Abstract
In this study, the association between dietary pattern and lifestyle in predicting hypertension (HTN) among 425 overweight and obese children and adolescents aged 6 to 18 years was evaluated. In the current cross-sectional study, the lifestyle-scoring algorithm was developed considering the Iranian Health Reform Plan (HRP) criterion. HTN was defined according to standard protocols. Hierarchical linear regression models were used for the analysis. The prevalence of overweight/obesity was 5.82%. The results showed that systolic and diastolic blood pressure (SBP, DBP) had significant positive correlation with age (p < 0.001 and p < 0.001) and body mass index (BMI) (p < 0.005 and p < 0.007), respectively. Moreover, DBP had a significant correlation with fruit consumption of less than 2 servings per day versus no consumption (p = 0.014, B = 0.444), fruit consumption of more than 2 servings per day versus no consumption (p = 0.014, B = 0.480), and vegetable consumption less than 3 servings per day versus no consumption (p = 0.045, B = −0.374). Moreover, DBP had a significant correlation with fast foods/junk foods consumption of 1–2 items per week versus almost daily consumption (p = 0.047, B = −0.177). The final model could predict 32.1% of HTN by SBP and DBP (R2 = 0.32). According to our findings, lower intake of vegetables and fruits, higher amounts of fast foods, higher age and BMI could be potent predictors of high blood pressure among Iranian children and adolescents.
Keywords: Hypertension, Children, Adolescents, Obesity
INTRODUCTION
Childhood obesity has been considered as one of the most serious public health problems in the world [1]. The increasing prevalence of childhood obesity with numerous obesity-related comorbidities not only menace the health of those affected but also put pressure on the health care system [2]. Hypertension (HTN) is one of the most important health problems among obese children and adolescents [3]. The prevalence of HTN in obese children ranges from 19% to 22%, compared to 4% to 6% in normal weight children [4]. The prevalence of HTN among Iranian children and adolescents was estimated to be 8.9% in overall [5]. Several other studies also reported higher prevalence of HTN among Iranian children and adolescents [6,7]. HTN in childhood is associated with cardiovascular disease in adulthood [5]. Studies showed that HTN is significantly associated with the combination of genetic, environment, behavioral, and dietary factors [8]. Menghetti et al. [9] demonstrated that obese children and adolescents had four times higher risk of developing HTN than normal weight children. Their cross-sectional study among 2,007 healthy children and adolescents revealed lower physical activity, higher hours spent for television and/or computer, higher rate of unhealthy diet eating behaviors, and lower fruit and salad consumption among obese hypertensive children [9]. The National Heart, Lung, and Blood Institute's (NHLBI) Growth and Health Study, which followed 2,185 females over 10 years of age, demonstrated that consuming more than two servings of dairy and more than 3 servings of fruits and vegetables daily is associated with lower blood pressure (BP) in childhood and a 36% lower risk of developing high BP by young adulthood [10]. Similar relationships in 2,440 youth aged 10 to 22 years with type 1 diabetes mellitus in the SEARCH for Diabetes in Youth Study demonstrated an inverse association between healthy diet and BP [11].
Since the 1970s, the NHLBI has recommended measurement of BP in healthy children as a part of their routine health maintenance [12]. Preventive programs combining dietary modifications alongside higher physical activity could serve beneficial effects against systolic blood pressure (SBP) [13]. The Iranian Health Reform Plan (HRP) aiming to improve Iranians' health systems with the responsibility of the Ministry of Health and Medical Education (MOHME) was launched on May 15, 2014, with three main approaches: financial protection of the people, creating justice in access to health care services, and improving the quality of health care services. Most studies on the HRP have focused on the effectiveness of the HRP and its effects on access to health services, as well as the economic impact of HRP [14,15]. Evaluation of the nutritional status was performed by obtaining anthropometric information, demographic factors, dietary intakes, and lifestyle patterns. These parameters constituted to a part of lifestyle-scoring algorithm including nutritional and lifestyle associated risk factors. In the current report, we assessed the predictors of childhood HTN according to the lifestyle-scoring algorithm using data from Iranian HRP among children and adolescents [16].
MATERIALS AND METHODS
Study and participant's characteristics
The present study is a cross-sectional evaluation of data from HRP. The preliminary data were obtained from the Integrated Health System, known as SIB System (https://sib.tbzmed.ac.ir/home/), from 2018 September 1 to 2019 January 30. Accordingly, anthropometric information, demographic factors, dietary intakes, and lifestyle-associated factors in 425 overweight and obese children and adolescents (246 males and 179 females) referred to Shahid Bakeri Health Center in Tabriz, Iran were collected. The center is a large primary and secondary health care referral system involving the population of four districts with different socioeconomic status and an acceptable representativeness of general population. The inclusion criteria were: aged 6 to 18 years and being overweight or obese. The exclusion criteria were any renal or liver disorders, diabetes, heart disease, thyroid dysfunctions, pregnancy, smoking, and taking steroid medications.
Demographic, anthropometric and BP measurements
Demographic information and detailed diet history were recorded by an expert dietitian via direct face-to-face interviews with the participants and their parents. Weight was measured to the nearest 100 g (Seca GmbH & Co., Hamburg, Germany). Height was measured using a stadiometer (with a precision of 0.1 cm) and body mass index (BMI) was calculated as weight in kilograms divided by height in meters squared. SBP and diastolic blood pressure (DBP) were measured on left arm at the heart level after at least 5 minutes of resting in sitting position. The mean of two recordings was assigned as final BP value. High BP was defined according to the criteria presented by the fourth report on the diagnosis, evaluation, and treatment of high BP in American children and adolescents by gender-specific BP tables [17]. Accordingly, SBP and DBP < 90 percentile considered as normal, ≥ 90 percentile and < 95 percentile as prehypertension, and ≥ 95 percentile considered as hypertension. Since the childhood overweight and obesity criteria are different from adults, we used the World Health Organization (WHO) criteria for identifying overweight and obesity in people aged 5–18 years old. According to these criteria, BMI for age more than 2 standard deviations (SDs) from the middle of growth standards were considered as obese and more than 1 SD from the middle of growth standards were considered as overweight.
Lifestyle-scoring algorithm
In the lifestyle-scoring algorithm, the HRP criterion was used, which considers the usual dietary intake of different food groups, dietary habits, physical activity, as well as anthropometric measurements and BMI using a questionnaire. The lifestyle-scoring criteria of children and adolescents in the HRP are different from the adult criteria. The questionnaire included information about dietary consumption of fruits, vegetables, dairy products, the daily meals' frequency, fast food/junk food consumption, physical activity, and hours of TV/PC watching. Food intakes were measured based on serving size according to the food pyramid. Each serving of fruits is equal to a medium-sized fruit (e.g., apple, banana, orange, or pear), ½ cup of small fruits such as berries and grapes, or ½ cup of cooked fruit, or ¼ cup of dried fruit, or ½ cup of fresh and natural fruit juice, or their exchanges. Each serving of vegetables is equal to a cup of raw leafy vegetables (spinach and lettuce), or ½ cup of cooked vegetables, green peas, green beans and chopped carrots, or a medium tomato or carrot, or their exchanges. Each serving of dairy is equal to one glass (250 mL) of milk or yogurt or 45–60 g cheese or their exchanges [18]. The questionnaire contained seven questions with Likert scoring pattern. The total scores of all questions are summed up and the final score ranges between 0 to 14. The details of scoring procedure are presented in Table 1. We used a researcher-designed questionnaire, the validity of which was verified by the internal evaluation software presented by the MOHME. The questionnaire was sent to the experts to receive their opinions in classifying and scoring, which was finally approved [19].
