Cardiology Research and Practice
Volume 2018 (2018), Article ID 4585412, 11 pages
https://doi.org/10.1155/2018/4585412

Research Article

“Traditional” and “Healthy” Dietary Patterns Are Associated with Low Cardiometabolic Risk in Brazilian Subjects

Brenda Kelly Souza Silveira,¹ Juliana Farias de Novaes,² Nínive de Almeida Reis,¹ Larissa Pereira Lourenço,³ Ana Helena Moretto Capobiango,³ Sarah Aparecida Vieira,⁴ and Helen Hermana Miranda Hermsdorff²

¹MSc, Department of Nutrition and Health, Universidade Federal de Viçosa, Viçosa, Minas Gerais, Brazil
²PhD, Department of Nutrition and Health, Universidade Federal de Viçosa, Viçosa, Minas Gerais, Brazil
³Undergraduate Student, Department of Nutrition and Health, Universidade Federal de Viçosa, Viçosa, Minas Gerais, Brazil
⁴PhD, Department of Integrated Education in Health, Universidade Federal do Espírito Santo, Espírito Santo, Brazil

Correspondence should be addressed to Helen Hermana Miranda Hermsdorff; helenhermana@ufv.br

Received 14 May 2018; Revised 28 September 2018; Accepted 23 October 2018; Published 19 November 2018

Academic Editor: Terrence D. Ruddy

Copyright © 2018 Brenda Kelly Souza Silveira et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

This study aimed at determining the dietary patterns and investigating their association with cardiometabolic risk markers in a brazilian population at risk. This transversal study was carried out with data of 265 patients (n = 123 M/172 W, age 42 ± 16 years) of the Cardiovascular Health Care Program—PROCARDIO-UFV, Brazil—who had their first appointment between 2012 and 2017. A 24-hour recall was applied. The dietary patterns were determined by Principal Component Analysis. Anthropometric, clinical-metabolic, sociodemographic, and lifestyle data were collected through medical record analysis. Five patterns were identified: “Traditional”, “Caloric”, “Unhealthy”, “Healthy,” and “Healthy Snacks”. In bivariate analysis, the “Healthy” pattern was negatively associated with WC (waist circunference), BMI (body mass index), WHR (waist-to-hip ratio), SBP (systolic blood pressure), fasting glucose, TG/HDL, LDL/HDL, and TG/HDL values and positively to HDL. The “Traditional” pattern was positively associated with adiposity indicators (WC, BMI, and WHR) and negatively associated with body fat, TyG (triglyceride-glucose index), HDL, and LDL (). However, in adjusted models of Poisson regression, individuals with positive factor score (higher adherence) in the “Traditional” and “Healthy” patterns had less occurrence of abdominal obesity (PR 0.85; 95% CI 0.74–0.99/PR 0.88; 95% CI 0.02–0.76), as well as dyslipidemia (PR 0.06; 95% CI 0.02–0.51/PR 0.03; 95% CI 0.01–0.27), diabetes (PR 0.05; 95% CI 0.01–0.45/PR 0.02; 95% CI 0.01–021), and hypertension (PR 0.06; 95% CI 0.02–0.50/PR 0.02; 95% CI 0.01–0.21). A greater adherence to the “Healthy” pattern was associated with lower values to cardiometabolic risk markers and less occurrence of chronic diseases, while the “Traditional” pattern presented contradictory results.

1. Introduction

Cardiovascular diseases (CVD) and their complications are the main causes of increased mortality, accounting for 31% of deaths [1, 2]. Although genetic factors may contribute to their development, lifestyle factors, such as sedentary lifestyle, alcohol intake, and unhealthy eating habits, are the main determinants for CVD development and progression [3]. Several researchers have investigated nutrients and bioactive compounds capable of modulating inflammation, oxidative stress, and other mechanisms responsible for CVD development [4–6]. However, in recent decades, the scientific literature has also analyzed dietary patterns and not just the effect of a single nutrient on human health [7–9], because they are ingested simultaneously, acting synergistically in the body through complementary or antagonistic mechanisms. In this sense, the cluster analysis techniques and the principal component analysis or PCA (factorial) are the statistical methods more frequently used when determining dietary patterns [10, 11]. PCA has been frequently used in nutritional epidemiology confirming or identifying a new pattern by assembling food groups with few components and minimal loss of the original information from the Food Survey [12]. In turn, dietary patterns with high industrialized food which are predominantly high in sodium, trans fats, and sugar have been designated to increase the risk of chronic diseases development and death [13, 14], while patterns with predominant in natura foods are associated to better health [15, 16]. Although several studies have described the dietary patterns in different populations [9, 14, 17], few have investigated their association with multiple risk factors for CVD. To our knowledge, no Brazilian study has investigated the association between dietary patterns and cardiometabolic risk markers in population groups at risk for CVD. Therefore, the objective of this study is to determine the dietary patterns in a population receiving nutritional accompaniment because at least one risk factor for CVD is present and investigate the potential association of these patterns with cardiometabolic risk markers.

2. Methods

2.1. Subjects

This transversal study was carried out with 295 individuals (172 women and 123 men), adults and elders (42 ± 16 years), and assisted by the Cardiovascular Health Care program (PROCARDIO-UFV) of the Universidade Federal de Viçosa—UFV (Brazil). This program performs continuous nutritional intervention in the university community and is registered in the Brazilian Registry of Clinical Trials ReBEC identifier number RBR-5N4Y2G. The program’s inclusion criteria are as follows: patients of both genders, who had their first appointment between March 2012 and July 2017, age ≥20 years, being an UFV employee/employee’s spouse or offspring or student, having been diagnosed with cardiovascular disease or occurrence of cardiometabolic risk factor, such as overweight (body mass index ≥25 kg/m²), hypertriglyceridemia (≥150 mg/dL), and hypercholesterolemia (≥200 mg/dL), low HDL (men <40 mg/dL and women <50 mg/dL), blood pressure ≥130/≥ 85 mmHg or systemic arterial hypertension diagnosis (systolic blood pressure ≥140 and/or diastolic blood pressure ≥90 mmHg), fasting blood glucose ≥100 mg/dL or diagnosis of diabetes mellitus (fasting blood glucose ≥126 mg/dL), and/or medical referral. The programme methodology has already been previously described [18, 19]. We excluded five patients who did not complete the interview and six others who underestimated or overestimated food consumption as described in the next section. Our study was approved by the Ethics Committee on research with human beings from UFV (of. Ref. no. 066/2012/CEPH), according to the resolution 466/2012 of the National Health Council. All participants in this study read and signed the term of free and informed consent in accordance to the principles of the Helsinki declaration.

2.2. Food Consumption

Patients underwent a 24-hour recall (24HR), which, according to Willett 1998 [20], may be sufficient to estimate food and nutrients intake in a population, provided that the sample has sufficient size. To guarantee data collection quality, we adopted the “multiple-pass” technique [21] and used photographic albums of presets and standard utensils for measures performed at home. All interviewers (nutritionists and undergraduation students of nutrition) were trained during four months and supervised during the first interviews.

