Dietary Correlates of Elevated Blood Pressure Among Children and Adolescents in Ibadan, Southwestern, Nigeria

Dietary Correlates of Elevated Blood Pressure Among Children and Adolescents in Ibadan, Southwestern, Nigeria | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Dietary Correlates of Elevated Blood Pressure Among Children and Adolescents in Ibadan, Southwestern, Nigeria MAGBAGBEOLA DAVID DAIRO, A. A. ALAYANDE, JOY E. WILLIAMS, PRAISE O ONUOHA, and 9 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9303561/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Background Dietary intake of foods high in fat and sugar in children and adolescents can potentially increase the risk of developing elevated blood pressure at a younger age. Studying the dietary factors associated with elevated blood pressure in children and adolescents is crucial for early detection of modifiable risk factors. This study determines the dietary correlates of elevated blood pressure among children and adolescents in Ibadan, Nigeria. Methods In a descriptive cross-sectional study, 335 adolescents were enrolled using a multi-stage sampling technique. Data collected from the respondents was analysed using Statistical Package for the Social Sciences (IBM SPSS Version 27.0) and R Studio. Descriptive statistics were used to report the findings. Principal Component Analysis was used to identify the dietary patterns. Chi-square tests, independent samples t-tests, one-way ANOVA, and regression analysis were used to test the association and relationships between variables. The significance level was set at 5% at a p-value < 0.05. Results Traditional/plant-based diet, sweetened beverage, and westernized diet were identified. Adherence to these patterns includes: low (33.1%), moderate (33.4%), and high (33.4%). Mean SBP ranges from 102.3 ± 15.3 mmHg to 104.4 ± 14.4 mmHg for the traditional/plant-based pattern, 102.3 ± 14.1 mmHg to 103.7 ± 15.8 mmHg for the sweetened beverage pattern, and 102.5 ± 16.2 mmHg to 103.4 ± 14.4 mmHg for the protein/Westernized pattern. Children and adolescents with high adherence to the traditional/plant-based diet had significantly lower odds (OR = 0.43, 95% CI: 0.20–0.92, p = 0.029) of elevated diastolic blood pressure compared to those with low adherence. Therefore, findings support the promotion of traditional, plant-based diets among adolescents and children. Conclusion While no association was observed between dietary patterns and systolic blood pressure, adherence to a traditional/plant-based diet was associated with reduced odds of elevated diastolic blood pressure, indicating a potential protective effect. Child & adolescent health blood pressure cardiovascular disease burden Nigeria Figures Figure 1 Introduction Hypertension often progresses undetected in children and adolescents, and if not diagnosed or managed, it continues into adulthood, increasing the risk for cardiovascular disease, kidney damage, stroke, and heart failure. In sub-Saharan Africa, the complications of hypertension have continued to increase at a significant rate, which unfortunately progresses through adulthood as a silent killer and leading cause of vast disability and mortality [ 12 ]. Elevated blood pressure is one of the most common health concerns in children and adolescents that poses a significant public health challenge globally, although it is less recognized. [ 14 ]. The increasing prevalence of elevated blood pressure in children and adolescents appears to be strongly tied to the increasing prevalence of obesity and sedentary lifestyles in children and adolescents. Enough evidence has confirmed that elevated blood pressure (BP) in children and adolescents can lead to negative outcomes, such as vascular damage, cardiometabolic risk, and organ damage, which increases an individual’s risk of developing hypertension in adulthood [ 13 ]. Nutrition-related hypertension prevalence has nearly tripled between 1975 and 2016, with childhood obesity being considered an important nutritional risk factor for childhood hypertension, constituting a major public health problem worldwide. [ 11 ]. By 2035, an estimated 68 million children will experience serious consequences of high blood pressure due to their high BMI, being overweight or obese [ 6 ]. Overweight and obesity lead to adverse metabolic effects on blood pressure, cholesterol, triglycerides, and insulin resistance. The dietary intake of foods containing high fat and sugar, as well as the low level of habitual physical activity in children, can potentially increase the risk of developing elevated blood pressure at a younger age, as well as behavioral patterns of adolescence [ 5 ]. Nigeria is experiencing a nutrition transition, with rapid urbanization and associated adoption of Western diets and a reduction in energy expenditure among children and adolescents. [ 1 ]. An increasing burden of hypertension in both children and adolescents is likely to be of serious consequence as a result of limited resources and inadequate health budgets in most primary healthcare settings in this region, resulting in inadequate routine screening for early detection of hypertension in children and adolescents [ 10 ]. While most hypertension cases in children and adolescents remain undetected and uncontrolled, the risk of long-term cardiovascular complications in adolescents, including early-onset heart disease and stroke, tends to rise, thereby placing a growing burden on healthcare systems and a significant threat to public health [ 13 ]. By identifying poor dietary habits early, effective interventions can be implemented to prevent the progression of hypertension into adulthood, ultimately reducing the future burden of cardiovascular diseases and reducing the cost of healthcare. Therefore, this study represents a baseline exploratory component of a larger project aimed at developing a predictive system for high blood pressure, and aimed to assess the dietary correlates of elevated blood pressure among children and adolescents in Ibadan, southwest Nigeria. Methodology The research was conducted in Ibadan, a metropolitan city located in Oyo State, southwestern Nigeria. As the administrative capital of the state, Ibadan serves as a major hub for politics and economics. The city is highly urbanized and densely populated, ranking among the largest metropolitan areas in Nigeria. According to national population data [ 8 ]. Ibadan has an estimated metropolitan population approaching four million inhabitants. Administratively, Ibadan is structured into eleven local government areas, comprising five predominantly urban local governments and six that are largely rural in character. Study design and duration : This study employed a community-based descriptive cross-sectional study design, which was conducted between the period of May and November 2025 among children and adolescents aged 8 to 14 years residing in selected communities in both rural and urban settlements of Ibadan during the period of the study. The study was conducted in four LGAs and included twelve communities: Siba, Yemetu, Oke Adu, Adeoyo, Sango, Agbowo, Sapati, Kute, Olowu, Oke Aremu, Inalende, and Opoo. Sample Size Determination A total of 335 children and adolescents were recruited for this study. Cochran's formula n= [ (Z) 2 [P(1-P)]/d 2 for estimating the sample size for a single proportion was used, with a prevalence of 26.7%, adapted from [ 4 ]. Where: n = required sample size z = standard normal deviate (1.96 for 95% confidence level) p = expected prevalence d = margin of error (0.05) Sampling technique The eligible participants were selected using a multistage sampling technique. In the first stage, a list of all local governments in Ibadan was obtained, which comprises 11 local government areas: Ibadan North, Ibadan North-West, Ibadan North-East, Ibadan South-West, Ibadan South-East, Akinyele, Lagelu, Egbeda, Ona-Ara, Oluyole, and Ido. The local government was stratified into urban, comprising Ibadan North, Ibadan North-west, Ibadan North-East, Ibadan South-West, Ibadan South-East, and rural LGAs, comprising Ido, Lagelu, Egbeda, Ona-Ara, Akinyele, and Oluyole. From each stratum, two urban LGAs (Ibadan north and Ibadan north-west) and two rural LGAs (Ido and Lagelu) were randomly selected via balloting. In each selected LGA, communities such as Yemetu, Adeoyo, Agbowo, Sango in Ibadan North, Opoo, in Ibadan North-West, Oke Adu, Oke Aremu, Sapati, Kute, Olowu in Lagelu, and Siba in Ido were purposely selected based on the accessibility of children and adolescents and population distribution. In the second stage, A house-to-house survey was conducted in the selected communities. Systematic sampling was used to select households. A sampling interval was determined with a random starting point, and every third household was selected until the required sample size was achieved. Lastly, in households with more than one eligible child and adolescent, simple random sampling via balloting was used to select up to two children or adolescents per household. Each eligible child or adolescent’s name or number was placed in a lottery, and a child or adolescent was selected by balloting. This ensured wider representation across households while preventing over-representation from any single household. Data Collection Instrument Data was collected using a pre-tested, semi-structured, interviewer-administered questionnaire designed to obtain information on the sociodemographic characteristics and to assess the dietary patterns of children and adolescents aged 8–14 years. A validated 47-item food frequency questionnaire (FFQ) adapted from [ 2 ] was used to assess the dietary patterns of the selected adolescent. The frequency of intake of six food groups was assessed, namely: roots and tubers, cereals and legumes, fruits and vegetables, meat and meat products, fats and oils, and beverages. Each food item has a choice of 11 frequencies, namely: daily (1x), daily (2times), daily (3times), weekly (1x), weekly (2times), weekly (3times), monthly (1x), monthly (2times), monthly (3times), rarely, and never. This was recategorized as daily (≥ 7/week), frequently (3–6/week), occasionally (1–2week), rarely (< 1/week), and never. The questionnaire for this study included three major sections: Socio-demographic data of the respondents, Dietary patterns of the respondents, and Blood Pressure Measurement. Blood pressure measurement was done using an “ANDON” automatic blood pressure monitor, with a curve size appropriate for children and adolescents, and measurements followed the National Blood Pressure guidelines for children and adolescents [ 7 ]. Before data collection, the questionnaire was pre-tested in Barika, a notable community in Ibadan North LGA, which was not included in the main study, to evaluate its validity and reliability. The pre-test findings demonstrated a high level of internal consistency, with a Cronbach’s alpha coefficient of 0.889, indicating that the instrument was sufficiently reliable. Furthermore, the validity of the questionnaire was assessed by experts in the field, who confirmed its appropriateness for use in the study. Data collected from the field were cleaned, entered, and analyzed using Statistical Package for the Social Sciences (IBM SPSS Version 