Table 1. The life style algorithm scoring details in the health care reform plan.
| Question | Food group | Score | |
|---|---|---|---|
| 1 | Daily fruit consumption | No consumption or occasionally consumption | 0 |
| < 2 serving | 1 | ||
| ≥ 2 serving | 2 | ||
| 2 | Daily vegetable | No consumption or occasionally consumption | 0 |
| < 3 serving | 1 | ||
| ≥ 3 serving | 2 | ||
| 3 | Dairy product consumption | No consumption or occasionally consumption | 0 |
| < 3 serving | 1 | ||
| ≥ 3 serving | 2 | ||
| 4 | Daily fast foods/junk foods consumption | Almost every day | 0 |
| One or two such items a week | 1 | ||
| Rarely (repetitively less than the weekly) | 2 | ||
| 5 | Frequency of daily meal consumption (e.g., main meals and snacks) | ≤ 2 | 0 |
| 3–4 meals | 1 | ||
| ≥ 5 meals | 2 | ||
| 6 | TV/PC using time | > 2 hours | 0 |
| About 2 hours | 1 | ||
| < 2 hours | 2 | ||
| 7 | Physical activity per week (defining as 60 minutes of moderate and severe physical activity every time and divided in different days of the week) | No targeted physical activity in the week | 0 |
| < 420 minutes/week | 1 | ||
| ≥ 420 minutes/week | 2 | ||
Statistical analysis
The data were expressed as mean ± standard error (SE) and frequency (%) for quantitative and qualitative variables, respectively (nominal or ordinal). The BMI was categorized as quartile as follows: BMI < 22.20, 22.20–25.22, 25.22–28.79 and ≥ 28.79 kg/m2. Normality of data was confirmed based on three criteria: SD less than half of the mean, Kurtosis in the range of ± 3, and skewness in the range of ± 1.5. Data analysis was performed using statistical software STATA (MP 4.2 Portable 2017; StataCorp LLC, College Station, TX, USA). Univariate and multivariable hierarchical linear regression models were used for the association between parameters. In hierarchical multivariable linear regression analysis, three models were applied according to the R-squared value of each studied group. The group of food habits (daily consumption of dairy products, fruits, vegetables, fast food consumption, and the number of meals and snacks) had R2 of 0.0348; the group considering demographic factors including gender, age, BMI, and obesity or overweight status had R2 of 0.0277; and the group of physical activity and watching TV/PC had R2 of 0.0281. Accordingly, the models' priority for entrance to the hierarchical multivariable linear regression analysis were as follows: the first model considered demographic factors; second model considered factors in the first model with additional adjustment for the food habits; and the third model considered first and second model with further adjustment for physical activity and TV/PC watching.
Ethical approval and consent to participate
Each participant was completely informed about the study protocol and provided a written informed consent form before taking part in the study. The study protocol was approved and registered by the ethics committee of Tabriz University of Medical Sciences, Iran (Ethics number: IR.TBZMED.REC.1398.840). Written informed consent was obtained from all parents of the children before participation in the study. For participants above 16, written informed consent was obtained.
RESULTS
Demographic and anthropometric characteristics and the mean SBP and DBP in participants are presented in Table 2. The characteristics of the participants are presented in Table 3. Among 425 participants (246 boys and 179 girls), 86.12%, 9.41%, and 4.47% had normal BP, prehypertension, and HTN, respectively. Total prevalence of prehypertension and HTN was 19.88%.
Table 2. Demographic and anthropometric characteristics and the mean SBP and DBP in participants.
| Variable | Mean ± SD | Min–Max |
|---|---|---|
| Age | 10.62 ± 3.46 | 5–16 |
| Weight | 59.77 ± 2.27 | 19–133 |
| Height | 148 ± 18.9 | 105–187 |
| BMI | 25.76 ± 4.53 | 17.23–41.05 |
| DBP | 62.31 ± 9.45 | 40–100 |
| SBP | 100.14 ± 13.0 | 70–160 |
BMI, body mass index; DBP, diastolic blood pressure; SBP, systolic blood pressure; SD, standard deviation.
Table 3. General characteristics of the participants.
| Variables | Subgroups | Total | Boys | Girls |
|---|---|---|---|---|
| Number of participants | 425 | 246 | 179 | |
| BMI quartiles* | 1st | 106 (24.94) | 64 (26.02) | 42 (23.46) |
| 2nd | 105 (24.71) | 64 (26.02) | 41 (22.91) | |
| 3rd | 105 (24.71) | 54 (21.95) | 51 (28.49) | |
| 4th | 106 (24.94) | 62 (25.20) | 43 (24.02) | |
| Blood pressure | Normal | 366 (86.12) | 208 (84.55) | 157 (87.71) |
| Prehypertension | 40 (9.41) | 27 (10.98) | 13 (7.26) | |
| Hypertension | 19 (4.47) | 10 (4.07) | 9 (5.03) | |
| Fruit | Rarely/never | 5 (1.18) | 3 (1.22) | 2 (1.12) |
| < 2 serving/day | 70 (16.47) | 37 (15.04) | 32 (17.88) | |
| ≥ 2 serving/day | 350 (82.35) | 205 (83.33) | 145 (81.01) | |
| Vegetable | Rarely/never | 8 (1.88) | 4 (1.63) | 4 (2.23) |
| < 3 serving/day | 126 (29.65) | 65 (26.42) | 60 (33.52) | |
| ≥ 3 serving/day | 291 (68.47) | 176 (71.54) | 115 (64.25) | |
| Dairy product | Rarely/never | 6 (1.41) | 2 (0.81) | 3 (1.68) |
| < 3 serving/day | 104 (24.47) | 49 (19.92) | 55 (30.73) | |
| ≥ 3 serving/day | 315 (74.12) | 194 (78.86) | 121 (67.60) | |
| Fast foods/junk foods | Almost every day | 37 (8.71) | 15 (6.10) | 21 (11.73) |
| 1–2 items/week | 103 (24.24) | 67 (27.24) | 36 (20.11) | |
| Rarely | 285 (67.06) | 163 (66.26) | 122 (68.16) | |
| Meals/snacks | ≤ 2 meals/day | 12 (2.82) | 4 (1.63) | 8 (4.47) |
| 3–4 meals/day | 288 (67.76) | 138 (56.10) | 89 (49.72) | |
| ≥ 5 meals/day | 185 (43.53) | 103 (41.87) | 82 (45.81) | |
| Watching TV/PC | > 2 hours/day | 171 (40.24) | 100 (40.65) | 70 (39.11) |
| 2 hours/day | 88 (20.71) | 54 (21.95) | 34 (18.99) | |
| < 2 hours/day | 166 (39.06) | 91 (36.99) | 75 (41.90) | |
| Physical activity | Without targeted physical activity | 72 (16.94) | 30 (12.20) | 41 (22.91) |
| < 420 minutes/week | 182 (42.82) | 105 (42.68) | 77 (43.02) | |
| ≥ 420 minutes/week | 171 (40.24) | 110 (44.72) | 61 (34.08) |
Values are presented as number of participants (%).