We excluded five patients who did not complete the interview and six others who underestimated (<500 kcal/day) or overestimated (>4000 kcal/day) food consumption [20]. For PCA, food recorded in milliliters/day (mL/d) was converted to grams/day (g/d) according to the Density Database Table, Version 2.0 [22]. A total of 217 different foods were reported in 24HR and were collapsed into 20 food groups according to chemical similarity, beginning with those consumed by less than 5% of the sample [11, 23].

2.3. Anthropometry

Weight, height, and waist circumference (WC) were measured according to the protocol standardized by PROCARDIO-UFV, previously described [24]. The body weight was measured in an electronic digital scale (Toledo 2098PP, São Bernardo do Campo, Brazil) with a maximum capacity of 200 kg and a precision of 50 g. The height was determined in a stadiometer (Stanley, CMS, England), with a maximum extension of 2 m and precision of 0.5 mm. The WC was measured on top of the umbilical scar. The body mass index (BMI) was calculated and classified. Overweight and obesity were considered at BMI ≥25.0 kg/m² and BMI ≥ 30.0 kg/m² [25] for adults and BMI ≥28.0 kg/m² and ≥30.0 kg/m² for elders, respectively [26]. Abdominal obesity was accounted for when WC ≥ 90 and ≥80 cm for men and women, respectively [27].

Waist-to-height ratio (WHtR) and waist-to-hip ratio (WHR) were calculated, and an increased cardiometabolic risk was considered when WHtR >0.5 [28] and WHR >1.0 for men and >0.85 for women [25].

Body fat (BF) was estimated through horizontal tetrapolar electric bioimpedance (Biodynamics® 310 model, Washington, USA), according to the protocol proposed by Lukaski et al. [29], The cut-off points for BF excess values were >20% for men and >30% for women [30].

2.4. Cardiometabolic Risk Markers

A qualified professional collected blood after 12 hours with disposable material and venipuncture. The enzymatic colorimetric method was used to analyze serum concentrations of glucose, HDL and LDL cholesterol, triglycerides (TG), and uric acid, while the ultrasensitive immunoturbidimetry method assessed serum concentration of ultrasensitive C-reactive protein (CRP). The CT/HDL, TG/HDL, and LDL/HDL ratios were calculated as well as the triglyceride-glucose index (TyG) which was calculated according to the formula Ln (TG (mg/dL) × fasting blood glucose (mg/dL)/2) [31]. Increased values were defined as fasting glucose ≥100 mg/dL, uric acid ≥6 mg/dL, CRP ≥ 3 mg/dL [32], and LDL/HDL ratio ≥3.3 [33].

Blood pressure was measured using a mechanical mercury sphygmomanometer (Missouri®, São Paulo, Brazil) with approximately 02 mmHg, according to the technique described in the VI Brazilian Hypertension Guidelines [34]. In addition, the participants were the ones to report medical diagnosis of diabetes, hypertension, and dyslipidemias and the use of medications.

2.5. Sociodemographic and Lifestyle Data

During an interview, participants reported age, sex, schooling, income (in minimum wages), marital status (single, married, stable union, divorced, or widowed), the type of link with UFV (employee, student or relative), smoking habit (smokers, ex-smokers or nonsmokers), alcoholism (do not drink, drink sometimes, drink daily, or ex-alcoholic), and regular practice of physical activity (>150 min/week) (yes or no).

2.6. Statistical Analysis

Exploratory factor analysis was performed using the principal component analysis (PCA). The Kaiser–Meyer–Olkin measurement of sampling adequacy (KMO) and the Bartlett test of sphericity (BTS) were estimated and considered appropriate if > 0.6 and <0.05, respectively [35, 36]. The communalities (h²) were calculated and an anti-image model was inspected to verify the adequacy of each variable to the PCA test, where the KMO value was presented in the diagonal of this matrix, being higher than 0.5 for all variables [37]. The orthogonal varimax rotation was performed to make the values interpretable.

Factor retention was based on the Kaiser criteria (eigenvalue >1.0) and the inflection point of the eigenvalues from the Cattell scree test (screeplot) [38], suggesting the retention of 10 and 7 factors, respectively. For the final decision, we considered the formation of interpretable patterns, and we chose the criterion of the Cattel chart with the exclusion of two factors [39, 40]. Food groups with factor score >0.25 were considered as nonsignificant in the pattern [41]. When a food group saturated with positive score >0.25 in two patterns, the one with the highest score prevailed. When a food group saturated with opposing charges (positive and negative) in two patterns, it was maintained in both.

The patterns were named according to the food items included and the nomenclature adopted in other studies [8, 23] to facilitate data comparison. Finally, the factor scores for each dietary pattern were calculated for each participant. A positive factor score indicates a high intake of foods within the respective pattern, while a negative factor score indicates a low intake.

The characterization variables of the sample were described by means of frequency distribution measures. The normality of the data was evaluated by the Kolmogorov–Smirnov test. Student’s t test was used to compare the mean scores in the dietary patterns according to self-reported diseases. In the bivariate analysis, the regression coefficient and the confidence interval were estimated through simple linear regression for analyzing potential association of cardiometabolic risk factors (dependent variables) and dietary patterns (independent variables). These analyses were performed in the Statistical Package for Social Science (SPSS® 24.0, Chicago, IL, USA, 2016). Poisson regression models were used to evaluate the association between cardiometabolic risk factors (dependent variables) and positive factor score in dietary patterns (independent variable). This analysis was performed in STATA software, version 13.0. A significance level of 5% was considered for all tests.

Statistical power was calculated in the OpenEpi software online version 3.01 [42], with a 95% confidence interval. Two groups were considered for this calculation: exposed (positive score in the “Traditional” pattern) and not exposed (positive score in the “Healthy” pattern) and the prevalence of overweight, dyslipidemia, hypertension, and diabetes. The power of the tests was, on average, 87.1%.

3. Results

This study included 265 subjects with cardiometabolic risk. The sociodemographic and clinical characteristics of the sample are presented in Table 1. A considerable prevalence of chronic diseases is observed as expected in the study sample.

Table 1: Sociodemographic, lifestyle, and clinical characteristics in cardiometabolic risk subjects (n = 295) in Brazil, 2017.

