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Ciência & Saúde Coletiva

Print version ISSN 1413-8123On-line version ISSN 1678-4561

Ciênc. saúde coletiva vol.25 no.7 Rio de Janeiro July 2020  Epub July 08, 2020 


Magnesium intake in a Longitudinal Study of Adult Health: associated factors and the main food sources

Ingestão de magnésio no Estudo Longitudinal de Saúde do Adulto: fatores associados e os principais alimentos contribuintes

Jéssica Levy1

Andreia Alexandra Machado Miranda1

Juliana Araujo Teixeira1

Eduardo De Carli1

Isabela Judith Martins Benseñor2

Paulo Andrade Lotufo2

Dirce Maria Lobo Marchioni1

1Faculdade de Saúde Pública, Universidade de São Paulo. Av. Dr Arnaldo 715, Cerqueira César. 01246-904 São Paulo SP Brasil.

2Hospital Universitário, Faculdade de Medicina, Universidade de São Paulo. São Paulo SP Brasil.


This study aimed to identify the sociodemographic and lifestyle factors associated with magnesium intake and describe the main food sources in the Brazilian Longitudinal Study of Adult Health (ELSA-Brazil). This observational, cross-sectional study was conducted using the baseline data from the ELSA-Brazil (2008-2010). Associations between usual magnesium intake and sociodemographic and lifestyle factors were analyzed using multiple linear regression. Food sources were identified by calculating the percentage contribution of each FFQ item to the amount of magnesium provided by all foods. The analysis was performed using Stata® software (version 12), assuming a statistical significance level of 5%. The top food sources to magnesium intake were as follows: beans, oats, nuts, white rice, orange, French bread, cooked fish, boneless meat, whole milk, and whole wheat bread. There were positive associations between magnesium intake and female sex; age ≥60 years; self-reported black, indigenous, or brown skin colors; per capita income ≥3 minimum wages, and moderate or vigorous physical activity levels. Sociodemographic and lifestyle factors were associated with magnesium intake among the evaluated individuals.

Key words Magnesium; Sociodemographic factors; Lifestyle; Food sources


O estudo tem por objetivo identificar fatores sociodemográficos e de estilo de vida associados à ingestão de magnésio e descrever seus principais alimentos contribuintes no Estudo Longitudinal de Saúde do Adulto (ELSA-Brasil). Trata-se de um estudo observacional, transversal, desenvolvido com dados da linha de base do ELSA-Brasil (2008-2010). Associações entre a ingestão habitual de magnésio e fatores sociodemográficos e de estilo de vida foram testadas por regressão linear múltipla. Contribuintes alimentares foram identificados a partir do cálculo do porcentual de magnésio fornecido por cada item do QFA em relação quantidade total proveniente de todos os alimentos. Os principais alimentos contribuintes para a ingestão de magnésio foram: feijão, aveia, nozes, arroz branco, laranja, pão francês, peixe cozido, carne sem osso, leite integral e pão integral. Foram encontradas associações positivas entre consumo de magnésio e sexo feminino, faixa etária ≥ 60 anos, cor de pele autodeclarada como negra, indígena ou parda, renda “per capita” ≥ 3 salários mínimos e níveis de atividade física moderado ou vigoroso. Alimentos da dieta tradicional do brasileiro foram os maiores contribuintes para a ingestão de magnésio, que também foi influenciada por fatores sociodemográficos e de estilo de vida.

Palavras-chave Magnésio; Fatores sociodemográficos; Estilo de vida; Alimentos contribuintes


Magnesium is the second most abundant intracellular ion and is involved in many metabolic functions, being vital for the activity of more 300 enzymes1. It plays an important role in ATP synthesis and activates almost all glycolytic enzymes and those of citric acid cycle. It is related to cell membrane permeability and electrical activity, besides being important for bone mineralization, muscle relaxation, and neurotransmission2-4. Deficiency of this ion can favor the development of various chronic noncommunicable diseases (NCDs), such as metabolic syndrome5-7, type 2 diabetes mellitus8,9, fibromyalgia10, hypertension8,11,12, osteoporosis13, and cardiovascular diseases14.