BMI, body mass index.
*BMI was categorized as quartile as follows: 1st quartile, < 22.20 kg/m2; 2nd quartile, 22.20–25.22 kg/m2; 3rd quartile, 25.22–28.79 kg/m2; and 4th quartile, ≥ 28.79 kg/m2.
Table 4 presents the mean SBP and DBP values according to the lifestyle-scoring algorithm. In males, elevated SBP and DBP were associated with increased consumption of fruits and vegetables, while reduced SBP and DBP were in parallel of reduced consumption of fast foods. Accordingly, reduced SBP values were associated with reduced TV/PC watching. In females, reduced SBP and DBP values were associated with reduced consumption of fast foods and TV/PC watching, and increased physical activity. Moreover, reduced SBP was associated with increased vegetables consumption. Table 5 shows the correlation between SBP and DBP with underlying variables, lifestyle, and dietary intake using the Pearson and Spearman correlation analysis. The results showed that, by examining each of the predictors separately, SBP and DBP had a positive correlation with BMI and physical activity and an inverse correlation with watching TV/PC (p < 0.05). Also, DBP had a positive correlation with consumption of fruits (p < 0.05).
Table 4. The mean SBP and DBP in participants according to lifestyle-related factors.
| Lifestyle factors | Boys (n = 246) | Girls (n = 179) | |||||
|---|---|---|---|---|---|---|---|
| No. | SBP | DBP | No. | SBP | DBP | ||
| Fruit | |||||||
| Rarely/never | 3 | 86.66 ± 5.77 | 46.66 ± 5.77 | 2 | 100 ± 0 | 60 ± 0 | |
| < 2 serving/day | 38 | 97.36 ± 13.54 | 60 ± 10.06 | 32 | 101.56 ± 11.46 | 63.87 ± 10.31 | |
| ≥ 2 serving/day | 205 | 100.78 ± 14.35 | 63.12 ± 9.65 | 145 | 99.77 ± 10.88 | 61.79 ± 8.47 | |
| Vegetable | |||||||
| Rarely/never | 4 | 97.5 ± 17.07 | 60.25 ± 15 | 4 | 103.75 ± 7.5 | 62.5 ± 5 | |
| < 3 serving/day | 66 | 97.57 ± 15.22 | 60.30 ± 1.14 | 60 | 103.41 ± 12.02 | 64.4 ± 9.40 | |
| ≥ 3 serving/day | 176 | 101.19 ± 13.78 | 63.23 ± 9.59 | 115 | 98.23 ± 10 | 60.95 ± 8.37 | |
| Dairy product | |||||||
| Rarely/never | 3 | 90 ± 0 | 56.66 ± 5.77 | 3 | 103.33 ± 5.77 | 63.33 ± 5.77 | |
| < 3 serving/day | 49 | 103.16 ± 13.52 | 64.38 ± 9.1 | 55 | 102.38 ± 11.02 | 64.07 ± 8.29 | |
| ≥ 3 serving/day | 194 | 99.56 ± 14.43 | 62.03 ± 10.05 | 121 | 99.06 ± 10.79 | 61.32 ± 8.97 | |
| Fast foods/junk foods | |||||||
| Almost every day | 16 | 101.25 ± 14.88 | 60.53 ± 11.57 | 21 | 102.38 ± 11.02 | 64.28 ± 11.64 | |
| 1–2 items/week | 67 | 100.52 ± 12.09 | 62.66 ± 60.71 | 36 | 101.44 ± 13.55 | 62.88 ± 9.37 | |
| Rarely | 163 | 99.90 ± 15.07 | 62.48 ± 1018 | 122 | 99.30 ± 10 | 61.55 ± 8.03 | |
| Meals/snacks | |||||||
| ≤ 2 meals/day | 4 | 105 ± 17.32 | 65 ± 17.32 | 8 | 105 ± 9.25 | 65 ± 9.25 | |
| 3–4 meals/day | 139 | 100.39 ± 14.07 | 62.19 ± 9.70 | 89 | 99.74 ± 10.83 | 61.11 ± 8.97 | |
| ≥ 5 meals/day | 103 | 99.66 ± 14.50 | 62.66 ± 9.87 | 82 | 100 ± 11.16 | 62.98 ± 8.49 | |
| Watching TV/PC | |||||||
| > 2 hours/day | 101 | 102.02 ± 13.08 | 62.97 ± 9.62 | 70 | 101.25 ± 11.53 | 63.57 ± 9.33 | |
| 2 hours/day | 54 | 97.03 ± 12.42 | 60.37 ± 9.85 | 34 | 100.29 ± 10.51 | 61.32 ± 6.43 | |
| < 2 hours/day | 91 | 99.94 ± 16.31 | 63.07 ± 10.10 | 75 | 99.13 ± 10.57 | 61.18 ± 9.11 | |
| Physical activity | |||||||
| Without targeted physical activity | 31 | 93.06 ± 13.27 | 57.58 ± 10.15 | 41 | 101.70 ± 9.19 | 63.29 ± 9.32 | |
| < 420 minutes/week | 105 | 102.38 ± 15.56 | 64.38 ± 11.41 | 77 | 101.58 ± 12.41 | 62.85 ± 8.71 | |
| ≥ 420 minutes/week | 110 | 100.04 ± 12.54 | 62 ± 7.48 | 61 | 97.13 ± 9.41 | 60.47 ± 7.40 | |
Values are presented as mean ± standard error.
SBP, systolic blood pressure; DBP, diastolic blood pressure.
Table 5. Univariate correlations between SBP and DBP with life style related factors in obese children and adolescents.