Variables

%

Sex

Female
172
58.3

Age (years)

<30
108
36.6

30–60
146
49.5

6–84
41
13.9

Education

Primary or secondary (complete or incomplete)
127
39.6

College (complete or incomplete)
168
60.4

Family Income

Until 4 minimum wages
209
67.4

>4 minimum wages
86
32.6

Employment at UFV

Employee or relative
177
59.7

Student
118
40.3

Smoking

Smoker or ex-smoker
93
32.0

Never smoked
198
68.0

Physical activity (>150 minutes per week)

Yes
156
52.9

Alcohol intake

Do not drink
114
39.9

Drink eventually
165
57.7

Drink daily
6
2.1

Ex-alcoholic
1
0.3

BMI (kg/m²)

Overweight
209
70.8

Self-report of medical diagnosis of diseases

Diabetes
56
19.0

Dyslipidemias
233
79.3

Hypertension
117
39.6

Use of medicines

Oral hypoglycemic or insulin
53
17.9

Statins or fibrates
106
35.9

Antihypertensives
117
39.6

UFV = Universidade Federal de Viçosa; BMI= body mass index. n = 291. n = 286.

Regarding PCA, the sample was adequate according to the KMO and BTS tests (KMO = 0.64 and BTS <0.001). The food groups used for analysis are described in Table 2.

Table 2: Food groups based on dietary patterns of individuals with cardiometabolic risk according to chemical and botanical composition similarities, Brazil, 2017.

Food/group
Foods found within the food record

1. Rice and tubers
White rice, baked or nonfried potatoes (all species of potatoes including Arracacia xanthorrhiza and sweet potato), yams, cassava, corn.

2. Beans
Beans (brown bean, red bean, black bean, or white bean), lentil, chickpeas.

3. Vegetable oils
Soy oil, olive oil (virgin or extra-virgin).

4. Leafy vegetable
Watercress, lettuce, green onion, cabbage, spinach, mint, basil, mustard, arugula, parsley.

5. Nonleafy vegetables
Pumpkin, zucchini, leek, eggplant, beet, broccoli, onion, carrot, chayote, cauliflower, eggplant, peppers (green, red, or yellow), palm heart, cucumber, okra, radish, cabbage, tomato, green beans.

6. Whole grain and nuts
Brown rice, oats, flaxseed, sesame seed, quinoa, almonds, peanuts, pistachios, cashews, and other nuts.

7. Fruits and natural juice
Fruits: avocado, pineapple, Barbados cherry, plum, prune, banana, khaki, coconut, guava, kiwi, lemon, orange, apple, papaya, mango, passion fruit, watermelon, strawberry, nectarine, pear, peach, tangerine, grape. Juices: Barbados cherry fruit, pineapple, guava, passion fruit, and grape plus coconut water.

8. Chicken salad sandwich
Sandwich (bread, salad, and a protein food source that is usually chicken)

9. Milk
Fluid or powdered milk (includes whole milk, half-creamed, or skimmed milk).

10. Dairy
Cheese: fresh cheese, half-cured fresh cheese, mozzarela, parmesan, provolone, curd, creamy cheese, ricotta and cottage. Beverages: fermented beverage, nonflavoured yogurt (whole or skimmed), fruit yogurt (whole or skimmed), dairy beverage (whole or skimmed), and chocolate milk∗.

11. Meat, fish, and eggs (cooked, baked, or grilled)
Grilled, roasted or cooked chicken, beef, or pork (all cuts), canned, cooked, grilled, or baked fish, tofu, and boiled or scrambled eggs.

12. Meat, offal, and eggs (fried)
Fried meat: beef, chicken, pork, fish. Offals (heart, gizzard, liver) of all species, regardless cooking technique, and fried eggs.

13. Processed meat
Bacon, hamburger steak, breaded, sausage.

14. Margarine/butter, sauces, mayonnaise
Margarine, butter, cream, pork lard, salad dressing, mayonnaise, processed tomato sauce.

15. Sweets and sugar
Candy, cappuccino, sugar added cocoa powder, chocolate bar (milk or dark), milk fudge, gelatin, jelly, ice cream, condensed milk, honey, peanut butter, popsicle, pudding.

16. Cookies, cakes, and breads
Cornstarch, cereal, cereal flour (Nestlé ™), cassava flour/starch, cornmeal, flour/wheat bran, noodles, cookies, cereal bar, biscuits (milk, cornstarch, flour, Sandwich cookie, waffer, or cream cracker), donuts, toasts, muffins, breads (white bread, loaf, hot dog bread, roll bread), bagel, Brazilian cheese bread, cakes and scones, granola (a mix made of whole grains, nuts, and dried fruit).

17. Fast Food e pastry
Fried or baked pastry (kibbeh, “Coxinha” (chopped fried chicken with dough), pie, sfiha, other salty snacks), pizza, hamburger

18. Soft beverages and artificial juices
Soft drinks, diet soft drinks, powdered juice, boxed juice, canned juice.

19. Alcoholic beverages
Beer, wine, “cachaça” (sugarcane hard liquor), vodka, rum.

20. Coffee and tea
Regular coffee, tea (mate or herbal).

Consumed by only three participants. Curd made from coagulated and pressed soy milk, consumed by only one participant.

Five dietary patterns were identified from the PCA test, which explained 39.7% of the dietary intake variance. The “Traditional” pattern, composed of rice and tubers, beans, vegetable oils, nonleafy vegetables, meats, fish, and eggs (grilled, cooked or roasted), explained 10.9% of the data variance. The second pattern named “Caloric” was composed of meat, offal and eggs (fried), processed meat, sweets and sugar, and soft beverages and artificial juices, explained 8.2% of the variance. The “Pastry” pattern was represented by fast food and pasta, with negative saturation for milk (whole or skimmed), sweets, and sugar accounting for 7.0% of the variance. In the “Healthy” pattern, whole grain food and nuts, milk, dairy, fruits, and natural juices were main groups. In addition, margarine/butter, sauces, and mayonnaise, as well as alcoholic beverages, coffee, and tea saturated with negative score, i.e., were inversely associated. This pattern explained 6.9% of the variance. The last pattern named “Healthy Snacks” was represented by leafy vegetables, chicken salad sandwich and presented negative saturation for fast food and pasta, explaining 6.7% of the dietary intake variance (Table 3).

Table 3: Dietary patterns and factorial score of food groups consumed by cardiometabolic risk subjects (n = 295) in Brazil, 2017.