The Estimated Average Requirement (EAR)15 of magnesium is between 255 mg and 265 mg for women and between 330 mg/day and 350 mg/day for adult and elderly men. Magnesium is present in dark green vegetables, legumes, oilseeds, milk and dairy products, and whole grains. Fish, meat, and some fruits are the poorest sources of this mineral2. In the United States, 60% of the adult population have insufficient magnesium intake to attend the EAR16. This scenario was observed in more than 70% of the Brazilian adult population, according to the 2008-2010 National Food Survey (INA)17.

Food consumption of an individual or a population is strongly influenced by age, sex, income, and schooling18-20. In Brazil, family income is positively associated with the consumption of milk, meat, fruits, vegetables, and legumes; however, the consumption of vegetables and legumes is moderate even in the richest stratum of the population17. Furthermore, some studies reported that families in less favored socioeconomic strata and mothers with lower educational level consume more sweets and products rich in fat20.

Knowledge about food components of a population diet and the identification of the determinants of nutrient consumption can serve as subsidies for the formulation of public policies for the promotion of healthy eating and of combating NCDs. This study aimed to identify the sociodemographic and lifestyle factors associated with magnesium intake and describe the main foods that contribute to this nutrient among participants of the Longitudinal Study of Adult Health (ELSA-Brazil), the largest multicenter cohort ever recruited for research incidence and risk factors of NCD in the Brazilian population21.


Study population

This observational, cross-sectional study was developed using the baseline data from the ELSA-Brazil. ELSA-Brazil participants were recruited between August 2008 and December 2010. ELSA-Brazil is a cohort of 15,105 participants of both genders, aged 35-74 years, and are active and retired workers from six different states of Brazil: Espírito Santo, Minas Gerais, Bahia, São Paulo, Rio de Janeiro, and Rio Grande do Sul. Data were collected by trained and certified personnel under strict quality control21-23. Those without food consumption information (n = 24) were excluded from this study, totaling 15,081 participants. Individuals below the 1st percentile and above the 99th percentile of the total energy intake estimates (n = 362) were also disregarded in order to exclude possibly invalid food intake data. Thus, the final study sample consisted of 14,719 individuals.

The ELSA-Brazil was approved by the research ethics committees of all its research centers. All individuals voluntarily participated in this study and signed an informed consent form.

Food consumption assessment

The food frequency questionnaire (FFQ) developed and validated for ELSA-Brazil was used to evaluate the habitual food consumption of participants in the last 12 months24. This semiquantitative FFQ has 114 food items and is answered by interview. The questions are structured into 3 sections: (1) food/preparations, (2) consumption portion measures, and (3) consumption frequencies, with 8 response options: “more than 3 times/day,” “2-3 times/day, “”once a day,””5-6 times a week,””2-4 times a week, “”once a week,””1-3 times a month,” and “never/almost never.” At the end of the FFQ, participants were asked if they changed their dietary intake or if they did a restrictive diet over the past six months, being the participants able to answer yes or no to this question.

To evaluate energy and nutrient intakes, we used the United States Department of Agriculture (USDA) Food Composition Database, except when its values were outside of the range of 80% to 120% from those described in the Brazilian Table of Food Composition, which cases the latter database was used24. To reduce the errors associated with dietary measurement, magnesium intake was adjusted by total energy intake using the residue method25. Energy-adjusted values were employed both in the stratification of quantiles and linear regression analysis.

Sociodemographic and lifestyle factors

The choice of sociodemographic and lifestyle factors that could influence the dietary pattern was based on previous studies that addressed the determinants of food intake in the Brazilian adult population18,19. Therefore, sex, age, schooling, income, self-reported skin color, smoking and alcohol habits, nutritional status, and physical activity level were selected for this study.

Participants were classified according to sex as male and female) and according to age as adults (34-59 years) and elderly (≥ 60 years). Schooling was categorized as “complete elementary school,” “complete high school,” and “higher education or postgraduate.” The family income per capita was initially calculated as equivalent to the average minimum wage in the period between 2008 and 2010 (R$ 463.33) and then stratified into < 3 or ≥ 3 minimum wages. The following categories of self-reported skin color proposed by the Brazilian Institute of Geography and Statistics in the demographic census were questioned: “white,” “black,” “brown or mixed,” “yellow,” and “indigenous”26. Due the low frequency of yellow and indigenous reporters, these two categories were collapsed for analysis.