27.0) and R Studio. Dietary patterns were summarized using Principal Component Analysis (PCA) to generate the factor scores for each dietary pattern, and data visualization using scree plots to show distributions of respondents' dietary patterns. Chi-square test was used to determine the association between dietary pattern group and BP status across Groups (Normal SBP/DBP and Elevated SBP/DBP). Independent Samples T-Test / One-Way ANOVA was used to compare mean dietary factor scores across age groups and sex with BP status. Simple linear regression analyses were carried out to determine the actual relationship between dietary pattern scores and SBP/DBP. A Binary logistic regression was further used to evaluate the relationship between dietary pattern adherence level and SBP/DBP. Confounding factors were controlled for, and the significance level was set at a p-value < 0.05 with a 95% CI. Blood Pressure Status for age, sex, and height was measured using a digital automated Blood Pressure monitor, with an appropriate cuff size based on the adolescent’s arm circumference. Two separate readings were taken at 5-minute intervals, and the averages were calculated. The dependent variable in the study was blood pressure levels, including systolic and diastolic blood pressure levels, which were further categorized into Normal and Elevated following the 2017 American Academy of Pediatrics (AAP) [ 15 ] pediatric hypertension guideline. For children aged < 13 years, blood pressure categories were defined using age-, sex-, and height-specific percentiles: normal (< 90th percentile), and elevated BP (≥ 90th to < 95th percentile). For adolescents aged ≥ 13 years, fixed thresholds were applied (normal < 120/<80 mmHg, elevated BP 120–129/<80 mmHg). Screening BP values from the AAP simplified table were used to identify participants requiring further evaluation. Participants were classified as having elevated SBP or elevated DBP if their measured SBP or DBP met or exceeded the recommended screening threshold for their age and sex. Separate variables were created for SBP and DBP status, which were coded as 0 = Normal SBP/DBP, and 1 = Elevated SBP/DBP. This approach ensured age-and sex-specific appropriate classification, aligning with recommended pediatric standards and improving accuracy over adult-based thresholds. Dietary patterns of the respondents which is the independent variable, were generated using a Principal Component Analysis (PCA), with Varimax rotation to identify dietary patterns from the 47 FFQ items. Kaiser–Meyer–Olkin (KMO) test and Bartlett’s Test of Sphericity assessed sampling adequacy and factorability. Items with low communalities (< 0.3) or cross-loadings ( 1, scree plot inspection, and factor interpretability. Factor loadings ≥ 0.4 were used to define factors (dietary patterns), and factor scores for each participant were computed using the regression method. Dietary patterns were obtained depending on the number of factors to be retained from the scree plot output, and these categories were used in further analysis to explore associations with SBP and DBP. Results Table 1 presents the identified dietary patterns with the total variance explained by the extracted dietary patterns. Three principal components with eigenvalues greater than one were retained based on the Kaiser criterion. The first factor (Traditional/Plant-based diet) accounted for 26.0% of the total variance, the second factor (Sweetened beverage diet) explained 9.9%, and the third factor (Protein/Westernized diet) explained 9.1%. Table 1 Identified dietary patterns with eigenvalues and Percentage (%) of Variance Explained Factor Eigenvalue % of Variance Explained Cumulative % Factor 1 (Traditional/Plant-based diet) 9.10 26.0% 26.0% Factor 2 (Sweetened beverage diet) 3.49 9.9% 35.9% Factor 3 (Protein/Westernized diet) 3.17 9.1% 45.0% Table 2 shows the factor loadings for each food item; apparently, all the food items in each factor have acceptable factor loadings > 0.40. Based on these factor scores, the factor scores derived from principal component analysis were standardized, with mean values of 0.00 and standard deviations of 1.00 for all three dietary patterns. These three food patterns were identified as: (i) Traditional/Plant-based diet, which was characterized by high loadings of fruits (mango, orange, pawpaw, pineapple, banana), vegetables, Ewedu, Okro, maize, yam, pap, beans, and local staple foods such as Eba and fufu. (ii) Sweetened beverage diet, which showed high loadings of chocolate drinks, soft drinks, yoghurt, milk drink, reflecting a more energy-dense, processed dietary pattern. (iii) Protein/Westernized diet included chicken, turkey, pork, mayonnaise, and margarine, reflecting a fattier and cholesterol-rich food pattern. Table 2 Principal Component Analysis (PCA) of Dietary Patterns Among Children & Adolescents Food Items Factor 1 (Traditional/Plant-based diet) Factor 2 (Sweetened beverage diet) Factor 3 (Protein/Westernized diet) Maize 0.677 - - Pap 0.577 - - White bread 0.539 - - Indomie 0.504 - - Spaghetti 0.502 - - Fufu 0.537 - - Cassava flour (lafun) 0.519 - - Yam 0.550 - - Potato 0.558 - - Beans 0.601 - - Moimoi 0.663 - - Akara 0.617 - - Groundnut 0.543 - - Soybean 0.478 - - Orange 0.726 - - Apple 0.474 - - Pineapple 0.611 - - Tangerine 0.738 - - Pawpaw 0.739 - - Mango 0.781 - - Banana 0.688 - - Vegetable (Efo) 0.497 - - Ewedu 0.531 - - Okra 0.537 - - Chicken - - 0.659 Turkey - - 0.740 Pork - - 0.546 Egg - 0.491 - Margarine - - 0.604 Mayonnaise - - 0.599 Soft drinks - 0.802 - Chocolate drinks - 0.696 - Nescafé/coffee - - 0.553 Milk drink - 0.767 - Yoghurt - 0.778 - Figure 1 Based on Communalities > 0.30, Scree plot, and Eigenvalues greater than 1, some food items were extracted as they were found to show no contributions to variances in dietary pattern among the respondents. Thirty-five (35) food items were later used to carry out the PCA, and three (3) major dietary patterns were retained, which together explained a substantial proportion of the total variance. The factor structure was interpreted using the rotated component matrix with a loading cut-off of 0.40. Table 3 none of the dietary patterns were significantly associated with SBP, with traditional/plant-based diet having a regression coefficient of (β = 0.062, p = 0.256), sweetened beverage diet with a regression coefficient of (β = 0.001, p = 0.982), and (β=-0.016, p = 0.777) for protein/westernized dietary pattern. Likewise, none of the dietary patterns were significantly associated with DBP, with traditional/plant-based diet slightly above the level of significance (β = 0.105, p = 0.056), sweetened beverage diet with a regression coefficient of (β = 0.026, p = 0.633) and (β = 0.033, p = 0.550) for protein/westernized dietary pattern. Table 3 Linear Regression Analysis of Dietary Patterns with SBP and DBP of Children and Adolescents (n = 335) Predictor Regression coefficient (β) 95% CI for β p-value Traditional/Plant-based diet SBP DBP 0.062 0.105 -0.66–2.48 -0.03–2.66 0.256 0.056 Sweetened beverage diet SBP DBP 0.001 0.026 -1.56–1.59 -1.02–1.67 0.982 0.633 Protein/Westernized diet SBP DBP -0.016 0.033 -1.80–1.35 -0.93–1.75 0.777 0.550 Table 4 based on levels of adherence among the 335 children and adolescents. For each dietary pattern, respondents were almost evenly distributed across low, moderate, and high adherence categories. Approximately one-third of participants fell into each level of adherence, with 33.1% classified as low adherence (n = 111), and 33.4% each classified as moderate (n = 112) and high adherence (n = 112). Table 4 Distribution of Dietary Pattern Categories (n = 335) Dietary Pattern Category Frequency (n) Percentage (%) Traditional/Plant-based diet Low adherence Moderate adherence High adherence 111 112 112 33.1 33.4 33.4 Sweetened beverages Low adherence Moderate adherence High adherence 111 112 112 33.1 33.4 33.4 Protein/Westernized diet Low adherence Moderate adherence High adherence 111 112 112 33.1 33.4 33.4 Table 5 Out of 112 adolescents with high adherence to the traditional/plant-based pattern, 101 (90.2%) had normal SBP, while just a few 11, 9.8%) had an elevated SBP, and this association was not statistically significant (p = 0.886). Likewise, for the sweetened beverage and protein/westernized pattern, out of the total number of adolescents with high adherence to the two dietary patterns, 14 (12.5%) had an elevated SBP, respectively, but still the difference remains statistically insignificant (p = 0.544; p = 0.671). Table 5 Association Between Dietary Patterns and Systolic Blood Pressure Category of Children and Adolescents (n = 335) Dietary Patterns Normal SBP n (%) Elevated SBP n (%) Total n (%) p-value Traditional/Plant-based diet Low Adherence Moderate Adherence High Adherence 100 (90.1) 99 (88.4) 101 (90.2) 11 (9.9) 13 (11.6) 11 (9.8) 111 (100) 112 (100) 112 (100) 0.886 Sweetened beverage diet Low Adherence Moderate Adherence High Adherence 99 (89.2) 103 (92.0) 98 (87.5) 12 (10.8) 9 (8.0) 14 (12.5) 111 (100) 112 (100) 112 (100) 0.544 Protein/Westernized diet Low Adherence Moderate Adherence High Adherence 101 (91.0) 101 (90.2) 98 (87.5) 10 (9.0) 11 (9.8) 14 (12.5) 111 (100) 112 (100) 112 (100) 0.671 Table 6 Out of 112 children and adolescents with high adherence to traditional/plant-based pattern, a higher 87 (77.7%) had normal DBP, while 25 (22.3%) had an elevated DBP, and this association was statistically significant (p = 0.035). Likewise, for the sweetened beverage and protein/westernized pattern, out of the total number of adolescents with high adherence to the two dietary patterns, few 18 (16.1%) and 22 (19.6) had an elevated SBP, respectively, and the differences are not statistically significant (p = 0.942; p = 0.177). Table 6 Association Between Dietary Patterns and Diastolic Blood Pressure Category of Children and Adolescents (n = 335) Dietary Patterns Normal DBP n (%) Elevated DBP n (%) Total n (%) p-value Traditional/Plant-based diet Low Adherence Moderate Adherence High Adherence 99 (89.2) 98 (87.5) 87 (77.7) 12 (10.8) 14 (12.5) 25 (22.3) 111 (100) 112 (100) 112 (100) 0.035 Sweetened beverage diet Low Adherence Moderate Adherence High Adherence 95 (85.6) 95 (84.8) 94 (83.9) 16 (14.4) 17 (15.2) 18 (16.1) 111 (100) 112 (100) 112 (100) 0.942 Protein/Westernized diet Low Adherence Moderate Adherence High Adherence 94 (84.7) 100 (89.3) 90 (80.4) 17 (10.7) 12 (10.7) 22 (19.6) 111 (100) 112 (100) 112 (100) 0.177 Table 7 For the traditional/plant-based dietary pattern, mean systolic blood pressure showed little variation across low (102.3 ± 15.3 mmHg), moderate (102.4 ± 14.0 mmHg), and high adherence (104.4 ± 14.4 mmHg), with no statistically significant difference (f = 0.736, p > 0.05). However, mean diastolic blood pressure increased with higher adherence, from 66.9 ± 11.7 mmHg among low adherents and 66.0 ± 12.7 mmHg among moderate adherents to 70.7 ± 12.8 mmHg among high adherents, and this difference was statistically significant (f = 4.419, p < 0.05). Across the sweetened beverage dietary pattern, both systolic and diastolic blood pressure values were similar across adherence categories, with no significant differences observed for systolic (f = 0.297, p > 0.05) or diastolic blood pressure (f = 0.512, p > 0.05). Likewise, no