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BACKGROUND
Cardiovascular disease (CVD) is the main cause of death worldwide (1). In 2017, it was calculated that 20.1 % of the Mexican population would die from heart disease (2). Furthermore, its prevalence has increased in the last decades in our country. In 2008, the mortality rate associated with heart disease was estimated at 8.4 per 10,000 individuals, while in 2017 it was reported to be 11.4 per 10,000 individuals (3).
Obesity, impaired fasting glucose, high blood pressure, and dyslipidemia are major risk factors for the development of CVD (4), while a high consumption of saturated fats, sodium, and refined carbohydrates is the main behavioral factor associated with this condition (5). Mexico has experienced important dietary changes over the last decades (6,7) with an increase in consumption of sweetened beverages and saturated fat, and a decrease in intake of fiber, fruits and vegetables (8), as well as excessive dietary sodium intake (9).
There is growing evidence that adherence to certain dietary patterns such as the Mediterranean diet (10,11) or the Dietary Approach to Stop Hypertension (DASH) (12-14) decreases the risk of developing CVD. The DASH dietary pattern was first suggested to prevent and treat hypertension (15,16); it promotes a high consumption of vegetables, fruits, whole grains, skim dairy products, fish, poultry, and seeds, and limits the intake of red meat, sugar, sweetened beverages, total and saturated fat, as well as cholesterol and sodium (17). In adult populations adherence to the DASH-style diet has demonstrated its ability not only to lower blood pressure (18) but also to improve lipid profile (19), glucose (20, 21), insulin resistance (20-22), proinflammatory proteins (23), body mass index (BMI), and waist circumference (24, 23). It has also been associated with a reduced incidence of CVD, ischemic heart disease (IHD) and stroke (12-14).
The impact of adherence to the DASH dietary pattern on diverse health outcomes has been evaluated in many populations; however, it has not been well studied among the Mexican population, whose diet is characterized by a diversity of traditional foods and preparation methods that may differ from those observed in the North American population, for which this dietary pattern was initially developed. Thus, the aim of this study was to assess adherence to the DASH dietary pattern by using an adapted DASH adequacy index (DASH-AI), and to evaluate its association with CVD risk markers among Mexican adults living in Mexico City.
MATERIAL AND METHODS
SUBJECTS AND STUDY DESIGN
This is a sub-analysis of baseline data from participants enrolled in the Tlalpan 2020 cohort, which is an ongoing observational, prospective, longitudinal study of risk factors for hypertension incidence in a Mexico City population aged 20-50 ye- ars. This study is being conducted by the National Institute of Cardiology Ignacio Chávez (INCICh, from its acronym in Spanish: Instituto Nacional de Cardiología Ignacio Chàvez). Methodology details of the Tlalpan 2020 cohort were published elsewhere (25). Overall, subjects with hypertension, diabetes mellitus, dysthyroidism, cerebrovascular disease, ischemic cardiomyopathy, acute coronary syndrome, active cancer with an effect on survival, cognitive or mental disability, pregnant women, and those taking medications that modify blood pressure were excluded from the cohort. For the purpose of this cross-sectional analysis, participants recruited in the cohort between September 2014 and June 2017 were included. Additionally, subjects who were screened but excluded from the Tlalpan 2020 cohort due to hypertension (SBP ≥ 140 mmHg and/or DBP ≥ 90 mmHg, n = 88) or diabetes mellitus (DM) (fasting glucose ≥ 126 mg/dL, n = 24) or both (n = 6) were included in this analysis. Subjects with incomplete dietary data (n = 8), extreme energy intake (n = 15) or incomplete urinary samples (n = 541), as described below, were excluded from the analysis. All participants provided their written informed consent to be recruited in the cohort. The study was approved by the Institutional Research and Bioethics Committees of the INCICh (REF:13-802). Clinical assessments and the collection of biologic samples and all data related to this study were conducted at the INCICh.
ANTHROPOMETRIC ASSESSMENT
Weight, height, and WC assessments were conducted by trained personnel according to the procedures described by The International Society for the Advancement of Kinanthropo- metry (26). Patients were fasting, shoeless, and wearing a hospital gown. We used a mechanical column scale (SECA 700) with a capacity of 220 kg (0.05-kg precision) to measure weight, and a stadiometer SECA 220 (1-mm precision) to assess height. WC was measured with a measuring tape made of glass fiber (BodyFlex) with a length of 150 cm (1-mm precision). BMI was computed by dividing body weight (kilograms) by the square of the height (squared meters).