| Predictor variables | SBP (mmHg) | DBP (mmHg) | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Coefficient | p | 95% CI | R2 | Coefficient | p | 95% CI | R2 | ||
| BMI* | |||||||||
| BMI quartile 1 | Referent | ||||||||
| BMI quartile 2 | 3.93 | 0.014 | 0.79, 7.06 | 0.2080 | 1.50 | 0.197 | −0.78, 3.78 | 0.2040 | |
| BMI quartile 3 | 9.50 | < 0.001 | 6.81, 13.08 | 7.07 | < 0.001 | 4.79, 9.35 | |||
| BMI quartile 4 | 15.51 | < 0.001 | 12.39, 18.64 | 10.55 | < 0.001 | 8.28, 12.83 | |||
| Overweight/obesity | |||||||||
| Overweight | Referent | ||||||||
| Obesity | 1.24 | 0.430 | −1.84, 4.32 | 0.0015 | 0.54 | 0.636 | −1.70, 2.78 | 0.0005 | |
| Nutrition screening pattern | 3.61 | 0.066 | −0.24, 7.46 | 0.0080 | 0.54 | 0.706 | −2.27, 3.35 | 0.0003 | |
| Fruit consumption | |||||||||
| Rarely or never/day | Referent | ||||||||
| < 2 serving/day | 7.28 | 0.225 | −4.84, 19.05 | 0.0058 | 9.77 | 0.025 | 1.24, 18.29 | 0.0153 | |
| ≥ 2 serving/day | 8.42 | 0.149 | −3.03, 19.87 | 10.57 | 0.013 | 2.27, 18.86 | |||
| Vegetable consumption | |||||||||
| Rarely or never/day | Referent | ||||||||
| Vegetable < 3 serving/day | −0.26 | 0.955 | −9.56, 9.03 | 0.0002 | −0.24 | 0.943 | −7.01, 6.52 | 0.0000 | |
| Vegetable ≥ 3 serving/day | −0.60 | 0.897 | −9.74, 8.54 | −0.16 | 0.962 | −6.81, 6.49 | |||
| Dairy consumption | |||||||||
| Rarely or never/day | Referent | ||||||||
| < 3 serving/day | 5.97 | 0.227 | −4.66, 16.61 | 0.0128 | 4.22 | 0.284 | −3.51, 11.96 | 0.0138 | |
| ≥ 3 serving/day | 2.70 | 0.611 | −7.74, 13.14 | 1.73 | 0.655 | −5.86, 9.32 | |||
| Fast foods/junk foods consumption | |||||||||
| Almost every day | Referent | ||||||||
| 1–2 items/week | −1.04 | 0.673 | −5.92, 3.83 | 0.0033 | −0.08 | 0.965 | −3.63, 3.47 | 0.0012 | |
| Rarely | −2.24 | 0.322 | −6.69, 2.20 | −0.75 | 0.650 | −3.99, 2.49 | |||
| Meals/snacks | |||||||||
| ≤ 2 meals or snacks/day | Referent | ||||||||
| 3–4 meals or snacks/day | −4.85 | 0.206 | −12.39, 2.67 | 0.0043 | −3.22 | 0.248 | −8.71, 2.25 | 0.0053 | |
| ≥ 5 meals or snacks/day | −5.18 | 0.179 | −12.77, 2.39 | −2.18 | 0.436 | −7.70, 3.32 | |||
| Nutritional pattern rating | −2.90 | 0.268 | −0.80, 0.24 | 0.0029 | −0.06 | 0.740 | −0.43, 0.31 | 0.0003 | |
| Watching TV/PC | |||||||||
| > 2 hours/day | Referent | ||||||||
| About 2 hours/day | −3.32 | 0.050 | 6.65, 0.004 | 0.0120 | −2.47 | 0.045 | −4.90, −0.05 | 0.0095 | |
| < 2 hours/day | −2.04 | 0.147 | −4.80, 0.72 | −0.99 | 0.333 | −3.00, 0.01 | |||
| Physical activity | |||||||||
| No physical activity | Referent | ||||||||
| < 420 minutes/week | 4.05 | 0.024 | 0.53, 7.75 | 0.0171 | 2.87 | 0.028 | 0.31, 5.43 | 0.0169 | |
| ≥ 420 minutes/week | 1.01 | 0.573 | −2.53, 4.57 | 0.622 | 0.636 | −1.96, 3.20 | |||
Bold styled values indicate the statistically significnace.
BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure.
*BMI was categorized as quartile as follows: quartile 1, <22.20 kg/m2; quartile 2, 22.20–25.22 kg/m2; quartile 3, 25.22–28.79 kg/m2; and quartile 4, ≥ 28.79 kg/m2.
Table 6 presents the predictors of BP among overweight and obese children and adolescents according to their lifestyle-associated factors in three models with hierarchical analysis. In the first model containing gender, age, BMI, and obesity status, SBP and DBP had significant correlations with age and BMI directly, and this model could predict 28.1% of HTN by increased SBP (R2 = 0.281) and 27.5% of HTN by increased DBP (R2 = 0.275). The second model included the consumption of fruits, vegetables, dairy products, fast foods/junk foods, meals or snacks, and nutritional screening score in addition to the variables included in the first model. The results showed that SBP and DBP had significant associations with age and BMI. Moreover, DBP had a direct association with fruits consumption. This model could predict 30.7% of HTN by increased SBP (R2 = 0.307) and 30.3% of HTN by increased DBP (R2 = 0.303). In the third model, which included watching TV/PC and doing physical activity in addition to the variables included in the second model, SBP had a significant positive correlation with age (p < 0.001, B = 0.344) and BMI (p = 0.005, B = 0.212). Also, DBP had a significant association with age (p < 0.001, B = 0.327), BMI (p = 0.007, B = 0.207), fruits consumption < 2 servings per day versus no consumption (p = 0.014, B = 0.444), fruits ≥ 2 servings versus no consumption (p = 0.014, B = 0.480), vegetables consumption < 3 servings versus no consumption (p = 0.045, B = −0.374), vegetables consumption ≥ 3 servings versus no consumption (p = 0.038, B = 0.439), respectively. DBP had also significant correlations with fast foods/junk foods consumption of 1-2 items per week versus almost daily consumption (p = 0.047, B = −0.177). This final model could predict 32.1% of HTN (R2 = 0.321). The results of the present study generally showed that although SBP and DBP levels in children and adolescents were associated with dietary intake and lifestyle, increased age and BMI can significantly increase SBP and DBP in the analysis based on three hierarchical multivariable linear regression model.
Table 6. Blood pressure predictors of obesity and overweight children and adolescents according to lifestyle (food intake and physical activity).
