de Pesquisas Epidemiológicas em Nutrição e Saúde. Dialogue on Ultra-Processed Food Produtcts: Solutions for Healthy and Sustainable Food Systems. São Paulo: Universidade de São Paulo; 2021. Núcleo de Pesquisas Epidemiológicas em Nutrição e Saúde Dialogue on Ultra-Processed Food Produtcts: Solutions for Healthy and Sustainable Food Systems São Paulo Universidade de São Paulo 2021

Study Association

This article is part of the thesis of master submitted by Mariana Barbosa Lopes, from Universidade Federal do Rio de Janeiro.

Ethics Approval and Consent to Participate

This study was approved by the Ethics Committee of the Hospital Universitário Clementino Fraga Filho/UFRJ under the protocol number 89033118.1.00005257. All the procedures in this study were in accordance with the 1975 Helsinki Declaration, updated in 2013. Informed consent was obtained from all participants included in the study.

Sources of Funding: There were no external funding sources for this study.