significant variation in systolic or diastolic blood pressure was found across adherence levels of the protein/Westernized dietary pattern (SBP: f = 0.103, p > 0.05; DBP: f = 0.230, p > 0.05). Table 7 Mean Systolic and Diastolic Blood Pressure Across Dietary Pattern Adherence Level (n = 335) Anthropometric Index Mean SBP (± SD) f / p-value Mean DBP (± SD) f / p-value Traditional/Plant-based diet Low adherence Moderate adherence High adherence 102.3 ± 15.3 102.4 ± 14.0 104.4 ± 14.4 f = 0.736 p > 0.05 66.9 ± 11.7 66.0 ± 12.7 70.7 ± 12.8 f = 4.419 p 0.05 67.7 ± 11.9 67.1 ± 13.7 68.8 ± 11.9 f = 0.512 p > 0.05 Protein/Westernized diet Low adherence Moderate adherence High adherence 103.4 ± 14.4 103.2 ± 13.1 102.5 ± 16/2 f = 0.103 p > 0.05 68.1 ± 12.3 67.2 ± 12.1 68.3 ± 13.3 f = 0.230 p > 0.05 Table 8 is a binary logistic regression analysis that examines the relationship between dietary patterns and diastolic blood pressure among children and adolescents. As shown in the table, a different pattern was observed for diastolic blood pressure. Children and adolescents with moderate adherence to the traditional/plant-based diet had significantly lower odds of elevated diastolic blood pressure compared to those with low adherence (Crude OR = 0.43, 95% CI: 0.20–0.92, p = 0.029). Although the odds for high adherence (OR = 0.50, 95% CI: 0.24–1.04) suggested a similar protective trend, the association did not reach statistical significance ( p = 0.063). Table 8 Binary Logistic Regression Showing the Association Between Dietary Patterns and Diastolic Blood Pressure of Children and Adolescents (n = 335) Variable Category Crude OR (95% CI) p-value Traditional/plant-based diet Low Adherence Moderate Adherence High Adherence 1.0 (ref) 0.43 (0.20–0.92) 0.50 (0.24–1.04) - 0.029 0.063 Sweetened beverage diet Low Adherence Moderate Adherence High Adherence 1.0 (ref) 0.91 (0.43–1.94) 0.88 (0.42–1.84) - 0.806 0.737 Protein/Westernized diet Low Adherence Moderate Adherence High Adherence 1.0 (ref) 0.68 (0.31–1.54) 1.32 (0.65–2.68) - 0.362 0.439 Discussion Principal component analysis identified three distinct dietary patterns among children and adolescents in Ibadan: traditional/plant-based, sweetened beverage, and protein/Westernized dietary patterns, with all patterns demonstrating acceptable factor loadings. Respondents were almost equally distributed across low, moderate, and high adherence categories for each dietary pattern, indicating a homogeneous distribution of dietary behaviors within the study population. This uniformity suggests the absence of a dominant dietary pattern and provides a balanced framework for examining associations between dietary patterns and health outcomes. Further analysis showed a significant age-related difference in mean dietary pattern scores for the traditional/plant-based diet, with higher mean scores observed among children aged 8–9 years compared with those aged 10–14 years. This finding may reflect greater reliance on home-prepared traditional meals among younger children, whereas older adolescents may experience increased autonomy in food choices. In contrast, no significant age-related differences were observed for the sweetened beverage or protein/Westernized dietary patterns. Additionally, dietary pattern scores did not differ significantly by sex across all patterns, indicating broadly comparable dietary behaviors between male and female respondents. These findings, however, should be interpreted cautiously given the cross-sectional nature of the study. Mean comparisons of blood pressure across dietary pattern adherence levels revealed minimal variation in systolic blood pressure within each dietary pattern, with differences of less than 3 mmHg between adherence groups. This explains the absence of statistically significant associations between dietary patterns and systolic blood pressure. Adolescents with high adherence to the traditional/plant-based dietary pattern exhibited systolic blood pressure values comparable to those with low and moderate adherence, while similar patterns were observed for the sweetened beverage and protein/Westernized diets. These results suggest that dietary habits alone may not exert a strong influence on systolic blood pressure during adolescence, possibly due to limited duration of dietary exposure, the relatively young age of participants, or the influence of other factors such as body composition, genetic predisposition, and physical activity. This observation aligns with the findings of [ 9 ], who reported that elevated blood pressure among children and adolescents attending the University College Hospital (UCH), Ibadan, was more strongly associated with early pubertal onset than with dietary factors. Together, these findings indicate that physiological and developmental factors may play a more prominent role than diet in determining systolic blood pressure in this age group. In contrast, diastolic blood pressure showed a statistically significant variation across adherence levels of the traditional/plant-based dietary pattern. Respondents with high adherence recorded higher mean diastolic blood pressure compared with those with low and moderate adherence, while mean diastolic blood pressure values for the sweetened beverage and protein/Westernized dietary patterns showed negligible differences across adherence categories. This suggests that dietary patterns may exert a more discernible influence on diastolic rather than systolic blood pressure in adolescents. The observed association may reflect the nutritional composition of traditional diets in the study setting, particularly sodium content from cooking methods or condiments, as well as the balance of protective nutrients such as potassium and other micronutrients. The lack of significant associations between diastolic blood pressure and the sweetened beverage or protein/Westernized dietary patterns contrasts with findings by [ 2 ], who reported that adolescent diets characterized by high consumption of energy-dense foods and low intake of fruits and vegetables increase the risk of overweight, obesity, and subsequent non-communicable diseases. Similarly, [ 3 ] found that unhealthy dietary habits, especially frequent consumption of processed foods high in sugar and unhealthy fats, were associated with increased BMI, waist circumference, waist-to-height ratio, and cardiovascular risk. Differences between these studies and the present findings may reflect variations in study populations, dietary assessment methods, and cumulative exposure to unhealthy diets. While dietary patterns may not significantly influence systolic blood pressure at this stage of life, they may play a role in shaping diastolic blood pressure, an important early cardiovascular risk indicator. These results underscore the need for comprehensive interventions that integrate healthy dietary practices with weight management and physical activity. Future longitudinal studies incorporating detailed dietary composition and lifestyle factors are warranted to better elucidate the long-term impact of dietary patterns on blood pressure and cardiovascular risk among adolescents. Conclusion This study identified three distinct dietary patterns among children and adolescents in Ibadan, with younger children showing greater adherence to traditional/plant-based diets and no significant sex-based differences in dietary behaviors. While dietary patterns were not significantly associated with systolic blood pressure, diastolic blood pressure showed a significant association as adherence to a traditional/plant-based diet was associated with a lower likelihood of elevated diastolic blood pressure, suggesting a potential protective effect. These findings highlight the complex role of diet in early cardiovascular regulation and underscore the importance of integrated lifestyle interventions, including healthy eating, physical activity, and weight management, for the prevention of future cardiovascular risk. Limitations: The cross-sectional design of the study limits the ability to establish causal relationships between nutritional status and blood pressure; therefore, only associations can be inferred. Although blood pressure was measured twice and the average value was used for analysis, measurements were obtained during a single visit. Temporary factors such as emotional state or recent physical activity may still have influenced blood pressure readings. Declarations Ethical approval: The study protocol was reviewed and approved by the UI/UCH Ethics Committee, College of Medicine, University of Ibadan (Approval number: UI/EC/25/0662), in accordance with the National Code for Health Research Ethics. All procedures involving human participants were conducted in accordance with the ethical standards of the institutional research committee and with the 1964 Declaration of Helsinki and its subsequent amendments. Consent to participate: Voluntary parental informed consent was obtained for all respondents in the study. For respondents who are 12 years and above, an additional freely-given individual assent was obtained before their participation in the study. Participants who wish to exit/withdraw from the study for any reason or other were granted the right to withdraw with no consequences or punishment. All information provided by the participants was handled with strict confidentiality. All procedures involving human participants were conducted in accordance with the ethical standards of the institutional research committee and with the 1964 Declaration of Helsinki and its subsequent amendments. Consent to publish: The data collected for this manuscript does not include any information that leads to the identification of the participants. However, intention to publish was made known to the parents and guardians of the participants, and they agreed to. Data Availability Statement: The datasets generated and analyzed for this study are available from the corresponding author upon reasonable request Funding Declaration: The publication of this finding is not funded by any grant. However, it should be reported that the data used in this study was obtained from the data collected in the project – Development of an Automated System for Dynamic Prediction of High Blood Pressure in School Children and Adolescents which is a funded study by TETFUND. Therefore, the project has no provision for payment for the publication of manuscripts. Clinical Trial Number : Clinical Trial Number not applicable Conflict of Interest: The authors declare no conflict of interest. References Adeomi AA, Adelusi IO, Adedeji PO, Awofeso AE, Oroleye OO, Gbadegesin DL. Nutritional status and Cardiometabolic health among adolescents: Findings from southwestern Nigeria. BMC Nutr. 2019;5(1). https://doi.org/10.1186/s40795-019-0308-5 . Akinola AO. The Food Consumption Pattern of Adolescents in Ibadan. World Nutr. 2023;14(4):26–32. https://doi.org/10.26596/wn.202314426-32 . Asaolu S, Zhang B, Mary AM, Kibenja D, Ma J, Said S, Adeniyi I, Barrow LF. Exploring the relationship between dietary patterns and obesity among Nigerian adults: a cross-sectional study. BMC Public Health. 