Food groups
Dietary patterns

Traditional
Caloric
Pastry
Healthy
Healthy snacks
h²

Rice and tubers
0.666
0.106
−0.013
−0.121
0.001
0.514

Beans
0.703
0.006
−0.097
0.065
0.048
0.543

Vegetable oils
0.768
0.234
0.181
0.066
−0.048
0.698

Leafy vegetable
0.074
−0.124
−0.036
−0.096
0.623
0.611

Nonleafy vegetables
0.410
−0.297
0.044
0.032
0.201
0.384

Whole grain and nuts
−0.005
0.204
0.236
0.576
−0.006
0.545

Fruits and natural juice
0.079
−0.107
−0.064
0.384
0.293
0.667

Chicken salad Sandwich
−0.155
0.100
0.072
−0.005
0.594
0.627

Milk
−0.087
0.202
−0.619
0.258
−0.102
0.648

Dairy
−0.249
0.022
0.347
0.459
−0.208
0.678

Meat, fish, and eggs (cooked, baked, or grilled)
0.465
−0.279
0.010
0.038
−0.230
0.583

Meat, offal, and eggs (fried)
0.005
0.476
0.251
0.070
0.339
0.533

Processed meat
0.048
0.512
0.283
−0.199
0.068
0.716

Margarine/butter, sauces, mayonnaise
−0.191
−0.057
0.045
−0.261
0.021
0.844

Sweets and sugar
−0.054
0.611
−0.271
0.032
−0.086
0.681

Cookies, cakes, and breads
−0.258
0.034
−0.422
−0.177
−0.212
0.587

Fast Food pastry
−0.211
0.173
0.589
0.005
−0.282
0.646

Soft beverages and artificial juices
0.124
0.619
−0.060
0.101
−0.170
0.611

Alcoholic beverages
0.011
0.083
0.182
−0.416
0.034
0.436

Coffee and tea
0.070
0.058
0.046
−0.568
−0.126
0.799

Variance explained (%)
10.9
8.2
7.0
6.9
6.7
—

Extraction method: principal component analysis. Varimax rotation with Kaiser normalization. Bold values indicate factorial score ≥0.25.

The mean factor score of the “Healthy” pattern was higher among subjects with normal weight than those who were overweight (Figure 1).

Figure 1: Scores of the “Traditional” and “Healthy” dietary patterns (mean and 95% confidence interval), according to self-report of chronic diseases, Brazil, 2017. Student’s t test ( for all variables).

Moreover, in the bivariate linear regression, the factorial score of the “Traditional” pattern was positively associated with WC, BMI, WHR, SBP (systolic blood pressure), and fasting glucose values and negatively associated with BF%, TyG, HDL, and LDL. The factor score of the “Healthy” pattern was negatively associated with WC, BMI, WHR, SBP, fasting glucose, CT/HDL, LDL/HDL, and TG/HDL ratios and positively associated with HDL (Table 4).

Table 4: Simple linear regression for association of cardiometabolic risk factors (dependent variables) with dietary patterns (independent variables) in cardiometabolic risk subjects (n = 295) in Brazil, 2017.

Dietary patterns
Waist circumference (cm)
BMI (kg/m²)
Body fat (%)
Waist-to-hip ratio

β
CI 95%

β
CI 95%

β
CI 95%

β
CI 95%

Traditional
3.159
1.523; 4.795
<0.001
0.667
0.040; 1.294
0.037
−1.208
−2.209;−0.207
0.018
0.029
0.018; 0.039
<0.001

Caloric
0.072
−1.602; 1.746
0.933
−0.074
−0.706; 0.557
0.817
−0.170
−1.183; 0.842
0.740
−0.004
−0.015; 0.007
0.467

Pastry
0.686
−0.984; 2.357
0.419
0.137
−0.495; 0.768
0.671
0.753
−0.296; 1.802
0.158
0.004
−0.007; 0.015
0.438

Healthy
−2.709
−4.352;−1.065
0.001
−1.065
−1.685;−0.446
0.001
−0.974
−2.059; 0.112
0.079
−0.019
−0.030; −0.009
0.001

Healthy snacks
0.483
−1.189; 2.155
0.570
0.018
−0.614; 0.649
0.956
0.320
−0.688; 1.329
0.532
0.011
0.008; 0.022
0.050

SBP (mmHg)
Fasting glucose (mg/dL)
TyG
HDL (mg/dL)

β
CI 95%

β
CI 95%

β
CI 95%

β
CI 95%

Traditional
0.017
0.001; 0.033
0.033
0.047
0.016; 0.079
0.003
−0.072
−0.142;−0.001
0.046
−0.073
−0.107;−0.039
<0.001

Caloric
−0.004
−0.020; 0.011
0.587
−0.043
−0.075;−0.012
0.007
0.043
−0.024; 0.111
0.210
0.010
−0.024; 0.045
0.561

Pastry
0.001
−0.016; 0.017
0.941
0.005
−0.028; 0.038
0.769
0.036
−0.034; 0.106
0.315
0.010
−0.025; 0.045
0.569

Healthy
−0.030
−0.045; −0.014
<0.001
−0.051
−0.083;−0.019
0.002
−0.001
0.970;−0.072
0.970
0.058
0.023; 0.092
0.001

Healthy snacks
0.014
−0.001; 0.030
0.071
0.019
−0.013; 0.050
0.245
0.028
−0.040; 0.096
0.420
−0.011
−0.045; 0.024
0.547

LDL (mg/dL)
CT/HDL ratio
LDL/HDL ratio
TG/HDL ratio

β
CI 95%

β
CI 95%

β
CI 95%

β
CI 95%

Traditional
−0.074
−0.115;−0.032
0.001
0.016
−0.021; 0.053
0.385
−0.004
−0.057; 0.048
0.869
0.050
−0.036; 0.135
0.253

Caloric
0.012
−0.028; 0.052
0.566
0.001
−0.035; 0.037
0.958
−0.006
−0.055; 0.043
0.805
0.015
−0.066; 0.097
0.712

Pastry
0.019
−0.021; 0.059
0.356
0.008
−0.028; 0.044
0.659
0.002
−0.048; 0.051
0.949
0.011
−0.073; 0.095
0.795

Healthy
−0.019
−0.060; 0.023
0.374
−0.066
−0.102;−0.031
<0.001
−0.071
−0.121;−0.021
0.005
−0.127
−0.210;−0.044
0.003

Healthy snacks
−0.019
−0.058; 0.021
0.349
0.004
−0.032; 0.040
0.834
−0.012
−0.061; 0.036
0.622
0.060
−0.023; 0.143
0.154

β = standardized beta coefficient; IC = confidence interval; BMI = body mass index; SBP = systolic blood pressure; TyG = triglyceride-glucose index; HDL = high-density lipoprotein; LDL = low-density lipoprotein; TG = triglycerides. Values in bold indicate statistical significance ().

In the prevalence analysis, subjects with a positive factor score (greater adherence) in the “Traditional” and “Healthy” patterns had a lower occurrence of abdominal obesity, dyslipidemia, diabetes mellitus, and hypertension increased, increased WHR and WHtR (). Those with a positive factor score in the “Traditional” pattern also had lower occurrence of overweight and an increased LDL/HDL ratio (Table 5).

Table 5: Poisson regression for association of cardiometabolic risk factors (dependent variables) with positive factor score∗ of dietary patterns (independent variables) in cardiometabolic risk subjects (n = 295), Brazil, 2017.