| Model | Predictor variable | SBP (mmHg) | DBP (mmHg) | ||||
|---|---|---|---|---|---|---|---|
| p | B | R2 | p | B | R2 | ||
| 1 | Sex | 0.754 | −0.130 | 0.281 | 0.537 | −0.025 | 0.275 |
| Age | < 0.001 | 0.354 | < 0.001 | 0.356 | |||
| BMI | 0.005 | 0.213 | 0.007 | 0.205 | |||
| Overweight | Referent | ||||||
| Obesity | 0.747 | −0.016 | 0.547 | −0.031 | |||
| 2 | Sex | 0.538 | −0.026 | 0.307 | 0.286 | −0.045 | 0.303 |
| Age | < 0.001 | 0.356 | < 0.001 | 0.342 | |||
| BMI | 0.005 | 0.215 | 0.007 | 0.208 | |||
| Overweight | Referent | ||||||
| Obesity | 0.761 | −0.015 | 0.545 | −0.013 | |||
| Fruit consumption rarely or never/day | Referent | ||||||
| Fruit < 2 serving/day | 0.142 | 0.259 | 0.009 | 0.465 | |||
| Fruit ≥ 2 serving/day | 0.059 | 0.334 | 0.003 | 0.533 | |||
| Vegetable consumption rarely or never/day | Referent | ||||||
| Vegetable < 3 serving/day | 0.404 | −0.147 | 0.159 | −2.250 | |||
| Vegetable ≥ 3 serving/day | 0.345 | −0.168 | 0.149 | −0.258 | |||
| Dairy consumption rarely or never/day | Referent | ||||||
| Dairy < 3 serving/day | 0.549 | 0.145 | 0.463 | 0.144 | |||
| Dairy ≥ 3 serving/day | 0.692 | 0.079 | 0.828 | 0.043 | |||
| Fast foods/junk foods consumption almost every day | Referent | ||||||
| Fast foods/junk foods 1–2 items/week | 0.085 | −0.135 | 0.090 | −0.134 | |||
| Fast foods/junk foods rarely | 0.055 | −0.174 | 0.081 | −0.159 | |||
| ≤ 2 meals or snacks/day | Referent | ||||||
| 3–4 meals/day | 0.659 | −0.058 | 0.597 | −0.070 | |||
| ≥ 5 meals/day | 0.306 | −0.144 | 0.424 | −0.089 | |||
| Nutritional pattern rating | 0.796 | 0.022 | 0.656 | 0.038 | |||
| 3 | Sex | 0.537 | −0.026 | 0.321 | 0.248 | −0.049 | 0.321 |
| Age | < 0.001 | 0.344 | < 0.001 | 0.327 | |||
| BMI | 0.005 | 0.2121 | 0.007 | 0.207 | |||
| Overweight | Referent | ||||||
| Obesity | 0.723 | −0.018 | 0.459 | −0.039 | |||
| Fruit consumption rarely or never/day | |||||||
| Fruit < 2 serving/day | 0.154 | 0.256 | 0.014 | 0.444 | |||
| Fruit ≥ 2 serving/day | 0.096 | −0.324 | 0.014 | 0.480 | |||
| Vegetable consumption rarely or never/day | Referent | ||||||
| Vegetable < 3 serving/day | 0.217 | −0.229 | 0.045 | −0.374 | |||
| Vegetable ≥ 3 serving/day | 0.189 | −0.277 | 0.038 | −0.439 | |||
| Dairy consumption rarely or never/day | Referent | ||||||
| Dairy < 3 serving/day | 0.432 | 0.169 | 0.525 | 0.130 | |||
| Dairy ≥ 3 serving/day | 0.719 | 0.081 | 0.972 | −0.007 | |||
| Fast foods/junk foods consumption almost every day | Referent | ||||||
| Fast foods/junk foods 1–2 items/week | 0.080 | −0.156 | 0.047 | −0.177 | |||
| Fast foods/junk foods rarely | 0.118 | −0.212 | 0.060 | −0.255 | |||
| ≤ 2 meals or snacks/day | Referent | ||||||
| 3–4 meals/day | 0.423 | −0.118 | 0.262 | −0.165 | |||
| ≥ 5 meals/day | 0.237 | −0.217 | 0.204 | −0.233 | |||
| Nutritional pattern rating | 0.724 | 0.103 | 0.344 | 0.277 | |||
| Watching TV/PC > 2 hours/day | Referent | ||||||
| Watching TV/PC about 2 hours/day | 0.169 | −0.093 | 0.059 | −0.128 | |||
| Watching TV/PC < 2 hours/day | 0.786 | 0.034 | 0.479 | −0.089 | |||
| Does not any targeted physical activity | Referent | ||||||
| Physical activity < 420 minutes/week | 0.169 | 0.116 | 0.329 | 0.081 | |||
| Physical activity ≥ 420 minutes/week | 0.880 | 0.020 | 0.622 | −0.066 | |||
The test of Hierarchical regression was performed. Bold styled values indicate the statistically significance.
BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure.
DISCUSSION
Pediatric HTN is undergoing a major shift from secondary HTN (mostly caused by renal disease) to essential HTN (as the main cause of HTN in childhood and adolescence) [20]. The factors related to primary HTN in children were different for increased SBP and DBP. In the current report, we evaluated the important predictors of HTN in children and adolescents and found that age and BMI were the main predictors of SBP, while age, BMI, higher intakes of fruits, vegetables, and fast foods were in positive and significant association with DBP. The univariate analysis showed a significant correlation between adiposity indicators and sedentary lifestyle (lower physical activity and spent much time for TV/PC watching) with SBP and DBP, whereas higher intake of fruits was also associated with DBP.
In this study, we used a hierarchical linear multivariable regression model to examine the effect of each of the predictors in a model on other variables. Several other reports also confirmed the main determinant role of BMI in HTN development. Falkner et al. [21] examined electronic medical record data from primary care practices on 18,618 children aged 2 to 19 years and showed that 16.7% of children and adolescents were at risk of overweight and 20.2% were overweight. They also demonstrated that there was a significant association between increasing BMI with SBP and DBP in children and adolescents [21]. Dong et al. [22] also reported that increased prevalence of HTN was associated with higher BMI among 943,128 participants aged 7 to 17 years. Similar results were also observed in the Zeberio et al. study, indicating a positive association between SBP and BMI in school-aged children [23]. Several mechanisms have been suggested for the association between increasing BMI with HTN development. Sorof et al. [24] showed that increased carotid artery intima-media thickness (C-IMT) is associated with HTN in children. Studies show that there is a significant independent predicting role of CRP in C-IMT and flow-mediated vasodilation in children [25]. Ogawa et al. [26] also showed that higher concentrations of circulating inflammatory cytokines associated with the atherosclerotic process and C-reactive protein (CRP) was one of the most susceptible indicators of HTN in obese Japanese children.
Similar to our results, several studies have also demonstrated that a rise in the BP is associated with increased age in children and adolescents [23,27]. Several reports also indicated the possible role of puberty as an important determinant in the association between obesity and HTN among adolescents, especially females. This might also explain the increased prevalence of HTN with increased age [28]. Barba et al. [27] reported that in the period close to the completion of puberty, the association between age and BP becomes more evident among females. In the study by Oliveros et al. [29], the prevalence of HTN and stage 2 of HTN was higher among younger children compared with older ones, while stage 1 HTN was more prevalent among older children. It seems that the age range and the presence of puberty hormones in girls and boys is one of the reasons for conflicting results in different studies.
Among the dietary factors influencing the nutritional pattern score in the current study, a positive correlation between HTN and increased consumption of fruits groups and fast food/junk food consumption, and an inverse correlation between HTN and vegetables groups was observed. Increased fast food consumption containing high amounts of salt, sugar, and fat [30] is associated with increased obesity state [31,32]. Studies showed that high levels of fats, sugar, and salt intake in fast foods are one of the possible reasons of increased BP in children and adolescents [33,34,35]. The possible role of increased salt and sodium consumption in the pathogenesis of HTN has been clarified. Recently, the role of other dietary factors, food patterns, and lifestyle habits in increasing BP has also been a focus of interest. Stamler et al. [36] showed that in addition to sodium, several other nutrients, including calcium, magnesium, potassium, and fiber are also involved in the pathogenesis of HTN. One meta-analysis of 56 studies on the effect of sodium restriction on BP showed that sodium restriction could be beneficial among elderly individuals with HTN; however, its beneficial effects were low among people with normal BP [37]. The higher sodium and energy contents of fast/junk foods are possibly an underlying reason of the association between fast food consumption and HTN.