Smoking was evaluated using a semi-structured questionnaire about smoking habits at the time of the interview and in the past. Based on this questions, participants were categorized as “non-smokers,” “former smokers,” or “smokers.” Alcohol consumption data (grams of ethanol/day) were obtained from the FFQ. Participants were classified as alcohol “non-consumers” or “consumer” based on the reporting of consumption of any alcoholic beverages in the previous 12 months, irrespective of its frequency or amount.

To assess nutritional status, body mass index (BMI) was calculated and classified according to the World Health Organization criteria: low weight (< 18.5 kg/m2), eutrophia (18.5-24.9 kg/m2), overweight (25-29.9 kg/m2), and obesity (≥ 30 kg/m2)27. For the evaluation of physical activity level, we used the International Physical Activity Questionnaire (IPAQ)28, which consist in predetermined questions on frequency and duration of walking as well as moderate and vigorous physical activities at work, commuting, home and leisure times29. For the purposes of this study, we used only the domain of physical activity during leisure time, considering that these types of activities has been more consistently associated with socio-demographic factors, such as income, age, schooling and sex30. Moreover, physical activity in leisure is most frequently studied in epidemiological surveys31,32.

Statistical analysis

The consumption of energy-adjusted magnesium was stratified in quintiles in order to better represent the ranking of dietary magnesium, and sociodemographic and lifestyle factors were described according to the lowest (1st quintile) and highest (5th quintile) levels of its intake. Sociodemographic and lifestyle factors were presented as frequencies and percentages according to the sex of the participants. Pearson’s chi-squared test was used to evaluate the significant associations between variables.

The contribution of food to magnesium intake was calculated according to the methodology proposed by Block et al.33. Magnesium provided by each food item was divided by the total population magnesium intake to obtain the contribution of each food item. Then, the foods were listed according to the contribution ranking34.

The associations between energy-adjusted magnesium intake (mg/day, dependent variable) and sociodemographic and lifestyle factors (predictors) were tested by multiple linear regression analysis using the stepwise backward method. The energy-adjusted magnesium consumption variable approaches normality, according to the Shapiro-Wilk test and the use of histogram and Q-Q plot graphs, thus meeting this assumption for multiple linear regression.The sociodemographic and lifestyle factors included in the model were sex (reference: male), age (reference: adults), income (reference: < 3 minimum salaries), skin color (reference: white), schooling (reference: complete primary school), smoking (reference: non-smoker), alcohol consumption (reference: non-consumer), assess nutritional status (reference: eutrophy) and physical activity (reference: light).

The multiple model was further adjusted by self-reported change in dietary habits over the past 6 months. All analyses were performed using the Stata® (version 12) software, assuming a level of statistical significance of 5%.


The sample consisted of 14,719 participants, predominantly adults (78.5%), female sex (54.6%), non-smokers (57.1%), self-reported as white (52.6%), and with a higher education level or a post-graduate level (53.2%). As regards nutritional status, 40.3% of the population was classified as overweight and 22.8% were obese.

The distribution of sociodemographic and lifestyle characteristics according to the magnesium intake of men and women is presented in Table 1. Higher proportions of the elderly and individuals with a higher education or who achieved a postgraduate level, with income ≥ 3 minimum wages, who are former or non-smoker, with eutrophia, and with moderate or vigorous physical activity level had magnesium intake in the last quintile, compared with the first (Table 1).

Table 1 Socio-demographic and lifestyle data according to magnesium intake in ELSA-Brasil. Brazil, 2008-2010. 