BIOCHEMICAL DATA
Twelve-hour fasting blood samples were drawn from all participants. Serum levels of glucose, triglycerides (Tg), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-c), and low-density lipoprotein cholesterol (LDL-c) were determined by using automated analyzers at the Central Lab of the INCICh.
TWENTY-FOUR-HOUR URINARY SODIUM EXCRETION
Urinary sodium excretion was analyzed as a proxy of sodium intake. Participants were asked to collect a 24 h urine sample the day prior to their baseline visit at the INCICh. They were provided with a preservative-free container and were instructed on the correct form of collecting a urine sample (discard the first urine in the morning and collect all urine for a period of 24 h, including the first void of the day of the visit). Participants were also asked to store the collected urine in a cool place during the collection period. Urinary sodium was determined using the ion selective electrode method, and urinary creatinine was measured by Jaffe's colorimetric assay in automated analyzers. Urine samples were considered as complete when urinary creatinine levels were within the standard creatinine excretion rate interval according to sex (15-25 mg/kg/24 h for men and 10-20 mg/kg/24 h for women) (9).
DIETARY INFORMATION
Dietary information was collected by trained personnel using a semiquantitative food frequency questionnaire (FFQ) previously validated for the Mexican population (28). This FFQ comprises 116 foods and beverages categorized into ten different food groups: 1) Dairy products; 2) Fruits; 3) Eggs, meat, fish, poultry, and processed meat; 4) Vegetables; 5) Legumes; 6) Cereal products; 7) Sweets and candies; 8) Beverages; 9) Oils; and 10) Mexican foods. The FFQ evaluates ten frequencies of consumption of a standardized portion of each food or beverage during one year prior to the interview: never, less than once per month, 1-3 times per month, once per week, 2-4 times per week, 5-6 times per week, once per day, 2-3 times per day, 4-5 times per day, and 6 or more times per day. Energy and daily nutrient intake were estimated by using the software program ‘Sistema de Evaluaciòn de Hàbitos Nutricionales y Consumo de Nutrimentos' (Evaluation System of Nutritional Habits and Nutrient Consumption) (29), developed by the National Institute of Public Health of Mexico.
Participants with incomplete FFQ or extreme energy intake were excluded from the analysis. FFQ was considered incomplete when there were one or more missing answers. An individual's energy intake was considered as extreme when its value was 3 standard deviations (30) below or above the mean value according to sex. Individuals with an energy intake < 500 kcal/day were also excluded from the analysis.
OTHER COVARIATES
Other variables such as education level, physical activity, alcohol consumption and tobacco use were also collected during face-to-face interviews conducted by trained research personnel. Education level was documented as the last academic degree held. Physical activity was determined using the long version (7 days) of the “International Physical Activity Questionnaire" (IPAQ) for people aged between 15 and 69 years (31). This questionnaire evaluates three specific characteristics of physical activity: intensity, frequency and duration through questions that consider work-, home-, transportation- and exercise-related activities. Metabolic equivalents (METs, measured as minutes/week) were estimated and physical activity was categorized as low, middle and high according to the IPAQ criteria (31). Current alcohol consumers were all participants who, at the time of the interview, reported to currently consume alcohol regardless of frequency of consumption (daily, every other day, every weekend, every two weeks, once a month, or less than once a month). Current smokers were those persons who reported to have smoked at least 100 cigarettes in their lifetime and that, at the time of the interview, smoked either every day or some days (32).
DASH ADEQUACY INDEX (DASH-AI)
To assess adherence of the diet to the nutrient intake recommendations of the DASH dietary pattern (15), we adapted the DASH adequacy index (DASH-AI) from the work of Gao et al. (33). Gao's index considers 8 of the 10 DASH nutrient intake recommendations: total fat: ≤ 27 % of total energy per day (%TE)/day; saturated fatty acids: ≤ 6 %TE/day; proteins: ≥ 18 %TE/day; sodium < 2300 mg/day, potassium: ≥ 4700 mg/day, calcium: ≥ 1250 mg/day, magnesium: ≥ 500 mg/day, and fiber: ≥ 30 g/day. One point is granted to each nutrient if the person reaches the corresponding recommendation, and zero points otherwise. We adapted this index by including the two remaining DASH dietary recommendations for cholesterol (≤ 27 mg/day) and carbohydrates ≤ 55 %TE/day), and considering three levels of compliance instead of two to add flexibility to the scoring system. In our DASH-AI, one point is granted to each dietary factor when its intake complies with the DASH recommendation, 0.5 points if it does not meet the DASH recommendation but is within a range between the DASH recommendation and another recommendation proposed by a health organization, and 0 points if the intake is out of that range. The health organizations considered included: The National Institutes of Health (Adult Treatment Panel III) (4) for proteins, carbohydrates, total fat, and cholesterol; the World Health Organization (34) for saturated fat and potassium; and the Institute of Medicine of United States for calcium (35) and magnesium (36). For dietary sodium, we considered the level of intake at which people with heart failure presented a higher risk for an acute decompensated heart failure event (37). The DASH-AI total score is the sum of the points obtained from the 10 dietary factors. The higher the score of the DASH-AI, the better the diet quality index (Table I).