Numerous studies have shown that higher intakes of fruits, vegetables, and dairy products could have an effective role in prevention of childhood HTN due to several nutrients, including potassium, magnesium, calcium, and fiber [38,39]. The positive association between fruits intake and HTN in our study, in contrast with several previous studies, could be attributed to the difference in study design, target group characteristics such as age or gender distribution [38], or possibly because of considering fruits and vegetables as a group with no separation [39]; however, we analyzed fruits and vegetables separately in 2 independent groups. Additionally, in the current study, 100% fruit juices were considered as fruit groups, in the foods groups pyramid, and were likely to increase BP by increasing consumption of fructose-rich fruit juice. Auerbach et al. [40] showed that consumption of more than 24 ounce per day of 100% fruit juice was associated with increased HTN risk. Also, 100% fruit juices might be associated with HTN through several possible biological mechanisms, including increased energy intake, weight gain, and uric acid production, which is also associated with elevated BP [41]. Moreover, consuming whole fruits in high amounts can increase weight due to increased energy intake. Considering that fiber is one of the most important factors in whole fruits that plays a role in BP, in our study, fruit juices and fruits were not separated. Therefore, further studies could study the effects of whole fruits and fruit juices separately.
No significant correlation was found between BP and dairy products in the current study. Yuan et al. indicated that high intake of dairy (≥ 2 servings per day) has antihypertensive effects on BP among children aged 8 to 10 years [42]. In a study by DellaValle et al. [43], greater intakes of dairy products were associated with lower SBP in white but not black children and teens, suggesting the possible role of demographic factors including race in beneficial role of dairy products. Given that, the type of dairy products in terms of low- or high-fat, as well as different types of dairy products such as milk, yogurt, cheese was not identified in our study; this may justify the discrepancy in the results, because studies show that specific types of dairy products such as low-fat milk and milk products have inverse association with hypertension and there were no association with high-fat dairy or cheese [44].
In the current report, 43.53% of participants consumed more than five meals per day; however, there was no significant relationship between frequency of meals' and HTN. Several studies showed that the prevalence of obesity declined by increased number of meals [45]. Donin et al. [46] presented that more snacks and meal consumption lead to obesity and cardiovascular problems in children. Toschke et al. [45] studied 4,370 children aged 5–6 years and found that the prevalence of obesity declines by increasing the number of meals.
In the final regression model, we did not observe any significant correlation between BP and physical activity or TV/PC watching. Torrance et al. [47] demonstrated that 40 minutes of moderate to vigorous aerobic-based physical activity 3–5 days/week is required to reduce BP in obese children. TV commercials influence the food choices of children in different ways. Using their highfalutin and vivid messages, they encourage people to buy the advertised products [48]. A program comprising screening, early detection, and health promotion through school health programs may help to prevent future complications of HTN [49]. In another study, de Moraes et al. [50] showed that maintaining sedentary behaviors during childhood increases the risk of developing HTN after 2 years of follow-up among 16,228 children 2–9 year. There is strong evidence that physical activity has a strong effect on the release of vasodilator factors produced by the vascular endothelium [51], such as nitric oxide and endothelium derived hyperpolarizing factor (EDHF) [52], and children with a physical activity of less than 60/min/d have lower vasodilation capacity of the endothelium, which could be the biological mechanism by which they develop HTN. There are multiple possible physiological mechanisms by which sedentary behaviors contribute to increased BP. Pedersen and Febbraio [53] suggested that sedentary behaviors change the myokine response in the skeletal muscle that leads to promoting the endothelial dysfunction in the cardiovascular system by increasing the pro-inflammatory adipokines. Consequently, this increment could be the start of atherosclerosis processes, and progressively develop into hypertension [53]. Although in the current study we observed a correlation between BP and TV watching and physical activity in the univariate regression, these associations had been disappeared by including several moderators into the model. It is suggested that more studies be conducted through considering the moderator's variables in this field.
Several limitations of the current study should be addressed. First, the family history of HTN among study participants was not considered. Second, the effects of other food items, including the amount of salt and fat intake were not evaluated. Finally, the socio-economic status of household, educational level for parents, and the presence of care-givers were not evaluate in this study.
CONCLUSION
According to our findings, it can be concluded that in the children and adolescent populations, pathogenesis of higher BP is influenced by age, higher BMI, and lifestyle factors like dietary intake. Therefore, by increasing the age of children, especially in overweight and obese ones, controlling their weight, encouraging the consumption of more vegetables, and recommending less fast food consumption could be a preventive strategy against HTN.
Acknowledgements
This research was part of a Ph.D thesis project, belonged to Shahnaz Taghizadeh, coded as IR.TBZMED.REC.1398.840 which has been approved by the ethics committee of Tabriz University of Medical Sciences in Tabriz, Iran (Grant number: 62918). We are grateful to the Tabriz University of Medical Science for their financial support.
Footnotes
Funding: The current study was financially supported by a grant from Tabriz University of Medical Sciences (registration number: IR.TBZMED.REC.1398.840). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Conflict of Interest: The authors declare that they have no competing interests.
References
- 1.Ogden CL, Carroll MD, Kit BK, Flegal KM. Prevalence of childhood and adult obesity in the United States, 2011–2012. JAMA. 2014;311:806–814. doi: 10.1001/jama.2014.732. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Juonala M, Magnussen CG, Berenson GS, Venn A, Burns TL, Sabin MA, Srinivasan SR, Daniels SR, Davis PH, Chen W, Sun C, Cheung M, Viikari JS, Dwyer T, Raitakari OT. Childhood adiposity, adult adiposity, and cardiovascular risk factors. N Engl J Med. 2011;365:1876–1885. doi: 10.1056/NEJMoa1010112. [DOI] [PubMed] [Google Scholar]