Characteristics Magnesium Intake (Quintiles)
Total Male Female
P 1º Q 5º Q P 1º Q 5º Q P
Magnesium Quintis (mg / day, min-max) 11,6 -1290,5 58,9 -388,7 532,4 -1290,5 11,6-388,6 532,3 -1252,5
Age Group (n,%)
Adults 11547 78,5 < 0,001 1320 82,3 1020 72,5 < 0,001 1142 85,2 1095 71,3 < 0,001
Elderly 3172 21,6 284 17,7 387 22,3 198 14,8 441 28,7
Self-reported skin color (n,%)
White 7653 52,6 < 0,001 836 52,9 711 51,3 0,384 583 44,1 796 52,4 < 0,001
Brown 4082 28,1 471 29,8 420 30,3 389 29,4 398 26,2
Black 2304 15,8 221 14,0 206 14,9 308 23,3 268 17,6
Others 514 3,5 53 3,3 48 3,5 42 3,2 58 3,8
Schooling (n,%)
Complete primary education 1813 12,3 < 0,001 256 16,0 229 16,3 < 0,001 161 12,0 121 7,9 < 0,001
Complete high school 5078 34,5 587 36,6 470 33,4 626 46,7 464 30,2
Higher or Postgraduate 7828 53,2 761 47,4 708 50,3 553 41,3 951 61,9
Income per capita (n,%)
< 3 minimum wages 7312 49,7 < 0,001 886 55,2 709 50,4 < 0,001 842 62,8 618 40,2 < 0,001
≥3 minimum wages 7407 50,3 718 44,8 698 49,6 498 37,2 918 59,8
Smoking (n,%)
Non-smoking 8405 57,1 < 0,001 763 47,6 749 53,2 < 0,001 801 59,8 1009 65,7 < 0,001
Former smoker 4419 30,0 542 33,8 517 36,7 318 23,7 413 26,9
Smoker 1894 12,9 299 18,6 141 10,0 221 16,5 114 7,4
Alcohol consumption (n,%)
Non-consumer 2959 22,5 0,060 297 19,4 320 24,1 0,006 308 27,1 310 24,5 0,06
Consumer 10189 77,5 1235 80,6 1010 75,9 830 72,9 956 75,5
Nutritional status (n,%)
Low weight 135 0,9 < 0,001 14 0,9 13 0,9 0,007 15 1,1 17 1,1 < 0,001
Eutrophy 5291 36,0 499 31,1 534 38,0 465 34,7 639 41,6
Overweight 5929 40,3 729 45,5 596 42,4 462 34,5 545 35,5
Obesity 3358 22,8 362 22,6 263 18,7 397 29,7 335 21,8
Leisure time physical activity (n,%)
Light 11155 76,9 < 0,001 1260 79,9 917 66,1 < 0,001 1186 90,1 1041 68,9 < 0,001
Moderate 2037 14,1 182 11,5 262 18,9 87 6,6 296 19,6
Vigorous 1309 9,0 136 8,6 208 15,0 43 3,3 175 11,6

The values presented are minimum and maximum for the continuous variable and frequencies and percentages for the categorical variables. Associations between categories were analyzed with the Pearson Chi-square test. A value of P < 0.05 was considered statistically significant.

The top ten contributors to magnesium intake are described in Table 2. The highest contributors were beans (24.0%), oats (4.5%), nuts (3.6%), white rice (3.3%), orange (3.3%), French bread (3.2%), cooked fish (3.0%), boneless meat (2.6%), whole milk (2.3%), and whole-grain bread (2.1%) (Table 2).

Table 2 Main food sources of magnesium intake at ELSA-Brasil. Brazil, 2008-2010. 

Rank Food Percentage contribution 95% confidence interval
Bean 24,2 (23,8; 24,6)
Oats 4,5 (4,3; 4,6)
Walnuts 3,6 (3,4; 3,8)
White rice 3,3 (3,2; 3,3)
Orange 3,3 (3,2; 3,3)
Bread 3,2 (3,1; 3,2)
Cooked fish 3,0 (2,9; 3,0)
Boneless meat 2,6 (2,5; 2,6)
Whole Milk 2,3 (2,3; 2,4)
10º Whole grain bread 2,1 (2,0; 2,2)

Except for schooling, all other sociodemographic and lifestyle variables investigated were independently associated with magnesium intake. As shown in Table 3, positive and significant correlations were found between intake of magnesium and female gender; age ≥60 years; skin color self-declared as black, brown, or indigenous; income ≥3 minimum wages; and moderate or vigorous physical activity levels. By contrast, smoking, alcohol consumption, and overweight or obesity were negatively associated with magnesium intake (Table 3).

Table 3 Multiple linear regression model between magnesium intake and socio-demographic and lifestyle factors in ELSA-Brasil, 2008-2010. 