Cardiometabolic risk factors
Traditional
Healthy

Adjusted model¹
Adjusted model¹

PR (CI 95%)

PR (CI 95%)

Overweight
0.85 (0.74–0.99)
0.043
0.88 (0.68–1.14)
0.363

Abdominal obesity
0.19 (0.03–0.96)
0.045
0.13 (0.02–0.76)
0.024

High WHR
0.05 (0.01–0.19)
<0.001
0.03 (0.00–0.22)
<0.001

High WHtR
0.20 (0.04–0.99)
0.045
0.14 (0.02–0.81)
0.028

Excessive body fat
0.68 (0.34–1.35)
0.274
0.71 (0.37–1.35)
0.304

High LDL/HDL ratio
0.86 (0.75–0.99)
0.041
1.46 (0.61–3.48)
0.387

High Uric acid
0.97 (0.57–1.63)
0.923
1.07 (0.68–1.69)
0.756

High CRP
1.16 (0.88–1.52)
0.278
0.91 (0.65–1.26)
0.595

Dyslipidemias
0.06 (0.02–0.51)
0.009
0.03 (0.01–0.27)
0.001

Diabetes
0.05 (0.01–0.45)
0.007
0.02 (0.01–0.18)
<0.001

Hypertension
0.06 (0.02–0.50)
0.009
0.02 (0.01–0.21)
0.001

∗Positive factor score = higher adherence to the dietary pattern. ¹ Model adjusted for age, education, physical activity, and alcoholism. PR = prevalence ratio; 95% CI = confidence interval 95%.

4. Discussion

This cross-sectional study, conducted with adults presenting cardiometabolic risk, identified five eating patterns (“Traditional”, “Caloric”, “Healthy,” and “Healthy Snacks”). These patterns are similar to those described in previous publications that have also used PCA and commonly interpret a healthy pattern, an unhealthy pattern, and an intermediate pattern [7, 43, 44]. The “Traditional” pattern is frequently present in Brazilian studies conducted with different age groups [17,45–47]. This pattern consists of foods that characterize the Brazilian eating habits (rice and tubers, beans, meats, and vegetable oils) and has received several denominations, such as “Brazilian,” “Traditional,” or “Prudent” [44, 45].

In this study, diabetics and hypertensives presented a higher mean score in the “Traditional” pattern. In the bivariate analysis, this pattern was positively associated with indicators of adiposity, blood pressure, fasting glycemia, and negative percentage of body fat, TyG, HDL, and LDL. However, in a model adjusted for confounding factors, subjects with higher adherence in this pattern presented low overweight occurrence, increased LDL/HDL ratio, and diagnosis of dyslipidemia, diabetes, and hypertension. In the literature, the associations between the “Traditional” pattern and the cardiometabolic risk are controversial. In this sense, a study carried out with Brazilian adult women identified a similar pattern and considered the “Traditional” pattern a risk, denominating it as a “cost risk 1 dietary pattern”, because this pattern consists of low cost food [43]. In another Brazilian study, the “Traditional” pattern was positively associated with glycemia and BMI and negatively associated with TG and WHR [48]. Among Europeans, the “Traditional” pattern (potatoes, sautéed vegetables, oils and margarine, red and processed meat, coffee, and bread) was associated with a higher risk of CVD [7]. Other researchers have observed the protective effect of this pattern in several age groups [17, 23, 49], including less occurrence of obesity and risk behaviors for NCD. Foods in the “Traditional” pattern, such as vegetables, rice, beans, and eggs, are considered healthy, and the Food Guide for the Brazilian Population recommends them to be consumed daily in the context of a balanced and diversified diet [50]. However, some of the foods that make up this pattern, such as rice, tubers, and vegetable oils, are of higher caloric density. The negative relationship between the “Traditional” pattern and health status may be influenced by the excessive addition of oil and salt during the meal preparation, which can contribute to positive caloric balance, and, consequently, adiposity increase and dyslipidemias development [48]. In turn, a greater adherence to the “Healthy” pattern, characterized by a greater intake of whole grain foods, nuts, fruits and natural juices, milk and dairy products, and a low consumption of margarine, butter, oily sauces, alcohol, coffee, and tea, was associated negatively to the adiposity indicators, blood pressure, and fasting blood glucose. In a model adjusted for confounding factors, the “Healthy” pattern was associated to low occurrence of abdominal obesity, dyslipidemia, diabetes, and hypertension. This pattern resembles other cardioprotective patterns described in the literature and associated with low disease risk [15, 51, 52] and low mortality [53–55]. Therefore, the protective effect of this pattern is well established. In a cohort, the highest score in the “Prudent” pattern (wine, eggs, fruit, vegetables, fish, etc.), similar to our “Healthy” pattern, was associated with reduced CVD risk [7]. In a British elderly population, the “Mediterranean” pattern was associated with low mortality (reduction of 18% from highest to lowest tertile) [9]. Among elderly participants of the British Regional Heart Study, the second quartile of the “Prudent” pattern (fruits, vegetables, fish, legumes, rice, eggs, olive oil, etc.) was associated with low risk of CVD death [14]. The healthy patterns described in the literature are characterized by the ingestion of cardioprotective foods: vegetables, fruits, nuts, olive oil, and low ingestion of wine and saturated fats [54]. The ingestion of nuts and extra-virgin olive oil are related to a higher HDL serum concentration, a better control of lipemia, a lower risk of developing obesity, diabetes, and dyslipidemia [56, 57], and reduction of CVD deaths [57, 58]. Such benefits are attributed to the bioactive compounds present in these foods, such as polyphenols that have antioxidant effect and inhibit the oxidation of LDL. The main mechanism linked to CVD development is LDL oxidation caused by oxidative stress and associated with subclinical inflammation, endothelial dysfunction, and atherosclerosis [53, 59]. In this context, fruits and vegetables contribute to the reduction of these diseases, as they are sources of antioxidants and bioactive compounds that contribute to a better lipid profile and lower abdominal obesity [60, 61]. In addition to the intake of fruits, vegetables, olive oil, and nuts, cardioprotective diets are also characterized by the low intake of red meat and saturated fats, as these are associated with metabolic syndrome and risk factors for CVD [62, 63]. Nevertheless, white meat and fish have been related to protective food patterns [64]. The inverse association between the “Healthy” pattern with adiposity markers allows us to infer about the protective role of this pattern on CVD, considering that the increase of adiposity leads to a greater release of cytokines and inflammatory biomarkers that favor insulin resistance and atherosclerosis [65]. Moreover, this pattern resembles the standards considered effective in preventing and controlling CVD and its complications [66, 67].

The samples in our study were mainly of high level of education (60%) and mostly teachers and college students who spend a lot of time away from home, a factor that contributes to a greater consumption of fast food and processed foods [68, 69]. Although the increase in schooling is associated with increased income and access to food, food choices have multiple determinants [70, 71]. In this sense, a Brazilian study identified that subjects with a high level of education were divided into two groups: the one that had greater adherence to the “Healthy” pattern and the one that had greater adherence to the “High risk” pattern [43]. On the contrary, those with a low level of education had greater adherence to the “Pattern of risk and low cost”, similar to our “Traditional” pattern [43]. Therefore, increasing schooling contributes to access to food, but in itself does not ensure a better diet quality. Motivation, awareness, and other factors may be related. Finally, in relation to the method used for dietary pattern analysis, PCA has the advantage of being an empirical approach in the determination of dietary patterns, that is, no inferences are made about the composition of the patterns or their effects on health. Hence, this approach allows identifying specific characteristics of the alimentary habit of each population. However, food grouping and the choice of how many patterns will be retained occur subjectively, which may influence associations with assessed outcomes, as well as the constitution of patterns, and make it difficult to compare results. For this reason, this study was based on previous publications [8, 23] to carry out the grouping of foods. In view of the above, more studies are needed to investigate the relationship between the patterns and the cardiometabolic risk, in the context of the specificities of the food habit of each population.