- 3.Friedemann C, Heneghan C, Mahtani K, Thompson M, Perera R, Ward AM. Cardiovascular disease risk in healthy children and its association with body mass index: systematic review and meta-analysis. BMJ. 2012;345:e4759. doi: 10.1136/bmj.e4759. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Salvadori M, Sontrop JM, Garg AX, Truong J, Suri RS, Mahmud FH, Macnab JJ, Clark WF. Elevated blood pressure in relation to overweight and obesity among children in a rural Canadian community. Pediatrics. 2008;122:e821–7. doi: 10.1542/peds.2008-0951. [DOI] [PubMed] [Google Scholar]
- 5.Akbari M, Moosazadeh M, Ghahramani S, Tabrizi R, Kolahdooz F, Asemi Z, Lankarani KB. High prevalence of hypertension among Iranian children and adolescents: a systematic review and meta-analysis. J Hypertens. 2017;35:1155–1163. doi: 10.1097/HJH.0000000000001261. [DOI] [PubMed] [Google Scholar]
- 6.Ebrahimi H, Emamian MH, Hashemi H, Fotouhi A. Prevalence of prehypertension and hypertension and its risk factors in Iranian school children: a population-based study. J Hypertens. 2018;36:1816–1824. doi: 10.1097/HJH.0000000000001789. [DOI] [PubMed] [Google Scholar]
- 7.Ataei N, Aghamohammadi A, Ziaee V, Hosseini M, Dehsara F, Rezanejad A. Prevalence of hypertension in junior and senior high school children in Iran. Iran J Pediatr. 2007;17:237–242. [Google Scholar]
- 8.Malik VS, Willett WC, Hu FB. Global obesity: trends, risk factors and policy implications. Nat Rev Endocrinol. 2013;9:13–27. doi: 10.1038/nrendo.2012.199. [DOI] [PubMed] [Google Scholar]
- 9.Menghetti E, Strisciuglio P, Spagnolo A, Carletti M, Paciotti G, Muzzi G, Beltemacchi M, Concolino D, Strambi M, Rosano A. Hypertension and obesity in Italian school children: the role of diet, lifestyle and family history. Nutr Metab Cardiovasc Dis. 2015;25:602–607. doi: 10.1016/j.numecd.2015.02.009. [DOI] [PubMed] [Google Scholar]
- 10.Moore LL, Bradlee ML, Singer MR, Qureshi MM, Buendia JR, Daniels SR. Dietary Approaches to Stop Hypertension (DASH) eating pattern and risk of elevated blood pressure in adolescent girls. Br J Nutr. 2012;108:1678–1685. doi: 10.1017/S000711451100715X. [DOI] [PubMed] [Google Scholar]
- 11.Günther AL, Liese AD, Bell RA, Dabelea D, Lawrence JM, Rodriguez BL, Standiford DA, Mayer-Davis EJ. Association between the dietary approaches to hypertension diet and hypertension in youth with diabetes mellitus. Hypertension. 2009;53:6–12. doi: 10.1161/HYPERTENSIONAHA.108.116665. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Expert Panel on Integrated Guidelines for Cardiovascular Health and Risk Reduction in Children and Adolescents; National Heart, Lung, and Blood Institute. Expert panel on integrated guidelines for cardiovascular health and risk reduction in children and adolescents: summary report. Pediatrics. 2011;128(Suppl 5):S213–S256. doi: 10.1542/peds.2009-2107C. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Monzavi R, Dreimane D, Geffner ME, Braun S, Conrad B, Klier M, Kaufman FR. Improvement in risk factors for metabolic syndrome and insulin resistance in overweight youth who are treated with lifestyle intervention. Pediatrics. 2006;117:e1111–8. doi: 10.1542/peds.2005-1532. [DOI] [PubMed] [Google Scholar]
- 14.Maher A, Fazel Z. The effects of Iran's Health Sector Evolution Plan on catastrophic health expenditures in 2012 and 2015 (before and after the implementation of HSEP) Health Econ Outcome Res. 2021;7:24–32. [Google Scholar]
- 15.Tabari-Khomeiran R, Delavari S, Rezaei S, Rad EH, Shahmoradi M. The effect of Iranian health system reform plan on payments and costs of coronary artery bypass surgery in private hospitals of Iran. Int J Hum Rights Healthc. 2019;12:208–214. [Google Scholar]
- 16.Abdollahi Z, Torabi P, Mazandarani FS, Gotbabadi FS, Minaei M, Haghighi FN, Fallah H. Collection of care and nutrition services in the health system transformation program in the field of health. Tehran: Ministry of Health and Medical Edication; 2016. [In Persian] [Google Scholar]
- 17.National High Blood Pressure Education Program Working Group on High Blood Pressure in Children and Adolescents. The fourth report on the diagnosis, evaluation, and treatment of high blood pressure in children and adolescents. Pediatrics. 2004;114:555–576. [PubMed] [Google Scholar]
- 18.Mahan LK, Raymond JL. Krause's food & the nutrition care process. 14th ed. St. Louis (MO): Elsevier; 2016. [Google Scholar]
- 19.Esmailzadeh H, Rajabi F, Rostamigooran N, Majdzadeh R. Iran health system reform plan methodology. Iran J Public Health. 2013;42:13–17. [PMC free article] [PubMed] [Google Scholar]
- 20.Sorof J, Daniels S. Obesity hypertension in children: a problem of epidemic proportions. Hypertension. 2002;40:441–447. doi: 10.1161/01.hyp.0000032940.33466.12. [DOI] [PubMed] [Google Scholar]
- 21.Falkner B, Gidding SS, Ramirez-Garnica G, Wiltrout SA, West D, Rappaport EB. The relationship of body mass index and blood pressure in primary care pediatric patients. J Pediatr. 2006;148:195–200. doi: 10.1016/j.jpeds.2005.10.030. [DOI] [PubMed] [Google Scholar]
- 22.Dong Y, Ma J, Song Y, Ma Y, Dong B, Zou Z, Prochaska JJ. Secular trends in blood pressure and overweight and obesity in Chinese boys and girls aged 7 to 17 years from 1995 to 2014. Hypertension. 2018;72:298–305. doi: 10.1161/HYPERTENSIONAHA.118.11291. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Zeberio N, Malpeli A, Apezteguía M, Carballo MA, González HF. Nutritional status of school-aged children and its relation to blood pressure. Arch Argent Pediatr. 2013;111:92–97. doi: 10.5546/aap.2013.eng.92. [DOI] [PubMed] [Google Scholar]
- 24.Sorof JM, Alexandrov AV, Garami Z, Turner JL, Grafe RE, Lai D, Portman RJ. Carotid ultrasonography for detection of vascular abnormalities in hypertensive children. Pediatr Nephrol. 2003;18:1020–1024. doi: 10.1007/s00467-003-1187-0. [DOI] [PubMed] [Google Scholar]
- 25.Järvisalo MJ, Harmoinen A, Hakanen M, Paakkunainen U, Viikari J, Hartiala J, Lehtimäki T, Simell O, Raitakari OT. Elevated serum C-reactive protein levels and early arterial changes in healthy children. Arterioscler Thromb Vasc Biol. 2002;22:1323–1328. doi: 10.1161/01.atv.0000024222.06463.21. [DOI] [PubMed] [Google Scholar]
- 26.Ogawa Y, Kikuchi T, Nagasaki K, Hiura M, Tanaka Y, Uchiyama M. Usefulness of serum adiponectin level as a diagnostic marker of metabolic syndrome in obese Japanese children. Hypertens Res. 2005;28:51–57. doi: 10.1291/hypres.28.51. [DOI] [PubMed] [Google Scholar]
- 27.Barba G, Casullo C, Dello Russo M, Russo P, Nappo A, Lauria F, Siani A. Gender-related differences in the relationships between blood pressure, age, and body size in prepubertal children. Am J Hypertens. 2008;21:1007–1010. doi: 10.1038/ajh.2008.228. [DOI] [PubMed] [Google Scholar]