2024;24(1). https://doi.org/10.1186/s12889-024-18792-4 . Atoh I, Ezeogu J, Okeke CV, Umeh SI, Ekure E, Omokhodion SI, Njokanma FO. High blood pressure pattern amongst adolescents in Lagos, South West Nigeria. Pan Afr Med J. 2023;44:206. https://doi.org/10.11604/pamj.2023.44.206.38670 . Izadi A, Khedmat L, Tavakolizadeh R, Motahedi SY. The intake assessment of diverse dietary patterns on childhood hypertension: Alleviating the blood pressure and lipidemic factors with low-sodium seafood rich in omega-3 fatty acids. Lipids Health Dis. 2020;19(1). https://doi.org/10.1186/s12944-020-01245-3 . Lobstein T, Powis J, Jackson-Leach R. (2024). World Obesity Atlas 2024 . https://data.worldobesity.org/publications/?cat=22 National Heart, Lung, and Blood Institute. (2005). The fourth report on the diagnosis, evaluation, and treatment of high blood pressure in children and adolescents (NIH Publication No. 05-5267). U.S. Department of Health and Human Services. https://www.nhlbi.nih.gov/files/docs/resources/heart/hbp_ped.pdf National Population Commission. (2009). 2006 population and housing census of the Federal Republic of Nigeria: National and state population and housing tables: Priority tables, volume 1 (Priority Tables). Retrieved from https://catalog.ihsn.org//catalog/3340/download/48519 Oluwayemi IO, Oyedeji OA. (2021). A ten-year review of childhood obesity in a teaching hospital, South West Nigeria. In Nigerian Journal of Clinical Practice (Vol. 24, Issue 6, pp. 841–846). Wolters Kluwer Medknow Publications. https://doi.org/10.4103/njcp.njcp_595_20 Papka NY, Babaniyi IB, Aikhionbare HA, Oladele JT, Chinawa JM. Blood Pressure Pattern and Prevalence of Hypertension amongst Apparently Healthy Primary School Pupils in Abuja, Nigeria. Nigerian Postgrad Med J. 2024;31(2):111–7. https://doi.org/10.4103/npmj.npmj_254_23 . Pérez-Gimeno G, Rupérez AI, Vázquez-Cobela R, Herráiz-Gastesi G, Gil-Campos M, Aguilera CM, Moreno LA, Trabazo MRL, Bueno-Lozano G. Energy-dense salty food consumption frequency is associated with diastolic hypertension in Spanish children. Nutrients. 2020;12(4). https://doi.org/10.3390/nu12041027 . Rabi, D. M., McBrien, K. A., Sapir-Pichhadze, R., Nakhla, M., Ahmed, S. B., Dumanski, S. M.,Butalia, S., Leung, A. A., Harris, K. C., Cloutier, L., Zarnke, K. B., Ruzicka, M.,Hiremath, S., Feldman, R. D., Tobe, S. W., Campbell, T. S., Bacon, S. L., Nerenberg,K. A., Dresser, G. K., … Daskalopoulou, S. S. (2020). Hypertension Canada’s 2020 Comprehensive Guidelines for the Prevention, Diagnosis, Risk Assessment, and Treatment of Hypertension in Adults and Children. Canadian Journal of Cardiology , 36 (5), 596–624. https://doi.org/10.1016/j.cjca.2020.02.086. Tang X, Liu Y, Hu J, Zhai L, Jia L, Ding N, Ma Y, Wen D. Association of waist circumference with blood pressure and familial dietary habits in preschool children: a cross-sectional study in northeastern China. Ital J Pediatr. 2022;48(1). https://doi.org/10.1186/s13052-022-01236-3 . Thomas J, Stonebrook E, Kallash M. Pediatric hypertension: Review of the definition, diagnosis, and initial management. Int J Pediatr Adolesc Med. 2022;9(1):1–6. https://doi.org/10.1016/j.ijpam.2020.09.005 . Flynn JT, Kaelber DC, Baker-Smith CM, Blowey D, Carroll AE, Daniels SR, de Ferranti SD, Dionne JM, Falkner B, Flinn SK, Gidding SS, Goodwin C, Leu MG, Powers ME, Rea C, Samuels J, Simasek M, Thaker VV, Urbina EM. & Subcommittee on Screening and Management of High Blood Pressure in Children. (2017). Clinical practice guideline for screening and management of high blood pressure in children and adolescents. Pediatrics, 140(3), e20171904. https://doi.org/10.1542/peds.2017-1904 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 18 May, 2026 Reviewers agreed at journal 12 May, 2026 Reviewers agreed at journal 10 May, 2026 Reviewers agreed at journal 09 May, 2026 Reviews received at journal 05 May, 2026 Reviewers agreed at journal 04 May, 2026 Reviewers invited by journal 04 May, 2026 Editor invited by journal 13 Apr, 2026 Editor assigned by journal 10 Apr, 2026 Submission checks completed at journal 09 Apr, 2026 First submitted to journal 09 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9303561","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":637786387,"identity":"ee620d12-2889-42a4-bf3d-3e3386f9462f","order_by":0,"name":"MAGBAGBEOLA DAVID DAIRO","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvklEQVRIiWNgGAWjYDCCAwwGDAkMDHIMDDwkajEmUQsQJDYQrYXv9uGNHx7usUnfcPzswQcfGOzkdBsIaJE8l1YskfAsLXfDmbxkwxkMycZmBwhoMTjDYyCRcOBw7oYDOWbSPAwHErcRocX4B1BLusH5N8RrMQPZkmBwg1hbJM+wlVkkHEgznHnjjbHhDAMi/MJ3hnnzzR8HbOT5zucYPvhQYSdHUAscKIBVGhCrHATkG0hRPQpGwSgYBSMKAAAMg0ZW5b23ywAAAABJRU5ErkJggg==","orcid":"","institution":"University of Ibadan","correspondingAuthor":true,"prefix":"","firstName":"MAGBAGBEOLA","middleName":"DAVID","lastName":"DAIRO","suffix":""},{"id":637786391,"identity":"cda4e9ad-0813-4ee1-84fb-d6a3c990b4df","order_by":1,"name":"A. A. ALAYANDE","email":"","orcid":"","institution":"University of Ibadan","correspondingAuthor":false,"prefix":"","firstName":"A.","middleName":"A.","lastName":"ALAYANDE","suffix":""},{"id":637786392,"identity":"24773c8e-a4e6-4e64-b90d-142d3ddbae46","order_by":2,"name":"JOY E. WILLIAMS","email":"","orcid":"","institution":"University of Ibadan","correspondingAuthor":false,"prefix":"","firstName":"JOY","middleName":"E.","lastName":"WILLIAMS","suffix":""},{"id":637786393,"identity":"b02de1f3-7cc3-4b1d-9ef3-c32cda2f0e5a","order_by":3,"name":"PRAISE O ONUOHA","email":"","orcid":"","institution":"University of Ibadan","correspondingAuthor":false,"prefix":"","firstName":"PRAISE","middleName":"O","lastName":"ONUOHA","suffix":""},{"id":637786394,"identity":"309b68dd-f07e-4816-93ff-4bdbb47d1c49","order_by":4,"name":"J. A SAMUEL","email":"","orcid":"","institution":"University of Ibadan","correspondingAuthor":false,"prefix":"","firstName":"J.","middleName":"A","lastName":"SAMUEL","suffix":""},{"id":637786396,"identity":"c2586dbb-a760-471b-9cde-24d5bfd78b89","order_by":5,"name":"D. A. OLAWUNI","email":"","orcid":"","institution":"University of Ibadan","correspondingAuthor":false,"prefix":"","firstName":"D.","middleName":"A.","lastName":"OLAWUNI","suffix":""},{"id":637786398,"identity":"905b67a9-d956-40d7-8dd6-550f14ae6569","order_by":6,"name":"DEMILADE H. ADESINA","email":"","orcid":"","institution":"University of Ibadan","correspondingAuthor":false,"prefix":"","firstName":"DEMILADE","middleName":"H.","lastName":"ADESINA","suffix":""},{"id":637786399,"identity":"24ff4618-b629-40c0-ba17-958b30e4ca1a","order_by":7,"name":"Temilade O. ADEROUNMU","email":"","orcid":"","institution":"Obafemi Awolowo University","correspondingAuthor":false,"prefix":"","firstName":"Temilade","middleName":"O.","lastName":"ADEROUNMU","suffix":""},{"id":637786400,"identity":"5ca69232-a018-433a-b4bf-63a33a8c38e4","order_by":8,"name":"Ijeoma N. DIAKU-AKINWUMI","email":"","orcid":"","institution":"Lagos State University","correspondingAuthor":false,"prefix":"","firstName":"Ijeoma","middleName":"N.","lastName":"DIAKU-AKINWUMI","suffix":""},{"id":637786402,"identity":"a7487b56-3826-4e15-83ae-3b24152157cc","order_by":9,"name":"Kofoworola I. Adediran","email":"","orcid":"","institution":"University of Ibadan","correspondingAuthor":false,"prefix":"","firstName":"Kofoworola","middleName":"I.","lastName":"Adediran","suffix":""},{"id":637786403,"identity":"905c237e-3486-4cc4-b4ab-22950c060fdf","order_by":10,"name":"A. D. Ademola","email":"","orcid":"","institution":"University of Ibadan","correspondingAuthor":false,"prefix":"","firstName":"A.","middleName":"D.","lastName":"Ademola","suffix":""},{"id":637786407,"identity":"14bce094-9584-4185-ab8c-743e309beb7f","order_by":11,"name":"Biodun O. Akinyemi","email":"","orcid":"","institution":"Obafemi Awolowo University","correspondingAuthor":false,"prefix":"","firstName":"Biodun","middleName":"O.","lastName":"Akinyemi","suffix":""},{"id":637786413,"identity":"087134b2-c45f-490e-922a-75add74edf1c","order_by":12,"name":"O F W Onifade","email":"","orcid":"","institution":"University of Ibadan","correspondingAuthor":false,"prefix":"","firstName":"O","middleName":"F W","lastName":"Onifade","suffix":""}],"badges":[],"createdAt":"2026-04-02 13:08:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9303561/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9303561/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109153510,"identity":"65d75ca6-dec1-4418-9e5e-f519971ebeb9","added_by":"auto","created_at":"2026-05-13 06:18:34","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":17112,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eScree plot of Principal Component Analysis (PCA)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9303561/v1/137c21fa580804f69bed9191.png"},{"id":109204831,"identity":"fd57943d-992a-4892-9345-d9880b4e0d4e","added_by":"auto","created_at":"2026-05-13 15:02:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":347221,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9303561/v1/c1611c3a-8f3a-48b7-87bc-6229be59d09c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eDietary Correlates of Elevated Blood Pressure Among Children and Adolescents in Ibadan, Southwestern, Nigeria\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHypertension often progresses undetected in children and adolescents, and if not diagnosed or managed, it continues into adulthood, increasing the risk for cardiovascular disease, kidney damage, stroke, and heart failure. In sub-Saharan Africa, the complications of hypertension have continued to increase at a significant rate, which unfortunately progresses through adulthood as a silent killer and leading cause of vast disability and mortality [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Elevated blood pressure is one of the most common health concerns in children and adolescents that poses a significant public health challenge globally, although it is less recognized. [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The increasing prevalence of elevated blood pressure in children and adolescents appears to be strongly tied to the increasing prevalence of obesity and sedentary lifestyles in children and adolescents. Enough evidence has confirmed that elevated blood pressure (BP) in children and adolescents can lead to negative outcomes, such as vascular damage, cardiometabolic risk, and organ damage, which increases an individual\u0026rsquo;s risk of developing hypertension in adulthood [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Nutrition-related hypertension prevalence has nearly tripled between 1975 and 2016, with childhood obesity being considered an important nutritional risk factor for childhood hypertension, constituting a major public health problem worldwide. [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. By 2035, an estimated 68\u0026nbsp;million children will experience serious consequences of high blood pressure due to their high BMI, being overweight or obese [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Overweight and obesity lead to adverse metabolic effects on blood pressure, cholesterol, triglycerides, and insulin resistance. The dietary intake of foods containing high fat and sugar, as well as the low level of habitual physical activity in children, can potentially increase the risk of developing elevated blood pressure at a younger age, as well as behavioral patterns of adolescence [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Nigeria is experiencing a nutrition transition, with rapid urbanization and associated adoption of Western diets and a reduction in energy expenditure among children and adolescents. [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. An increasing burden of hypertension in both children and adolescents is likely to be of serious consequence as a result of limited resources and inadequate health budgets in most primary healthcare settings in this region, resulting in inadequate routine screening for early detection of hypertension in children and adolescents [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. While most hypertension cases in children and adolescents remain undetected and uncontrolled, the risk of long-term cardiovascular complications in adolescents, including early-onset heart disease and stroke, tends to rise, thereby placing a growing burden on healthcare systems and a significant threat to public health [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. By identifying poor dietary habits early, effective interventions can be implemented to prevent the progression of hypertension into adulthood, ultimately reducing the future burden of cardiovascular diseases and reducing the cost of healthcare. Therefore, this study represents a baseline exploratory component of a larger project aimed at developing a predictive system for high blood pressure, and aimed to assess the dietary correlates of elevated blood pressure among children and adolescents in Ibadan, southwest Nigeria.