5. Conclusion

A greater adherence to the “Healthy” pattern, similar to other cardioprotective patterns, was associated with the lower cardiometabolic risk outcome and less occurrence of chronic diseases, while the “Traditional” pattern presented contradictory results, being more studies needed to elucidate the relationship between “Traditional” Brazilian dietary pattern and the risk of chronic diseases, as well as the interference of sugars, oils, and salt in this relationship.

Data Availability

The PROCARDIO-UFV data used to support the findings of this study are restricted by the registration number 066/2012/CEPH in order to protect patient privacy. Data are available from the corresponding author upon request for researchers who meet the criteria for access to confidential data.

Conflicts of Interest

The authors have no conflicts of interest to declare.

Acknowledgments

Financial support was provided by the CAPES Foundation (Finance code 001), National Council for Scientific and Technological Development (CNPq/MS/SCTIE/DECIT/SAS/DAB/CGAN no. 408279/2017-6), and Foundation for Research Support of the State of Minas Gerais (FAPEMIG). HHM Hermsdorff is a CNPq Research Productivity fellow. We would like to thank all the professionals of the PROCARDIO-UFV for contributing in data collection and especially the volunteers for participating in the study. We would also like to thank CAPES (MEC/Brasil) for the master’s scholarship granted to BKS Silveira.

References

Organização Panamericana de Saúde, “Social determinants and risks for health, chronic noncommunicable diseases and mental health: cardiovascular diseases,” OPAS, Washington, DC, USA, 2016, http://www.paho.org.

World Health Organization, “Fact sheets: the top 10 causes of death,” World Health Organization, Geneva, Switzerland, 2017, http://www.who.int/mediacentre/factsheets/fs310/en/.

Sociedade Brasileira de Cardiologia, “South American Guidelines on cardiovascular prevention and rehabilitation,” Arquivos Brasileiros de Cardiologia, vol. 103, pp. 1–31, 2014.

P. W. Siri-tarino, Q. Sun, F. B. Hu, and R. M. Krauss, “Saturated fat, carbohydrate, and cardiovascular disease,” American Journal of Clinical Nutrition, vol. 91, no. 3, pp. 502–509, 2010.

D. M. Rocha, J. Bressan, and H. H. Hermsdorff, “The role of dietary fatty acid intake in inflammatory gene expression: a critical review,” Sao Paulo Medical Journal, vol. 135, no. 2, pp. 157–168, 2017.

L. Darghosian, M. Free, J. Li et al., “Effect of omega-three polyunsaturated fatty acids on inflammation, oxidative stress, and recurrence of atrial fibrillation,” American Journal of Cardiology, vol. 115, no. 2, pp. 196–201, 2015.

S. Biesbroek, A. D. L. Van Der, M. C. C. Brosens et al., “Identifying cardiovascular risk factor – related dietary patterns with reduced rank regression and random forest in the EPIC-NL cohort,” American Journal of Clinical Nutrition, vol. 102, no. 1, pp. 146–154, 2015.

M. T. A. Olinto, D. P. Gigante, B. Horta, V. Silveira, I. Oliveira, and W. Willett, “Major dietary patterns and cardiovascular risk factors among young Brazilian adults,” European Journal of Nutrition, vol. 51, no. 3, pp. 281–291, 2012.

M. Hamer, S. A. McNaughton, C. J. Bates, and G. D. Mishra, “Dietary patterns, assessed from a weighed food record, and survival among elderly participants from the United Kingdom,” European Journal of Clinical Nutrition, vol. 64, no. 8, pp. 853–861, 2010.

U. M. Devlin, B. A. McNulty, A. P. Nugent, and M. J. Gibney, “The use of cluster analysis to derive dietary patterns: methodological considerations, reproducibility, validity and the effect of energy mis-reporting,” Proceedings of the Nutrition Society, vol. 71, no. 4, pp. 599–609, 2012.

L. de O. Cardoso, M. S. Carvalho, O. G. Cruz et al., “Eating patterns in the Brazilian longitudinal study of adult health (ELSA-Brasil): an exploratory analysis,” Cadernos de Saúde Pública, vol. 32, no. 5, pp. 1–14, 2016.

I. T. Jolliffe and J. Cadima, “Principal component analysis: a review and recent developments,” Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol. 374, no. 2065, 2016.

M. Ozawa, M. Shipley, M. Kivimaki, A. Singh-Manoux, and E. J. Brunner, “Dietary pattern, inflammation and cognitive decline: the Whitehall II prospective cohort study,” Clinical Nutrition, vol. 36, pp. 1–7, 2016.

J. L. Atkins, P. H. Whincup, R. W. Morris, L. T. Lennon, O. Papacosta, and S. G. Wannamethee, “Dietary patterns and the risk of CVD and all-cause mortality in older British men,” British Journal of Nutrition, vol. 116, no. 7, pp. 1246–1255, 2016.

G. Viscogliosi, E. Cipriani, M. L. Liguori et al., “Mediterranean dietary pattern adherence: associations with prediabetes, metabolic syndrome, and related microinflammation,” Metabolic Syndrome and Related Disorders, vol. 11, no. 3, pp. 210–216, 2013.

E. Denova-Gutierrez, K. L. Tucker, M. Flores, S. Barquera, and J. Salmeron, “Dietary patterns are associated with predicted cardiovascular disease risk in an urban Mexican adult population,” Journal of Nutrition, vol. 146, no. 1, pp. 90–97, 2016.

M. Ferreira, A. Previdelli, T. Freitas, K. M. Marques, R. Goulart, and R. Aquino, “Dietary patterns and associated factors among the elderly,” Revista Brasileira de Geriatria e Gerontologia, vol. 20, no. 4, pp. 534–544, 2017.

J. S. Rodrigues, A. P. de Almeida, C. de O. B. Rosa, and H. H. M. Hermsdorff, “Are body fat and uric acid associated with cardiovascular risk scores? Cross-sectional analysis in the PROCARDIO-UFV trial,” International Journal of Cardiovascular Sciences, vol. 30, pp. 313–324, 2017.

A. P. de Almeida, D. M. U. P. Rocha, L. M. Mendonça, J. F. de Novaes, and H. H. M. Hermsdorff, “Carotenoid and polyphenol consumption in subjects with cardiometabolic risk,” Nutrición Clínica y Dietética Hospitalaria, vol. 36, pp. 138–145, 2016.