OUTCOMES: CVD RISK MARKERS
The studied CVD risk markers were BMI, WC, glucose, Tg, TC, HDL-c, and LDL-c. For the purpose of the analysis, they were studied in their continuous and categorical forms, according to the following cutoff points, to be considered elevated or altered: BMI: ≥ 25 kg/m2; WC: ≥ 90 cm for men and ≥ 80 cm for women (38); glucose: ≥ 100 mg/dL (38); Tg: ≥ 150 mg/dL (4); TC: ≥ 200 mg/dL (4); HDL-c: < 40 mg/dL (4); LDL-c: ≥ 130 mg/dL (4).
STATISTICAL ANALYSIS
Descriptive statistics of the study sample were presented as means ± SD, and compared between men and women by using the Student's t-test. Categorical variables were described as percentages and compared between men and women by employing the χ2 test.
Multivariable linear regression analyses were performed to test the linear association between total DASH-AI score and cardiovascular risk markers. A set of two models was computed for each studied outcome, except for BMI and WC. Model 1 was adjusted for relevant covariates for all outcomes: age, sex, physical activity, education level and daily energy intake. Model 2 encompassed all variables included in Model 1 plus other relevant covariates for each outcome. In this fully adjusted model, SBP and DBP were further adjusted for BMI, alcohol consumption and tobacco use; glucose, total cholesterol, HDL-c and LDL-c were further adjusted for BMI; and Tg for BMI and alcohol consumption. Model 1 was considered as the fully adjusted model for BMI and WC. Regression coefficients (β) and 95 % confidence intervals (CI) were estimated for the association between the DASH-AI score and each CVD risk marker. Additionally, multivariable logistic analyses were performed to estimate adjusted odds ratio (OR) and 95 % CI of having altered values of the studied CVD risk markers. A set of two models was computed for each studied outcome, except for BMI and WC as described above.
A p-value < 0.05 was considered statistically significant for all analyses. All statistical analyses and data processing were performed using STATA, version 14 (Stata Corp., College Station, TX, USA) for Mac (39).
RESULTS
Overall, 1490 participants were included in the analysis, of which 505 (33.9 %) were men. Table II shows the general characteristics of the study sample by sex. Men were younger than women, and there was a higher proportion of participants with a post-secondary education or higher, tobacco use and alcohol consumption, as well as a higher calorie intake among men. We observed no significant differences in the categories of physical activity or total DASH-AI between sexes.
Table II. General characteristics of the studied sample
DASH-AI: DASH adequacy index. *Student's t-test to compare continuous variables between men and women. †χ2 test to compare categorical variables between men and women.
Table III shows the anthropometric, clinical, and biochemical characteristics of the study sample. Mean WC, SBP and DBP were significantly higher among men when compared to women. Biochemical indicators also showed mean values significantly higher among men compared to women, except for HDL-c, which was higher in women.
Table III. Anthropometric, clinical, and biochemical characteristics of the studied population grouped by sex*
BMI: body mass index (kg/m2); WC: waist circumference (cm); SBP: systolic blood pressure; DBP: diastolic blood pressure; HDL-c: high-density lipoprotein cholesterol; LDL-c: low-density lipoprotein cholesterol; Tg: triglycerides. *t-test for comparison between men and women.
Mean 24-h urinary sodium excretion in the overall study sample was 3119.5 ± 1280.9 mg/24 h, and it was significantly higher in men as compared to women.
Figure 1 shows the proportion of participants that scored 0, 0.5 or 1 point for each dietary factor included in the DASH-AI. More than half of the studied sample did not score any points for intake of proteins (64.6 %), total lipids (53.7 %), saturated fatty acids (57.6 %), cholesterol (70.3 %), calcium (69.5 %), potassium (73.0 %) and sodium (54.1 %). Carbohydrate intake was the DASH-AI component with the highest degree of adherence to DASH dietary recommendations, showing the largest proportion of participants achieving one point within the DASH-AI (79.3 %); the opposite was observed for saturated fatty acids, with the lowest proportion of participants achieving one point (1.5 %).