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cp\u003eThe research was conducted in Ibadan, a metropolitan city located in Oyo State, southwestern Nigeria. As the administrative capital of the state, Ibadan serves as a major hub for politics and economics. The city is highly urbanized and densely populated, ranking among the largest metropolitan areas in Nigeria. According to national population data [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Ibadan has an estimated metropolitan population approaching four million inhabitants. Administratively, Ibadan is structured into eleven local government areas, comprising five predominantly urban local governments and six that are largely rural in character.\u003c/p\u003e \u003cp\u003e \u003cb\u003eStudy design and duration\u003c/b\u003e: This study employed a community-based descriptive cross-sectional study design, which was conducted between the period of May and November 2025 among children and adolescents aged 8 to 14 years residing in selected communities in both rural and urban settlements of Ibadan during the period of the study. The study was conducted in four LGAs and included twelve communities: Siba, Yemetu, Oke Adu, Adeoyo, Sango, Agbowo, Sapati, Kute, Olowu, Oke Aremu, Inalende, and Opoo.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eSample Size Determination\u003c/strong\u003e \u003cp\u003eA total of 335 children and adolescents were recruited for this study. Cochran's formula n= [ (Z)\u003csup\u003e2\u003c/sup\u003e[P(1-P)]/d\u003csup\u003e2\u003c/sup\u003e for estimating the sample size for a single proportion was used, with a prevalence of 26.7%, adapted from [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003c/p\u003e \u003cp\u003eWhere:\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;required sample size\u003c/p\u003e \u003cp\u003ez\u0026thinsp;=\u0026thinsp;standard normal deviate (1.96 for 95% confidence level)\u003c/p\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;expected prevalence\u003c/p\u003e \u003cp\u003ed\u0026thinsp;=\u0026thinsp;margin of error (0.05)\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSampling technique\u003c/h2\u003e \u003cp\u003eThe eligible participants were selected using a multistage sampling technique. In the first stage, a list of all local governments in Ibadan was obtained, which comprises 11 local government areas: Ibadan North, Ibadan North-West, Ibadan North-East, Ibadan South-West, Ibadan South-East, Akinyele, Lagelu, Egbeda, Ona-Ara, Oluyole, and Ido. The local government was stratified into urban, comprising Ibadan North, Ibadan North-west, Ibadan North-East, Ibadan South-West, Ibadan South-East, and rural LGAs, comprising Ido, Lagelu, Egbeda, Ona-Ara, Akinyele, and Oluyole. From each stratum, two urban LGAs (Ibadan north and Ibadan north-west) and two rural LGAs (Ido and Lagelu) were randomly selected via balloting. In each selected LGA, communities such as Yemetu, Adeoyo, Agbowo, Sango in Ibadan North, Opoo, in Ibadan North-West, Oke Adu, Oke Aremu, Sapati, Kute, Olowu in Lagelu, and Siba in Ido were purposely selected based on the accessibility of children and adolescents and population distribution. In the second stage, A house-to-house survey was conducted in the selected communities. Systematic sampling was used to select households. A sampling interval was determined with a random starting point, and every third household was selected until the required sample size was achieved. Lastly, in households with more than one eligible child and adolescent, simple random sampling via balloting was used to select up to two children or adolescents per household. Each eligible child or adolescent\u0026rsquo;s name or number was placed in a lottery, and a child or adolescent was selected by balloting. This ensured wider representation across households while preventing over-representation from any single household.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData Collection Instrument\u003c/h3\u003e\n\u003cp\u003eData was collected using a pre-tested, semi-structured, interviewer-administered questionnaire designed to obtain information on the sociodemographic characteristics and to assess the dietary patterns of children and adolescents aged 8\u0026ndash;14 years. A validated 47-item food frequency questionnaire (FFQ) adapted from [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] was used to assess the dietary patterns of the selected adolescent. The frequency of intake of six food groups was assessed, namely: roots and tubers, cereals and legumes, fruits and vegetables, meat and meat products, fats and oils, and beverages. Each food item has a choice of 11 frequencies, namely: daily (1x), daily (2times), daily (3times), weekly (1x), weekly (2times), weekly (3times), monthly (1x), monthly (2times), monthly (3times), rarely, and never. This was recategorized as daily (\u0026ge;\u0026thinsp;7/week), frequently (3\u0026ndash;6/week), occasionally (1\u0026ndash;2week), rarely (\u0026lt;\u0026thinsp;1/week), and never. The questionnaire for this study included three major sections: Socio-demographic data of the respondents, Dietary patterns of the respondents, and Blood Pressure Measurement. Blood pressure measurement was done using an \u0026ldquo;ANDON\u0026rdquo; automatic blood pressure monitor, with a curve size appropriate for children and adolescents, and measurements followed the National Blood Pressure guidelines for children and adolescents [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Before data collection, the questionnaire was pre-tested in Barika, a notable community in Ibadan North LGA, which was not included in the main study, to evaluate its validity and reliability. The pre-test findings demonstrated a high level of internal consistency, with a Cronbach\u0026rsquo;s alpha coefficient of 0.889, indicating that the instrument was sufficiently reliable. Furthermore, the validity of the questionnaire was assessed by experts in the field, who confirmed its appropriateness for use in the study. Data collected from the field were cleaned, entered, and analyzed using Statistical Package for the Social Sciences (IBM SPSS Version 27.0) and R Studio. Dietary patterns were summarized using Principal Component Analysis (PCA) to generate the factor scores for each dietary pattern, and data visualization using scree plots to show distributions of respondents' dietary patterns. Chi-square test was used to determine the association between dietary pattern group and BP status across Groups (Normal SBP/DBP and Elevated SBP/DBP). Independent Samples T-Test / One-Way ANOVA was used to compare mean dietary factor scores across age groups and sex with BP status. Simple linear regression analyses were carried out to determine the actual relationship between dietary pattern scores and SBP/DBP. A Binary logistic regression was further used to evaluate the relationship between dietary pattern adherence level and SBP/DBP. Confounding factors were controlled for, and the significance level was set at a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 with a 95% CI. Blood Pressure Status for age, sex, and height was measured using a digital automated Blood Pressure monitor, with an appropriate cuff size based on the adolescent\u0026rsquo;s arm circumference. Two separate readings were taken at 5-minute intervals, and the averages were calculated.\u003c/p\u003e \u003cp\u003eThe dependent variable in the study was blood pressure levels, including systolic and diastolic blood pressure levels, which were further categorized into Normal and Elevated following the 2017 American Academy of Pediatrics (AAP) [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] pediatric hypertension guideline. For children aged\u0026thinsp;\u0026lt;\u0026thinsp;13 years, blood pressure categories were defined using age-, sex-, and height-specific percentiles: normal (\u0026lt;\u0026thinsp;90th percentile), and elevated BP (\u0026ge;\u0026thinsp;90th to \u0026lt;\u0026thinsp;95th percentile). For adolescents aged\u0026thinsp;\u0026ge;\u0026thinsp;13 years, fixed thresholds were applied (normal\u0026thinsp;\u0026lt;\u0026thinsp;120/\u0026lt;80 mmHg, elevated BP 120\u0026ndash;129/\u0026lt;80 mmHg). Screening BP values from the AAP simplified table were used to identify participants requiring further evaluation. Participants were classified as having elevated SBP or elevated DBP if their measured SBP or DBP met or exceeded the recommended screening threshold for their age and sex. Separate variables were created for SBP and DBP status, which were coded as 0\u0026thinsp;=\u0026thinsp;Normal SBP/DBP, and 1\u0026thinsp;=\u0026thinsp;Elevated SBP/DBP. This approach ensured age-and sex-specific appropriate classification, aligning with recommended pediatric standards and improving accuracy over adult-based thresholds.\u003c/p\u003e \u003cp\u003eDietary patterns of the respondents which is the independent variable, were generated using a Principal Component Analysis (PCA), with Varimax rotation to identify dietary patterns from the 47 FFQ items. Kaiser\u0026ndash;Meyer\u0026ndash;Olkin (KMO) test and Bartlett\u0026rsquo;s Test of Sphericity assessed sampling adequacy and factorability. Items with low communalities (\u0026lt;\u0026thinsp;0.3) or cross-loadings (\u0026lt;\u0026thinsp;0.4) were removed, and PCA was re-run until a stable factor solution was obtained. The number of factors retained was based on eigenvalues\u0026thinsp;\u0026gt;\u0026thinsp;1, scree plot inspection, and factor interpretability. Factor loadings\u0026thinsp;\u0026ge;\u0026thinsp;0.4 were used to define factors (dietary patterns), and factor scores for each participant were computed using the regression method. Dietary patterns were obtained depending on the number of factors to be retained from the scree plot output, and these categories were used in further analysis to explore associations with SBP and DBP.