- 28.Kar S, Khandelwal B. Fast foods and physical inactivity are risk factors for obesity and hypertension among adolescent school children in east district of Sikkim, India. J Nat Sci Biol Med. 2015;6:356–359. doi: 10.4103/0976-9668.160004. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Oliveros AM, Molinero A, Cervero M, Magro M, Ponte Y, Partearroyo T. Prevalence of hypertension and hypertension phenotypes by age and gender among schoolchildren in Spain: the Mepafac Madrid Regional Community Study. J Hypertens. 2018;36:e215 [Google Scholar]
- 30.Rudelt A, French S, Harnack L. Fourteen-year trends in sodium content of menu offerings at eight leading fast-food restaurants in the USA. Public Health Nutr. 2014;17:1682–1688. doi: 10.1017/S136898001300236X. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Alviola PA, 4th, Nayga RM, Jr, Thomsen MR, Danforth D, Smartt J. The effect of fast-food restaurants on childhood obesity: a school level analysis. Econ Hum Biol. 2014;12:110–119. doi: 10.1016/j.ehb.2013.05.001. [DOI] [PubMed] [Google Scholar]
- 32.Chou SY, Rashad I, Grossman M. Fast-food restaurant advertising on television and its influence on childhood obesity. J Law Econ. 2008;51:599–618. [Google Scholar]
- 33.Colín-Ramírez E, Castillo-Martínez L, Orea-Tejeda A, Villa Romero AR, Vergara Castañeda A, Asensio Lafuente E. Waist circumference and fat intake are associated with high blood pressure in Mexican children aged 8 to 10 years. J Am Diet Assoc. 2009;109:996–1003. doi: 10.1016/j.jada.2009.03.011. [DOI] [PubMed] [Google Scholar]
- 34.Nguyen S, Choi HK, Lustig RH, Hsu CY. Sugar-sweetened beverages, serum uric acid, and blood pressure in adolescents. J Pediatr. 2009;154:807–813. doi: 10.1016/j.jpeds.2009.01.015. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Soudarssanane M, Karthigeyan M, Stephen S, Sahai A. Key predictors of high blood pressure and hypertension among adolescents: a simple prescription for prevention. Indian J Community Med. 2006;31:164. [Google Scholar]
- 36.Stamler J, Caggiula A, Grandits GA, Kjelsberg M, Cutler JA. Relationship to blood pressure of combinations of dietary macronutrients. Findings of the Multiple Risk Factor Intervention Trial (MRFIT) Circulation. 1996;94:2417–2423. doi: 10.1161/01.cir.94.10.2417. [DOI] [PubMed] [Google Scholar]
- 37.Midgley JP, Matthew AG, Greenwood CM, Logan AG. Effect of reduced dietary sodium on blood pressure: a meta-analysis of randomized controlled trials. JAMA. 1996;275:1590–1597. doi: 10.1001/jama.1996.03530440070039. [DOI] [PubMed] [Google Scholar]
- 38.Wu L, Sun D, He Y. Fruit and vegetables consumption and incident hypertension: dose-response meta-analysis of prospective cohort studies. J Hum Hypertens. 2016;30:573–580. doi: 10.1038/jhh.2016.44. [DOI] [PubMed] [Google Scholar]
- 39.Moore LL, Singer MR, Bradlee ML, Djoussé L, Proctor MH, Cupples LA, Ellison RC. Intake of fruits, vegetables, and dairy products in early childhood and subsequent blood pressure change. Epidemiology. 2005;16:4–11. doi: 10.1097/01.ede.0000147106.32027.3e. [DOI] [PubMed] [Google Scholar]
- 40.Auerbach BJ, Littman AJ, Tinker L, Larson J, Krieger J, Young B, Neuhouser M. Associations of 100% fruit juice versus whole fruit with hypertension and diabetes risk in postmenopausal women: results from the Women's Health Initiative. Prev Med. 2017;105:212–218. doi: 10.1016/j.ypmed.2017.08.031. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41.Johnson RJ, Nakagawa T, Sanchez-Lozada LG, Shafiu M, Sundaram S, Le M, Ishimoto T, Sautin YY, Lanaspa MA. Sugar, uric acid, and the etiology of diabetes and obesity. Diabetes. 2013;62:3307–3315. doi: 10.2337/db12-1814. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42.Yuan WL, Kakinami L, Gray-Donald K, Czernichow S, Lambert M, Paradis G. Influence of dairy product consumption on children's blood pressure: results from the QUALITY cohort. J Acad Nutr Diet. 2013;113:936–941. doi: 10.1016/j.jand.2013.03.010. [DOI] [PubMed] [Google Scholar]
- 43.DellaValle DM, Carter J, Jones M, Henshaw MH. What is the relationship between dairy intake and blood pressure in black and white children and adolescents enrolled in a weight management program? J Am Heart Assoc. 2017;6:e004593. doi: 10.1161/JAHA.116.004593. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 44.Engberink MF, Hendriksen MA, Schouten EG, van Rooij FJ, Hofman A, Witteman JC, Geleijnse JM. Inverse association between dairy intake and hypertension: the Rotterdam Study. Am J Clin Nutr. 2009;89:1877–1883. doi: 10.3945/ajcn.2008.27064. [DOI] [PubMed] [Google Scholar]
- 45.Toschke AM, Küchenhoff H, Koletzko B, von Kries R. Meal frequency and childhood obesity. Obes Res. 2005;13:1932–1938. doi: 10.1038/oby.2005.238. [DOI] [PubMed] [Google Scholar]
- 46.Donin AS, Nightingale CM, Owen CG, Rudnicka AR, Cook DG, Whincup PH. Takeaway meal consumption and risk markers for coronary heart disease, type 2 diabetes and obesity in children aged 9–10 years: a cross-sectional study. Arch Dis Child. 2018;103:431–436. doi: 10.1136/archdischild-2017-312981. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 47.Torrance B, McGuire KA, Lewanczuk R, McGavock J. Overweight, physical activity and high blood pressure in children: a review of the literature. Vasc Health Risk Manag. 2007;3:139–149. [PMC free article] [PubMed] [Google Scholar]
- 48.Vijayapushpam T, Maheshwar M, Rao DR. A comparative analysis of television food advertisements aimed at adults and children in India. Int J Innov Res Sci Eng. 2014;2:476–483. [Google Scholar]
- 49.Borah PK, Devi U, Biswas D, Kalita HC, Sharma M, Mahanta J. Distribution of blood pressure & correlates of hypertension in school children aged 5–14 years from North East India. Indian J Med Res. 2015;142:293–300. doi: 10.4103/0971-5916.166591. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 50.de Moraes AC, Carvalho HB, Siani A, Barba G, Veidebaum T, Tornaritis M, Molnar D, Ahrens W, Wirsik N, De Henauw S, Mårild S, Lissner L, Konstabel K, Pitsiladis Y, Moreno LA IDEFICS consortium. Incidence of high blood pressure in children - effects of physical activity and sedentary behaviors: the IDEFICS study: High blood pressure, lifestyle and children. Int J Cardiol. 2015;180:165–170. doi: 10.1016/j.ijcard.2014.11.175. [DOI] [PubMed] [Google Scholar]
- 51.Zago AS, Zanesco A. Nitric oxide, cardiovascular disease and physical exercise. Arq Bras Cardiol. 2006;87:e264–70. doi: 10.1590/s0066-782x2006001900029. [DOI] [PubMed] [Google Scholar]
- 52.Kingwell BA. Nitric oxide-mediated metabolic regulation during exercise: effects of training in health and cardiovascular disease. FASEB J. 2000;14:1685–1696. doi: 10.1096/fj.99-0896rev. [DOI] [PubMed] [Google Scholar]
- 53.Pedersen BK, Febbraio MA. Muscles, exercise and obesity: skeletal muscle as a secretory organ. Nat Rev Endocrinol. 2012;8:457–465. doi: 10.1038/nrendo.2012.49. [DOI] [PubMed] [Google Scholar]