Table IV shows the multivariable association (β and 95 % CI) between the DASH-AI score and each of the studied cardiovascular risk markers. For every one-unit increase in the DASH-AI score, BMI significantly decreased 0.55 units, WC 1.66 cm, SBP 0.54 and DBP 0.65 mmHg after adjustment for sex, age, physical activity, education level, and energy intake (Model 1). No significant associations were observed in the fully adjusted models; however, DBP exhibited a trend towards a 0.35-mmHg decrease per one unit increase in the DASH-AI score.
Table IV. Linear association (β [95 % CI]) between the DASH-AI score and cardiovascular risk markers
BMI: body mass index (kg/m2); WC: waist circumferences (cm); SBP: systolic blood pressure; DBP: diastolic blood pressure; HDL-c: high-density lipoprotein cholesterol; LDL-c: low-density lipoprotein cholesterol; Tg: triglycerides. Model 1: adjusted for sex, age, physical activity, education level and energy intake. Model 2 for SBP and DBP: variables included in Model 1 plus BMI, alcohol consumption and tobacco use. Model 2 for glucose, total cholesterol, HDL-c and LDL-c: variables included in Model 1 plus BMI. Model 2 for Tg: variables included in Model 1 plus BMI and alcohol consumption.
Table V shows the multivariable-adjusted ORs (95 % CI) for the association between each cardiovascular risk marker and the DASH-AI score. The risk of having BMI ≥ 25 kg/m2, WC ≥ 90 cm in men and ≥ 80 cm in women, DBP ≥ 80 mmHg, and HDL-c < 40 mg/dL decreases by 18 %, 28 %, 17 %, and 13 % per each unit increase in the DASH-AI score, respectively, in Model 1. We observed no significant association in the fully adjusted models for SBP, DBP and biochemical parameters.
Table V. Logistic regression analysis for the association between DASH-AI score and cardiovascular risk markers
BMI: body mass index (kg/m2); WC: waist circumferences (cm); SBP: systolic blood pressure; DBP: diastolic blood pressure; HDL-c: high-density lipoprotein cholesterol; LDL-c: low-density lipoprotein cholesterol; Tg: triglycerides. Model 1: adjusted for sex, age, physical activity, education level and energy intake. Model 2 for SBP and DBP: variables included in Model 1 plus BMI, alcohol consumption and tobacco use. Model 2 for glucose, total cholesterol, HDL-c and LDL-c: variables included in Model 1 plus BMI. Model 2 for Tg: variables included in Model 1 plus BMI and alcohol consumption.
DISCUSSION
In this study conducted in an adult population living in Mexico City we found an inverse association of the DASH-AI score with BMI, WC, and DBP. We also reported an overall low adherence to DASH dietary recommendations, with a mean DASH-AI score in this studied sample of 3.3 ± 1.2 points from a total of 10 points, with potassium being the dietary component of the DASH-AI with the highest proportion of participants with no adherence. To our knowledge, this is the first study that evaluates adherence to the DASH dietary pattern through a score based on its nutrient intake goals and its association with cardiovascular risk markers in an adult Mexican population.
Other studies have also reported low adherence to the DASH dietary pattern. Lemon et al. (40) found a low percentage of individuals who adhered to the DASH nutrient intake recommendations for potassium (3.1 %), calcium (11.6 %), magnesium (2.5 %) and fiber (3.8 %) in an adult population from the United States. In our study, we observed a low adherence to the DASH nutrient intake recommendations for potassium (4.3 %), magnesium (4.8 %), calcium (4.2 %), saturated fatty acids (1.9 %), total lipids (6.2 %) and proteins (8.3 %). Gao et al. (33), in a study conducted in the United States, observed that Hispanics were less likely to adhere to the calcium recommendation (OR: 0.79, 95 % CI: 0.68 to 0.91) when compared to white, Chinese, and African participants, but more likely to comply with the fiber recommendation (OR: 2.23, 95 % CI: 1.53 to 3.23) when compared to other ethnicities. In our study, we found that 33.1 % of the studied population adhered to the fiber recommendation.
The DASH dietary pattern promotes a high consumption of vegetables, fruits, whole grains, skim dairy products, fish, poultry and seeds, and limits the intake of red meat, sugar, sweetened beverages, total and saturated fat, cholesterol and sodium (17). Low adherence to the DASH dietary pattern may be related to its strict nutrient intake daily goals (15); for instance, the DASH re- commendation for potassium is 4700 mg/day, while the WHO recommendation for this nutrient is 3510 mg/day (41); similarly, the recommendation for calcium consumption proposed by DASH is 1250 mg/d, while the US Institute of Medicine recommends an intake of 800 mg/day (35). In addition, the low adherence to the DASH nutrient intake recommendations may reflect the lack of a regional-specific adaptation of these dietary guidelines that includes local foods to achieve the DASH nutrient intake goals.