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eTable \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the identified dietary patterns with the total variance explained by the extracted dietary patterns. Three principal components with eigenvalues greater than one were retained based on the Kaiser criterion. The first factor (Traditional/Plant-based diet) accounted for 26.0% of the total variance, the second factor (Sweetened beverage diet) explained 9.9%, and the third factor (Protein/Westernized diet) explained 9.1%.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eIdentified dietary patterns with eigenvalues and Percentage (%) of Variance Explained\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eFactor\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eEigenvalue\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e% of Variance Explained\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eCumulative %\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eFactor 1 (Traditional/Plant-based diet)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e9.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e26.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e26.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eFactor 2 (Sweetened beverage diet)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e3.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e9.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e35.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eFactor 3 (Protein/Westernized diet)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e3.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e9.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e45.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eTable \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the factor loadings for each food item; apparently, all the food items in each factor have acceptable factor loadings\u0026thinsp;\u0026gt;\u0026thinsp;0.40. Based on these factor scores, the factor scores derived from principal component analysis were standardized, with mean values of 0.00 and standard deviations of 1.00 for all three dietary patterns. These three food patterns were identified as: (i) Traditional/Plant-based diet, which was characterized by high loadings of fruits (mango, orange, pawpaw, pineapple, banana), vegetables, Ewedu, Okro, maize, yam, pap, beans, and local staple foods such as Eba and fufu. (ii) Sweetened beverage diet, which showed high loadings of chocolate drinks, soft drinks, yoghurt, milk drink, reflecting a more energy-dense, processed dietary pattern. (iii) Protein/Westernized diet included chicken, turkey, pork, mayonnaise, and margarine, reflecting a fattier and cholesterol-rich food pattern.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePrincipal Component Analysis (PCA) of Dietary Patterns Among Children \u0026amp; Adolescents\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eFood Items\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eFactor 1 (Traditional/Plant-based diet)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eFactor 2 (Sweetened beverage diet)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eFactor 3 (Protein/Westernized diet)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eMaize\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.677\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003ePap\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.577\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eWhite bread\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.539\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eIndomie\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.504\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eSpaghetti\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.502\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eFufu\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.537\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eCassava flour (lafun)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.519\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eYam\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.550\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003ePotato\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.558\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eBeans\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.601\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eMoimoi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.663\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eAkara\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.617\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eGroundnut\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.543\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eSoybean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.478\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eOrange\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.726\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eApple\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.474\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003ePineapple\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.611\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eTangerine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.738\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003ePawpaw\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.739\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eMango\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.781\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eBanana\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.688\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eVegetable (Efo)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.497\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eEwedu\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.531\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eOkra\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.537\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eChicken\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.659\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eTurkey\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.740\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003ePork\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.546\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eEgg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.491\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eMargarine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.604\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eMayonnaise\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.599\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eSoft drinks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.802\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eChocolate drinks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.696\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eNescaf\u0026eacute;/coffee\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.553\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eMilk drink\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.767\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eYoghurt\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e0.778\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u0026nbsp;\u003c/span\u003e\u003c/strong\u003eBased on Communalities\u0026thinsp;\u0026gt;\u0026thinsp;0.30, Scree plot, and Eigenvalues greater than 1, some food items were extracted as they were found to show no contributions to variances in dietary pattern among the respondents. Thirty-five (35) food items were later used to carry out the PCA, and three (3) major dietary patterns were retained, which together explained a substantial proportion of the total variance. The factor structure was interpreted using the rotated component matrix with a loading cut-off of 0.40.\u003c/p\u003e\n\u003cp\u003eTable \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e none of the dietary patterns were significantly associated with SBP, with traditional/plant-based diet having a regression coefficient of (\u0026beta;\u0026thinsp;=\u0026thinsp;0.062, p\u0026thinsp;=\u0026thinsp;0.256), sweetened beverage diet with a regression coefficient of (\u0026beta;\u0026thinsp;=\u0026thinsp;0.001, p\u0026thinsp;=\u0026thinsp;0.982), and (\u0026beta;=-0.016, p\u0026thinsp;=\u0026thinsp;0.777) for protein/westernized dietary pattern. Likewise, none of the dietary patterns were significantly associated with DBP, with traditional/plant-based diet slightly above the level of significance (\u0026beta;\u0026thinsp;=\u0026thinsp;0.105, p\u0026thinsp;=\u0026thinsp;0.056), sweetened beverage diet with a regression coefficient of (\u0026beta;\u0026thinsp;=\u0026thinsp;0.026, p\u0026thinsp;=\u0026thinsp;0.633) and (\u0026beta;\u0026thinsp;=\u0026thinsp;0.033, p\u0026thinsp;=\u0026thinsp;0.550) for protein/westernized dietary pattern.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eLinear Regression Analysis of Dietary Patterns with SBP and DBP of Children and Adolescents (n\u0026thinsp;=\u0026thinsp;335)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003ePredictor\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\" style=\"width: 33.7208%;\"\u003e\n \u003cp\u003eRegression coefficient (\u0026beta;)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\" style=\"width: 18.0234%;\"\u003e\n \u003cp\u003e95% CI for \u0026beta;\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eTraditional/Plant-based diet\u003c/p\u003e\n \u003cp\u003eSBP\u003c/p\u003e\n \u003cp\u003eDBP\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\" style=\"width: 33.7208%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e0.062\u003c/p\u003e\n \u003cp\u003e0.105\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\" style=\"width: 18.0234%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e-0.66\u0026ndash;2.48\u003c/p\u003e\n \u003cp\u003e-0.03\u0026ndash;2.66\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e0.256\u003c/p\u003e\n \u003cp\u003e0.056\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eSweetened beverage diet\u003c/p\u003e\n \u003cp\u003eSBP\u003c/p\u003e\n \u003cp\u003eDBP\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\" style=\"width: 33.7208%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\" style=\"width: 18.0234%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e-1.56\u0026ndash;1.59\u003c/p\u003e\n \u003cp\u003e-1.02\u0026ndash;1.67\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e0.982\u003c/p\u003e\n \u003cp\u003e0.633\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eProtein/Westernized diet\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eSBP\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eDBP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\" style=\"width: 33.7208%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e-0.016\u003c/p\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\" style=\"width: 18.0234%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e-1.80\u0026ndash;1.35\u003c/p\u003e\n \u003cp\u003e-0.93\u0026ndash;1.