W. Willett, Nutritional Epidemiology, Oxford University, New York, NY, USA, 2nd edition, 1998.

J. M. Conway, L. A. Ingwersen, B. T. Vinyard, and A. J. Moshfegh, “Effectiveness of the USDA 5-step Multiple-Pass Method to assess food intake in obese and non-obese women,” American Journal of Clinical Nutrition, vol. 77, no. 5, pp. 71–78, 2003.

FAO/INFOODS, Density Database Version 2.0, Rome, Italy, Guidelines Guidelines for Converting Units, Denominators and Expressions, 2012.

D. B. Cunha, R. M. V. R. de Almeida, R. Sichieri, and R. A. Pereira, “Association of dietary patterns with BMI and waist circumference in a low-income neighbourhood in Brazil,” British Journal of Nutrition, vol. 104, no. 6, pp. 908–913, 2010.

H. A. D. Silva, J. C. C. Carraro, J. Bressan, and H. H. M. Hermsdorff, “Relation between uric acid and metabolic syndrome in subjects with cardiometabolic risk,” Einstein (São Paulo), vol. 13, no. 2, pp. 202–208, 2015.

World Health Organization, “Obesity: preventing and managing the global epidemic,” WHO Technical Report Series 894, World Health Organization, Geneva, Switzerland, 1998.

Organização Panamericana de Saúde, XXXVI Reunión del Comitê Asesor de Ivestigaciones en Salud – Encuestra Multicêntrica – Salud Beinestar y Envejecimeiento (SABE) en América Latina e el Caribe – Informe Preliminar, 2002.

K. G. M. M. Alberti, R. H. Eckel, S. M. Grundy et al., “Harmonizing the metabolic syndrome: a joint interim statement of the international diabetes federation task force on epidemiology and prevention,” Circulation, vol. 120, no. 16, pp. 1640–1645, 2009.

M. Ashwell and S. D. Hsieh, “Six reasons why the waist-to-height ratio is a rapid and effective global indicator for health risks of obesity and how its use could simplify the international public health message on obesity,” International Journal of Food Sciences and Nutrition, vol. 56, no. 5, pp. 303–307, 2005.

H. Lukaski, W. Bolonchuk, C. Hall, and W. Siders, “Validation of tetrapolar bioelectrical impedance method to assess human body composition,” Journal of Applied Physiology, vol. 60, no. 4, pp. 1327–1332, 1986.

G. Bray, C. Bouchard, and W. James, “Definitions and proposed current classifications of obesity,” in Handbook of Obesity, G. A. Bray, C. Bouchard, and W. P. T. James, Eds., pp. 31–40, Marcel Dekker, New York, NY, USA, 1998.

L. E. Simental-Mendía, M. Rodríguez-Morán, and F. Guerrero-Romero, “The product of fasting glucose and triglycerides as surrogate for identifying insulin resistance in apparently healthy subjects,” Metabolic Syndrome and Related Disorders, vol. 6, no. 4, pp. 299–304, 2008.

Sociedade Brasileira de Cardiologia., “V Diretriz Brasileira de Dislipidemias e Prevenção da Aterosclerose,” Arquivos Brasileiros de Cardiologia, vol. 101, pp. 1–36, 2013.

V. Hanak, J. Munoz, J. Teague, A. Stanley, and V. Bittner, “Accuracy of the triglyceride to high-density lipoprotein cholesterol ratio for prediction of the low-density lipoprotein phenotype B,” American Journal of Cardiology, vol. 94, no. 2, pp. 219–222, 2004.

Sociedade Brasileira de Cardiologia, “VI Diretrizes brasileiras de Hipertensão,” Arquivos Brasileiros de Cardiologia, vol. 95, pp. 1–51, 2010.

C. A. de Carvalho, P. C. de A. Fonsêca, L. N. Nobre, S. E. Priore, and C. C. Franceschini S do, “Metodologias de identificação de padrões alimentares a posteriori em crianças brasileiras: revisão sistemática,” Ciência and Saúde Coletiva, vol. 21, no. 1, pp. 143–154, 2016.

A. C. Moreira, “Comparação da Análise de Componentes Principais e da CATPCA na Avaliação da Satisfação do Passageiro de uma Transportadora Aérea,” Investigação Operacional, vol. 27, pp. 165–178, 2007.

A. G. Salvatti, M. A. M. S. Escrivão, J. A. de A. C. Taddei, and M. M. Bracco, “Padrões alimentares de adolescentes na cidade de São Paulo,” Revista de Nutrição, vol. 24, no. 5, pp. 703–713, 2011.

V. Pala, L. Lissner, A. Hebestreit et al., “Dietary patterns and longitudinal change in body mass in European children : a follow-up study on the IDEFICS multicenter cohort,” European Journal of Clinical Nutrition, vol. 67, no. 10, pp. 1042–1049, 2013.

M. A. De Castro, V. T. Baltar, S. S. D. C. Selem, D. M. L. Marchioni, and R. M. Fisberg, “Empirically derived dietary patterns: interpretability and construct validity according to different factor rotation methods,” Cadernos de Saúde Pública, vol. 31, no. 2, pp. 298–310, 2015.

F. Jannasch, F. Riordan, L. F. Andersen, and M. B. Schulze, “Exploratory dietary patterns : a systematic review of methods applied in pan-European studies and of validation studies,” British Journal of Nutrition, vol. 120, no. 6, pp. 601–611, 2018.

J. A. Grieger, J. Scott, and L. Cobiac, “Dietary patterns and breast-feeding in Australian children,” Public Health Nutrition, vol. 14, no. 11, pp. 1939–1947, 2011.

A. Dean, K. Sullivan, and M. Soe, “OpenEpi: open source epidemiologic statistics for public health,” 2013, http://www.openepi.com.

A. L. S. A. Alves, M. T. A. Olinto, J. S. D. da Costa, F. S. de Bairros, and M. A. A. Balbinotti, “Dietary patterns of adult women living in an urban area of Southern Brazil,” Revista de Saúde Pública, vol. 40, no. 5, pp. 865–873, 2006.

M. Hoffmann, K. G. Mendes, R. Canuto et al., “Dietary patterns in menopausal women receiving outpatient care in Southern Brazil,” Ciência and Saúde Coletiva, vol. 20, no. 5, pp. 1565–1574, 2015.

F. A. Massarani, D. B. Cunha, A. P. Muraro, B. da S. N. de Souza, R. Sichieri, and E. M. Yokoo, “Familial aggregation and dietary patterns in the Brazilian population,” Cadernos de Saúde Pública, vol. 31, no. 12, pp. 2535–2545, 2015.

R. Souza, S. Madruga, D. Gigante, I. Santos, A. Barros, and M. Assunção, “Dietary patterns and associated factors among children one to six years of age in a city in southern Brazil,” Cadernos de Saúde Pública, vol. 29, no. 12, pp. 1816–1828, 2013.