In this sense, a recent study in Mexican women evaluated the association between the DASH dietary pattern, among others, and anthropometric measures (42). The DASH score used was based on 8 food components rather than the DASH daily nutrient intake goals as in our study. After adjustment for age, education, center, energy intake, and physical activity, no significant association between the DASH score and anthropometric measures was observed (42). In our study, which included women and men, we found an adjusted negative association of the DASH-AI score with BMI, WC, SBP and DBP, controlling for sex, age, physical activity, education level and energy intake; also, we observed an adjusted 17 % decreased risk of having elevated DBP (≥ 80 mmHg) per one-point increase in the DASH-AI score when adjusting for the same covariates, supporting the blood pressure lowering effects of this dietary pattern, although this association was attenuated when BMI, alcohol consumption, and tobacco use were also included in the model. Differences in the results of these two studies might be related to the fact that the first one used an index based on food components of the DASH dietary patterns while we used an index based on the DASH daily nutrient goals. Thus, we considered nutrients instead of the foods endorsed by the DASH diet, and this might have helped take into consideration local foods during the assessment of adherence to the DASH dietary pattern. This points out the need for a local adaptation of this a priori-defined dietary pattern.
Other studies that evaluated adherence to the DASH diet using scores based on daily nutrient intake goals have reported similar results to those observed in our study. Saneei et al. (43) informed that the adherence to this dietary pattern significantly decreased the risk of having a WC greater than 80 cm (OR: 0.32; 95 % CI: 0.10, 0.68) and blood pressure greater than 130/85 mmHg (OR: 0.20; 95 % CI: 0.09, 0.67) in adult women from Iran. Staffileno et al. (44) also found that a lower compliance with the DASH nutrient intake recommendations increased the probability of presenting blood pressure ≥ 140/90 mmHg by 37 % (OR: 1.37; 95 % CI: 1.06, 1.77) in a Latino population living in the United States.
In this study, we adapted the index proposed by Gao et al. (33) by including the DASH recommendation for carbohydrates and cholesterol, and considering three levels of adherence to each dietary factor (full adherence: 1 point; intermediate adherence: 0.5 points; and no adherence at all: 0 points) in order to allow more flexibility for the scoring system. We opted for using an index based on the DASH nutrient intake goals rather than the DASH food components under the premise that the DASH nutrient intake goals may be assessed worldwide regardless of local dietary patterns, while the food intake and meal plan proposed by the DASH dietary pattern may not suit local food availability, and thus mislead the assessment of adherence to the DASH dietary pattern in populations such as the Mexican one. Additionally, it has been reported that the DASH diet foods tend to be less available among patients with a low socioeconomic status (45). Mackenbach et al. (46) studied the association between complying with the DASH food groups and socioeconomic level; the authors found that persons with a lower socioeconomic status were less likely to comply with the DASH food group recommendations when compared to those with a higher socioeconomic status (OR: 0.59; 95 % CI: 0.52 to 0.68) (45).
This study has some limitations that should be considered. Firstly, the cross-sectional design does not allow establishing causality between adherence to the DASH dietary pattern and cardiovascular risk markers. However, the FFQ used in this study collects information on dietary intake over the last year, which makes it possible to assess exposure to food during this period. Secondly, the FFQ does not include the consumption of certain foods such as fast food (hamburgers and hot dogs), skim milk, and oilseeds. These foods could be relevant in the assessment of adherence to the DASH diet. However, this study has the strength of having estimated sodium consumption through its excretion in 24-hour urine. Finally, the study population was composed of volunteers living in Mexico City who enrolled in a cohort study, and those excluded due to hypertension and/or diabetes, thus the results obtained in this study may not be extrapolated to the general Mexican population. Also, participants identified as having hypertension and/or diabetes were not aware of their having these conditions, thus they had not changed their diet based on this finding at the time of assessments.
CONCLUSION
The results of this study showed an association of BMI, WC, and DBP with adherence to the DASH dietary pattern as measured by an index based on the DASH nutrient intake goals (DASH-AI). We also observed an overall low adherence to DASH dietary recommendations, with the lowest rates of adherence observed for calcium, magnesium, potassium, and total fat. This study highlights the urgency there is to develop evidence-based dietary guidelines for the Mexican population aiming to reduce cardiovascular risk in our population.