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e0.777\u003c/p\u003e\n \u003cp\u003e0.550\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eTable \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e based on levels of adherence among the 335 children and adolescents. For each dietary pattern, respondents were almost evenly distributed across low, moderate, and high adherence categories. Approximately one-third of participants fell into each level of adherence, with 33.1% classified as low adherence (n\u0026thinsp;=\u0026thinsp;111), and 33.4% each classified as moderate (n\u0026thinsp;=\u0026thinsp;112) and high adherence (n\u0026thinsp;=\u0026thinsp;112).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDistribution of Dietary Pattern Categories (n\u0026thinsp;=\u0026thinsp;335)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\" style=\"width: 48.3871%;\"\u003e\n \u003cp\u003eDietary Pattern Category\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\" style=\"width: 24.5658%;\"\u003e\n \u003cp\u003eFrequency (n)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003ePercentage (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\" style=\"width: 48.3871%;\"\u003e\n \u003cp\u003eTraditional/Plant-based diet\u003c/p\u003e\n \u003cp\u003eLow adherence\u003c/p\u003e\n \u003cp\u003eModerate adherence\u003c/p\u003e\n \u003cp\u003eHigh adherence\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\" style=\"width: 24.5658%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e111\u003c/p\u003e\n \u003cp\u003e112\u003c/p\u003e\n \u003cp\u003e112\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e33.1\u003c/p\u003e\n \u003cp\u003e33.4\u003c/p\u003e\n \u003cp\u003e33.4\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\" style=\"width: 48.3871%;\"\u003e\n \u003cp\u003eSweetened beverages\u003c/p\u003e\n \u003cp\u003eLow adherence\u003c/p\u003e\n \u003cp\u003eModerate adherence\u003c/p\u003e\n \u003cp\u003eHigh adherence\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\" style=\"width: 24.5658%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e111\u003c/p\u003e\n \u003cp\u003e112\u003c/p\u003e\n \u003cp\u003e112\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e33.1\u003c/p\u003e\n \u003cp\u003e33.4\u003c/p\u003e\n \u003cp\u003e33.4\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\" style=\"width: 48.3871%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eProtein/Westernized diet\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eLow adherence\u003c/p\u003e\n \u003cp\u003eModerate adherence\u003c/p\u003e\n \u003cp\u003eHigh adherence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\" style=\"width: 24.5658%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e111\u003c/p\u003e\n \u003cp\u003e112\u003c/p\u003e\n \u003cp\u003e112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e33.1\u003c/p\u003e\n \u003cp\u003e33.4\u003c/p\u003e\n \u003cp\u003e33.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eTable \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e Out of 112 adolescents with high adherence to the traditional/plant-based pattern, 101 (90.2%) had normal SBP, while just a few 11, 9.8%) had an elevated SBP, and this association was not statistically significant (p\u0026thinsp;=\u0026thinsp;0.886). Likewise, for the sweetened beverage and protein/westernized pattern, out of the total number of adolescents with high adherence to the two dietary patterns, 14 (12.5%) had an elevated SBP, respectively, but still the difference remains statistically insignificant (p\u0026thinsp;=\u0026thinsp;0.544; p\u0026thinsp;=\u0026thinsp;0.671).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAssociation Between Dietary Patterns and Systolic Blood Pressure Category of Children and Adolescents (n\u0026thinsp;=\u0026thinsp;335)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eDietary Patterns\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\" style=\"width: 21.0721%;\"\u003e\n \u003cp\u003eNormal SBP n (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\" style=\"width: 22.366%;\"\u003e\n \u003cp\u003eElevated SBP n (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eTotal n (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eTraditional/Plant-based diet\u003c/p\u003e\n \u003cp\u003eLow Adherence\u003c/p\u003e\n \u003cp\u003eModerate Adherence\u003c/p\u003e\n \u003cp\u003eHigh Adherence\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\" style=\"width: 21.0721%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e100 (90.1)\u003c/p\u003e\n \u003cp\u003e99 (88.4)\u003c/p\u003e\n \u003cp\u003e101 (90.2)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\" style=\"width: 22.366%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e11 (9.9)\u003c/p\u003e\n \u003cp\u003e13 (11.6)\u003c/p\u003e\n \u003cp\u003e11 (9.8)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e111 (100)\u003c/p\u003e\n \u003cp\u003e112 (100)\u003c/p\u003e\n \u003cp\u003e112 (100)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.886\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eSweetened beverage diet\u003c/p\u003e\n \u003cp\u003eLow Adherence\u003c/p\u003e\n \u003cp\u003eModerate Adherence\u003c/p\u003e\n \u003cp\u003eHigh Adherence\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\" style=\"width: 21.0721%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e99 (89.2)\u003c/p\u003e\n \u003cp\u003e103 (92.0)\u003c/p\u003e\n \u003cp\u003e98 (87.5)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\" style=\"width: 22.366%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e12 (10.8)\u003c/p\u003e\n \u003cp\u003e9 (8.0)\u003c/p\u003e\n \u003cp\u003e14 (12.5)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e111 (100)\u003c/p\u003e\n \u003cp\u003e112 (100)\u003c/p\u003e\n \u003cp\u003e112 (100)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.544\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eProtein/Westernized diet\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eLow Adherence\u003c/p\u003e\n \u003cp\u003eModerate Adherence\u003c/p\u003e\n \u003cp\u003eHigh Adherence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\" style=\"width: 21.0721%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e101 (91.0)\u003c/p\u003e\n \u003cp\u003e101 (90.2)\u003c/p\u003e\n \u003cp\u003e98 (87.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\" style=\"width: 22.366%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e10 (9.0)\u003c/p\u003e\n \u003cp\u003e11 (9.8)\u003c/p\u003e\n \u003cp\u003e14 (12.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e111 (100)\u003c/p\u003e\n \u003cp\u003e112 (100)\u003c/p\u003e\n \u003cp\u003e112 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.671\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eTable \u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e Out of 112 children and adolescents with high adherence to traditional/plant-based pattern, a higher 87 (77.7%) had normal DBP, while 25 (22.3%) had an elevated DBP, and this association was statistically significant (p\u0026thinsp;=\u0026thinsp;0.035). Likewise, for the sweetened beverage and protein/westernized pattern, out of the total number of adolescents with high adherence to the two dietary patterns, few 18 (16.1%) and 22 (19.6) had an elevated SBP, respectively, and the differences are not statistically significant (p\u0026thinsp;=\u0026thinsp;0.942; p\u0026thinsp;=\u0026thinsp;0.177).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAssociation Between Dietary Patterns and Diastolic Blood Pressure Category of Children and Adolescents (n\u0026thinsp;=\u0026thinsp;335)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eDietary Patterns\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eNormal DBP n (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eElevated DBP n (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eTotal n (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eTraditional/Plant-based diet\u003c/p\u003e\n \u003cp\u003eLow Adherence\u003c/p\u003e\n \u003cp\u003eModerate Adherence\u003c/p\u003e\n \u003cp\u003eHigh Adherence\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e99 (89.2)\u003c/p\u003e\n \u003cp\u003e98 (87.5)\u003c/p\u003e\n \u003cp\u003e87 (77.7)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e12 (10.8)\u003c/p\u003e\n \u003cp\u003e14 (12.5)\u003c/p\u003e\n \u003cp\u003e25 (22.3)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e111 (100)\u003c/p\u003e\n \u003cp\u003e112 (100)\u003c/p\u003e\n \u003cp\u003e112 (100)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eSweetened beverage diet\u003c/p\u003e\n \u003cp\u003eLow Adherence\u003c/p\u003e\n \u003cp\u003eModerate Adherence\u003c/p\u003e\n \u003cp\u003eHigh Adherence\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e95 (85.6)\u003c/p\u003e\n \u003cp\u003e95 (84.8)\u003c/p\u003e\n \u003cp\u003e94 (83.9)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e16 (14.4)\u003c/p\u003e\n \u003cp\u003e17 (15.2)\u003c/p\u003e\n \u003cp\u003e18 (16.1)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e111 (100)\u003c/p\u003e\n \u003cp\u003e112 (100)\u003c/p\u003e\n \u003cp\u003e112 (100)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.942\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eProtein/Westernized diet\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eLow Adherence\u003c/p\u003e\n \u003cp\u003eModerate Adherence\u003c/p\u003e\n \u003cp\u003eHigh Adherence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e94 (84.7)\u003c/p\u003e\n \u003cp\u003e100 (89.3)\u003c/p\u003e\n \u003cp\u003e90 (80.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e17 (10.7)\u003c/p\u003e\n \u003cp\u003e12 (10.7)\u003c/p\u003e\n \u003cp\u003e22 (19.