Predictors β 95% confidence interval P
Sex (reference: Male)
Female 7,8 (4,2; 11,3) < 0,001
Age group (reference: Adults)
Elderly 20,2 (15,9; 24,6) < 0,001
Self-Decawn Skin Color (Reference: White)
Brown 5,3 (1,1; 9,5) 0,013
Black 8,5 (3,3; 13,8) 0,001
Others 3,7 (-6,1; 13,4) 0,460
Education (reference: Complete primary school)
Complete high school -5,4 (-11,4; 0,6) 0,080
Higher or postgraduate 0,6 (-6,4; 6,5) 0,985
Income per capita (reference: < 3 minimum salaries)
≥ 3 minimum wages 11,6 (7,4; 15,8) < 0,001
Smoking (reference: Non-smoker)
Former smoker 1,8 (-2,1; 5,7) 0,376
Smoker -18,0 (-23,3; -12,7) < 0,001
Ethic (reference: Non-consumer)
Consumer -7,0 (-11,2; -2,8) < 0,001
Nutritional status (reference: Eutrophy)
Low weight 14,0 (-4,4; 32,5) 0,1037
Overweight -6,7 (-10,7; -2,8) < 0,001
Obesity -13,3 (-17,9; -8,6) < 0,001
Physical activity level (reference: Light)
Moderate 27,8 (22,8; 32,9) < 0,001
Vigorous 31,5 (25,5; 37,5) < 0,001

A value of P < 0.05 was considered statistically significant. The model was further adjusted by self-reported change in dietary habits over the past 6 months.


In this study, variations in magnesium intake among ELSA-Brazil participants (2008-2010) were explained by sociodemographic characteristics that influence food sources, such as sex, age, race/ethnicity, and family income. In addition, smoking and consume alcohol were lifestyle habits that were negatively associated with mineral intake, as did obesity and overweight, while the opposite was evidenced in relation to the level of leisure physical activity, independently of other factors evaluated. Food sources that contributed to more than a half of total magnesium consumption included beans, cereals (oats, rice, and French bread), nuts, oranges, meats (fish and cattle), and milk, although dark green vegetables, almonds, nuts, and legumes had little expressive participation, suggesting a possible dietary inadequacy17,35,36.

Consistent with our observations, in a population-based study, Sales et al.35 reported that more than a quarter of the magnesium in the diet of São Paulo inhabitants came from beans, rice, and French bread, confirming the important contribution of typical Brazilian food standards. Moreover, age had a positive effect on the intake of magnesium and other minerals, such as calcium, phosphorus, and potassium, signaling better quality of diet among the elderly, in relation to adults and adolescents35. In our study, female gender, as well as age, was also associated with higher magnesium intake. In a previous analysis performed with the same ELSA-Brazil sample, Cardoso et al.37 revealed that women and elderly had higher adherence to a “healthy” diet characterized by vegetables and fruits38, which could be related to the higher intake of magnesium among these individuals.

According to the data from POF 2008-2009, in the Brazilian population, schooling and income are indicators of socioeconomic status independently associated with the higher consumption of saturated fat, sodium, and lower consumption of fiber, indicating that purchasing power and educational level do not necessarily determine better food choices in our social context33. Furthermore, analyses showed that income, not schooling, was associated with higher magnesium intake, after adjusting for demographic and lifestyle characteristics. These findings may be due to factors related to access, availability, and prices of magnesium food sources (dairy products, fresh meats, and vegetables)18,38. Notably, ELSA-Brazil participants, linked to teaching and research institutions, present a higher level of education than the general Brazilian population, which could make income a stronger determinant of food consumption. In fact, we notice a relatively higher contribution of oats, walnuts, cooked fish and whole grain bread, but lower of beans to the total magnesium intake among participants with an income per capita ≥ 3 minimum wages, suggesting a different pattern of this mineral food sources consumption among the richer participants (Supplementary Table 1), corroborating with literature39,40.

By contrast, individuals with self-declared skin color such as brown, black, or indigenous presented higher values of dietary magnesium. Due to ethnic miscegenation in Brazil, it is a fundamental element to understand the association of race/ethnicity with food consumption, given the recognized role of cultural heritage and historical value of food in the construction of traditional and healthy eating habits. In the National Health Survey (PNS, 2013), for example, black and brown skin colors were associated with a significantly higher frequency of regular bean consumption (≥ 5 times/week)18. As already commented, almost a quarter of the total intake of magnesium in ELSA-Brazil was attributed to this legume. Together with rice, beans make up the basis of traditional Brazilian lunch and dinner, and this combination has been shown to be a protective factor for obesity and other NCDs41-43.

Supplement Table 1 Main food sources of magnesium intake second income at ELSA-Brasil. Brazil, 2008-2010. 