D. M. Marchioni, R. M. Claro, R. B. Levy, and C. A. Monteiro, “Patterns of food acquisition in Brazilian households and associated factors: a population-based survey,” Public Health Nutrition, vol. 14, no. 9, pp. 1586–1592, 2011.

A. I. C. P. Neumann, I. S. Martins, L. F. Marcopito, and E. A. C. Araujo, “Dietary patterns associated with risk factors for cardiovascular diseases among residents of a Brazilian city,” Revista Panamericana de Salud Pública, vol. 22, no. 5, pp. 329–339, 2007.

P. R. M. Rodrigues, R. A. Pereira, D. B. Cunha et al., “Factors associated with dietary patterns in adolescents: a schoolbased study in Cuiabá, Mato Grosso,” Revista Brasileira de Epidemiologia, vol. 15, no. 3, pp. 662–674, 2012.

Ministério da saúde, Secretaria de Atenção à Saúde, Departamento de Atenção Básica, Guia Alimentar Para a População Brasileira, Secretaria de Atenção à Saúde, Coordenação-Geral da Política de Alimentação e Nutrição, Brasília, Brazil, 2nd edition, 2014.

A. Amor, M. Serra-Mir, M. Martínez-González et al., “Prediction of cardiovascular disease by the Framingham-REGICOR equation in the high-risk PREDIMED Cohort : impact of the Mediterranean diet across different risk Strata,” Journal of the American Heart Association, vol. 6, pp. 1–13, 2017.

M. Doménech, P. Roman, J. Lapetra et al., “Mediterranean diet reduces 24-hour ambulatory blood pressure, blood glucose, and lipids,” Hypertension, vol. 64, no. 1, pp. 26-27, 2014.

S. Bo, V. Ponzo, I. Goitre et al., “Predictive role of the Mediterranean diet on mortality in individuals at low cardiovascular risk: a 12-year follow-up population-based cohort study,” Journal of Translational Medicine, vol. 14, no. 1, 2016.

M. Bonaccio, A. Di Castelnuovo, S. Costanzo et al., “Adherence to the traditional Mediterranean diet and mortality in subjects with diabetes. Prospective results from the MOLI-SANI study,” European Journal of Preventive Cardiology, vol. 23, no. 4, pp. 400–407, 2016.

I. Alvarez-Alvarez, I. Zazpe, J. Pérez de Rojas et al., “Mediterranean diet, physical activity and their combined effect on all-cause mortality: the Seguimiento Universidad de Navarra (SUN) cohort,” Preventive Medicine, vol. 106, pp. 45–52, 2018.

L. L. Lopes, M. C. G. Peluzio, and H. H. M. Hermsdorff, “Monounsaturated fatty acid intake and lipid metabolism,” Jornal Vascular Brasileiro, vol. 15, no. 1, pp. 52–60, 2016.

L. Schwingshackl and G. Hoffmann, “Monounsaturated fatty acids, olive oil and health status: a systematic review and meta-analysis of cohort studies,” Lipids in Health and Disease, vol. 13, no. 1, p. 154, 2014.

G. Buckland, A. L. Mayen, A. Agudo et al., “Olive oil intake and mortality within the Spanish population (EPIC-Spain),” American Journal of Clinical Nutrition, vol. 96, no. 1, pp. 142–149, 2012.

C. Marín, E. Yubero-Serrano, J. López-Miranda, and F. Pérez-Jiménez, “Endothelial aging associated with oxidative stress can Be modulated by a healthy Mediterranean diet,” International Journal of Molecular Sciences, vol. 14, no. 5, pp. 8869–8889, 2013.

H. H. M. Hermsdorff, B. Puchau, A. C. P. Volp et al., “Dietary total antioxidant capacity is inversely related to central adiposity as well as to metabolic and oxidative stress markers in healthy young adults,” Nutrition and Metabolism, vol. 8, no. 1, p. 59, 2011.

P. G. Cocate, A. J. Natali, A. De Oliveira et al., “Fruit and vegetable intake and related nutrients are associated with oxidative stress markers in middle-aged men c,” Nutrition, vol. 30, no. 6, pp. 660–665, 2014.

P. G. Cocate, A. J. Natali, A. D. E. Oliveira, R. C. Alfenas, and H. H. M. Hermsdorff, “Consumption of branched-chain amino acids is inversely associated with central obesity and cardiometabolic features in a population of Brazilian middle aged men: potential role of leucine intake,” Journal of Nutrition, Health and Aging, vol. 19, no. 7, pp. 771–777, 2015.

J. Abellán Alemán, M. P. Zafrilla Rentero, S. Montoro-García et al., “Adherence to the “Mediterranean diet” in Spain and its relationship with cardiovascular risk (DIMERICA study),” Nutrients, vol. 8, no. 11, 680 pages, 2016.

J. Bressan, H. H. M. Hermsdorff, M. Á Zulet, and J. A. Martínez, “Hormonal and inflammatory impact of different dietetic composition: emphasis on dietary patterns and specific dietary factors,” Arquivos Brasileiros de Endocrinologia and Metabologia, vol. 53, no. 5, pp. 572–581, 2009.

H. H. M. Hermsdorff, M. Ángeles Zulet, J. Bressan, and J. Alfredo Martínez, “Effect of diet on the low-grade and chronic inflammation associated with obesity and metabolic syndrome,” Endocrinología y Nutrición, vol. 55, no. 9, pp. 409–419, 2008.

M. de Lorgeril, “Mediterranean diet and cardiovascular disease: historical perspective and latest evidence,” Current Atherosclerosis Reports, vol. 15, no. 12, 2013.

R. Casas, E. Sacanella, M. Urpí-Sardà et al., “The effects of the Mediterranean diet on biomarkers of vascular wall inflammation and plaque vulnerability in subjects with high risk for cardiovascular disease. A randomized trial,” PLoS One, vol. 9, no. 6, Article ID e100084, 2014.

I. N. Bezerra, A. D. M. Souza, R. A. Pereira, and R. Sichieri, “Consumption of foods away from home in Brazil,” Revista de Saúde Pública, vol. 47, pp. 200s–211s, 2013.

P. Carús, G. V. A. França, and A. J. D. Barros, “Place and type of meals consumed by adults in medium sized cities,” Revista de Saúde Pública, vol. 48, no. 1, pp. 68–74, 2014.

J. F. Keith, S. Stastny, A. Brunt, and W. Agnew, “Barriers and strategies for healthy food choices among American indian tribal college students: a qualitative analysis,” Journal of the Academy of Nutrition and Dietetics, vol. 118, no. 6, pp. 1017–1026, 2018.

E. Howse, C. Hankey, M. Allman-Farinelli, and A. Bauman, “Buying salad is a lot more expensive than going to influences their food choices,” Nutrients, vol. 10, no. 8, p. 996, 2018.