Income per capita
< 3 minimum wages ≥3 minimum wages
Rank Food Percentage contribution 95% confidence interval Rank Food Percentage contribution 95% confidence interval
Bean 28,6 (28,1;29,2) Bean 18.9 (18,5;19,4)
White rice 3,8 (3,7;3,8) Oats 5.8 (5,6;6,0)
French bread 3,6 (3,5;3,7) Walnuts 5.3 (5,1;5,5)
Orange 3,5 (3,3;3,6) Cooked Fish 3.2 (3,1;3,3)
Oats 3,3 (3,1;3,4) Orange 3.0 (2,9;3,1)
Whole Milk 2,9 (2,8;3,0) Whole grain bread 2.7 (2,6;2,8)
Cooked fish 2,7 (2,6;2,8) French bread 2.7 (2,6;2,8)
Boneless meat 2,6 (2,5;2,6) White rice 2.7 (2,6;2,7)
Banana 2,2 (2,1;2,3) Boneless meat 2.6 (2,5;2,7)
10º Walnuts 2,2 (1,8;2,5) 10º Skimmed milk 2.6 (2,5;2,6)

Changes in the gustatory ability of foods due to smoking and the recognized negative effect of excessive alcohol consumption on appetite and food consumption could explain the inverse correlation between these two lifestyle habits and the intake of magnesium. On the other hand, as already evidenced by another study35, higher values of dietary magnesium were estimated among the participants classified in the levels of moderate and vigorous physical activity. As characteristics of the nutritional transition faced by the country, urbanization and the adoption of unhealthy lifestyle habits have accompanied the increase in the consumption of ultra-processed foods and of low nutritional value18,22. The findings indicate the importance of promoting diet quality, with a stimulus to the consumption of magnesium sources, especially among subgroups at risk for NCDs.

That way, there is an inverse association between magnesium intake and excessive body weight, that is, the worse the nutritional status the lower the consumption of magnesium, even after adjustment for energy consumption, physical activity level, and other sociodemographic and lifestyle characteristics evaluated. Some authors, based on evidence of a deleterious role of magnesium deficiency on insulin resistance, inflammation, and oxidative stress, support the hypothesis of a causal relationship between the inadequacy of the mineral and the aggravation of weight gain and expansion of body adiposity, a characteristic of obesity44-46. Although other population studies, such as ours, reported a lower intake of magnesium among obese individuals16,44, it is still uncertain whether these findings reflect a poor overall quality of the diet or if the inadequacy of its consumption would be a risk factor for the disease45,46. Due to the transversal design of the study, inferences of causality are not possible; however, they can be explored with a longitudinal follow up of these individuals.

Furthermore, we estimated magnesium intakes with a FFQ, which is a method widely used in large epidemiological studies to rank individual according to their levels of dietary intakes in the previous twelve months45. However, its use can be considered another study limitation since this method is not consider the most appropriate for the quantitative analysis of micronutrients, given its inherent inaccuracy, that preclude the evaluation of individual or population nutrients intake adequacy. However, ELSA-Brazil FFQ was previously validated and performed well in classifying individuals according to magnesium intake levels, allowing their use in our comparative analysis between groups24,47.

To evaluate the energy and nutrient intake, the Food Composition Database of the United States Department of Agriculture (USDA) or the Brazilian Food Composition Table were used. In the Brazilian Table of Food Composition many foods are still presented only in their raw form; in addition, the table does not present many essential nutrients for analysis in studies on chronic diseases. The table used in the NDSR is representative for North American countries, therefore, the amounts of nutrients may vary in relation to food in Brazil. To overcome this issue, we used a systematic routine to correct contrasting nutrient values between databases, similarly to an approach employed by an American Latin multicentric study48 .


Foods from the traditional Brazilian diet were the largest contributors of dietary magnesium among the evaluated participants. In addition, not only sociodemographic but also lifestyle factors were associated with the ingestion of this mineral.


We thank ELSA-Brazil participants who agreed to collaborate in this study, with the support of the Ministry of Health, the Ministry of Science and Technology, National Research Council, and the Foundation for Research Support of the State of São Paulo.


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Received: March 08, 2018; Accepted: November 12, 2018; Published: November 14, 2018


The first author of the article, J Levy, done design and design of the study, analysis, interpretation of data and review of the article; AAM Miranda, JA Teixeira and E De Carli contributed analysis, interpretation of data and revision of the article; IJM Benseñor and PA Lotufo contribuited article revision and DML Marchioni contribuited review of the article and approval of the final version.

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