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e111 (100)\u003c/p\u003e\n \u003cp\u003e112 (100)\u003c/p\u003e\n \u003cp\u003e112 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.177\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eTable \u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e For the traditional/plant-based dietary pattern, mean systolic blood pressure showed little variation across low (102.3\u0026thinsp;\u0026plusmn;\u0026thinsp;15.3 mmHg), moderate (102.4\u0026thinsp;\u0026plusmn;\u0026thinsp;14.0 mmHg), and high adherence (104.4\u0026thinsp;\u0026plusmn;\u0026thinsp;14.4 mmHg), with no statistically significant difference (f\u0026thinsp;=\u0026thinsp;0.736, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). However, mean diastolic blood pressure increased with higher adherence, from 66.9\u0026thinsp;\u0026plusmn;\u0026thinsp;11.7 mmHg among low adherents and 66.0\u0026thinsp;\u0026plusmn;\u0026thinsp;12.7 mmHg among moderate adherents to 70.7\u0026thinsp;\u0026plusmn;\u0026thinsp;12.8 mmHg among high adherents, and this difference was statistically significant (f\u0026thinsp;=\u0026thinsp;4.419, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Across the sweetened beverage dietary pattern, both systolic and diastolic blood pressure values were similar across adherence categories, with no significant differences observed for systolic (f\u0026thinsp;=\u0026thinsp;0.297, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) or diastolic blood pressure (f\u0026thinsp;=\u0026thinsp;0.512, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Likewise, no significant variation in systolic or diastolic blood pressure was found across adherence levels of the protein/Westernized dietary pattern (SBP: f\u0026thinsp;=\u0026thinsp;0.103, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05; DBP: f\u0026thinsp;=\u0026thinsp;0.230, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eMean Systolic and Diastolic Blood Pressure Across Dietary Pattern Adherence Level (n\u0026thinsp;=\u0026thinsp;335)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eAnthropometric Index\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eMean SBP (\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003ef / p-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eMean DBP (\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003ef / p-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eTraditional/Plant-based diet\u003c/p\u003e\n \u003cp\u003eLow adherence\u003c/p\u003e\n \u003cp\u003eModerate adherence\u003c/p\u003e\n \u003cp\u003eHigh adherence\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e102.3\u0026thinsp;\u0026plusmn;\u0026thinsp;15.3\u003c/p\u003e\n \u003cp\u003e102.4\u0026thinsp;\u0026plusmn;\u0026thinsp;14.0\u003c/p\u003e\n \u003cp\u003e104.4\u0026thinsp;\u0026plusmn;\u0026thinsp;14.4\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003ef\u0026thinsp;=\u0026thinsp;0.736\u003c/p\u003e\n \u003cp\u003ep\u0026thinsp;\u0026gt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e66.9\u0026thinsp;\u0026plusmn;\u0026thinsp;11.7\u003c/p\u003e\n \u003cp\u003e66.0\u0026thinsp;\u0026plusmn;\u0026thinsp;12.7\u003c/p\u003e\n \u003cp\u003e70.7\u0026thinsp;\u0026plusmn;\u0026thinsp;12.8\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003ef\u0026thinsp;=\u0026thinsp;4.419\u003c/p\u003e\n \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eSweetened beverages\u003c/p\u003e\n \u003cp\u003eLow adherence\u003c/p\u003e\n \u003cp\u003eModerate adherence\u003c/p\u003e\n \u003cp\u003eHigh adherence\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e103.2\u0026thinsp;\u0026plusmn;\u0026thinsp;13.9\u003c/p\u003e\n \u003cp\u003e102.3\u0026thinsp;\u0026plusmn;\u0026thinsp;14.1\u003c/p\u003e\n \u003cp\u003e103.7\u0026thinsp;\u0026plusmn;\u0026thinsp;15.8\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003ef\u0026thinsp;=\u0026thinsp;0.297\u003c/p\u003e\n \u003cp\u003ep\u0026thinsp;\u0026gt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e67.7\u0026thinsp;\u0026plusmn;\u0026thinsp;11.9\u003c/p\u003e\n \u003cp\u003e67.1\u0026thinsp;\u0026plusmn;\u0026thinsp;13.7\u003c/p\u003e\n \u003cp\u003e68.8\u0026thinsp;\u0026plusmn;\u0026thinsp;11.9\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003ef\u0026thinsp;=\u0026thinsp;0.512\u003c/p\u003e\n \u003cp\u003ep\u0026thinsp;\u0026gt;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eProtein/Westernized diet\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eLow adherence\u003c/p\u003e\n \u003cp\u003eModerate adherence\u003c/p\u003e\n \u003cp\u003eHigh adherence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e103.4\u0026thinsp;\u0026plusmn;\u0026thinsp;14.4\u003c/p\u003e\n \u003cp\u003e103.2\u0026thinsp;\u0026plusmn;\u0026thinsp;13.1\u003c/p\u003e\n \u003cp\u003e102.5\u0026thinsp;\u0026plusmn;\u0026thinsp;16/2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u003cstrong\u003ef\u0026thinsp;=\u0026thinsp;0.103\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ep\u0026thinsp;\u0026gt;\u0026thinsp;0.05\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e68.1\u0026thinsp;\u0026plusmn;\u0026thinsp;12.3\u003c/p\u003e\n \u003cp\u003e67.2\u0026thinsp;\u0026plusmn;\u0026thinsp;12.1\u003c/p\u003e\n \u003cp\u003e68.3\u0026thinsp;\u0026plusmn;\u0026thinsp;13.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e\u003cstrong\u003ef\u0026thinsp;=\u0026thinsp;0.230\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ep\u0026thinsp;\u0026gt;\u0026thinsp;0.05\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eTable \u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e is a binary logistic regression analysis that examines the relationship between dietary patterns and diastolic blood pressure among children and adolescents. As shown in the table, a different pattern was observed for diastolic blood pressure. Children and adolescents with moderate adherence to the traditional/plant-based diet had significantly lower odds of elevated diastolic blood pressure compared to those with low adherence (Crude OR\u0026thinsp;=\u0026thinsp;0.43, 95% CI: 0.20\u0026ndash;0.92, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.029). Although the odds for high adherence (OR\u0026thinsp;=\u0026thinsp;0.50, 95% CI: 0.24\u0026ndash;1.04) suggested a similar protective trend, the association did not reach statistical significance (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.063).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eBinary Logistic Regression Showing the Association Between Dietary Patterns and Diastolic Blood Pressure of Children and Adolescents (n\u0026thinsp;=\u0026thinsp;335)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eCategory\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eCrude OR (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eTraditional/plant-based diet\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eLow Adherence\u003c/p\u003e\n \u003cp\u003eModerate Adherence\u003c/p\u003e\n \u003cp\u003eHigh Adherence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.0 (ref)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e0.43 (0.20\u0026ndash;0.92)\u003c/p\u003e\n \u003cp\u003e0.50 (0.24\u0026ndash;1.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003cp\u003e0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eSweetened beverage diet\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eLow Adherence\u003c/p\u003e\n \u003cp\u003eModerate Adherence\u003c/p\u003e\n \u003cp\u003eHigh Adherence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.0 (ref)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e0.91 (0.43\u0026ndash;1.94)\u003c/p\u003e\n \u003cp\u003e0.88 (0.42\u0026ndash;1.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003cp\u003e0.806\u003c/p\u003e\n \u003cp\u003e0.737\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eProtein/Westernized diet\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eLow Adherence\u003c/p\u003e\n \u003cp\u003eModerate Adherence\u003c/p\u003e\n \u003cp\u003eHigh Adherence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.0 (ref)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e0.68 (0.31\u0026ndash;1.54)\u003c/p\u003e\n \u003cp\u003e1.32 (0.65\u0026ndash;2.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003cp\u003e0.362\u003c/p\u003e\n \u003cp\u003e0.439\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003ePrincipal component analysis identified three distinct dietary patterns among children and adolescents in Ibadan: traditional/plant-based, sweetened beverage, and protein/Westernized dietary patterns, with all patterns demonstrating acceptable factor loadings. Respondents were almost equally distributed across low, moderate, and high adherence categories for each dietary pattern, indicating a homogeneous distribution of dietary behaviors within the study population. This uniformity suggests the absence of a dominant dietary pattern and provides a balanced framework for examining associations between dietary patterns and health outcomes. Further analysis showed a significant age-related difference in mean dietary pattern scores for the traditional/plant-based diet, with higher mean scores observed among children aged 8\u0026ndash;9 years compared with those aged 10\u0026ndash;14 years. This finding may reflect greater reliance on home-prepared traditional meals among younger children, whereas older adolescents may experience increased autonomy in food choices. In contrast, no significant age-related differences were observed for the sweetened beverage or protein/Westernized dietary patterns. Additionally, dietary pattern scores did not differ significantly by sex across all patterns, indicating broadly comparable dietary behaviors between male and female respondents. These findings, however, should be interpreted cautiously given the cross-sectional nature of the study. Mean comparisons of blood pressure across dietary pattern adherence levels revealed minimal variation in systolic blood pressure within each dietary pattern, with differences of less than 3 mmHg between adherence groups. This explains the absence of statistically significant associations between dietary patterns and systolic blood pressure. Adolescents with high adherence to the traditional/plant-based dietary pattern exhibited systolic blood pressure values comparable to those with low and moderate adherence, while similar patterns were observed for the sweetened beverage and protein/Westernized diets. These results suggest that dietary habits alone may not exert a strong influence on systolic blood pressure during adolescence, possibly due to limited duration of dietary exposure, the relatively young age of participants, or the influence of other factors such as body composition, genetic predisposition, and physical activity. This observation aligns with the findings of [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], who reported that elevated blood pressure among children and adolescents attending the University College Hospital (UCH), Ibadan, was more strongly associated with early pubertal onset than with dietary factors. Together, these findings indicate that physiological and developmental factors may play a more prominent role than diet in determining systolic blood pressure in this age group. In contrast, diastolic blood pressure showed a statistically significant variation across adherence levels of the traditional/plant-based dietary pattern. Respondents with high adherence recorded higher mean diastolic blood pressure compared with those with low and moderate adherence, while mean diastolic blood pressure values for the sweetened beverage and protein/Westernized dietary patterns showed negligible differences across adherence categories. This suggests that dietary patterns may exert a more discernible influence on diastolic rather than systolic blood pressure in adolescents. The observed association may reflect the nutritional composition of traditional diets in the study setting, particularly sodium content from cooking methods or condiments, as well as the balance of protective nutrients such as potassium and other micronutrients. The lack of significant associations between diastolic blood pressure and the sweetened beverage or protein/Westernized dietary patterns contrasts with findings by [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], who reported that adolescent diets characterized by high consumption of energy-dense foods and low intake of fruits and vegetables increase the risk of overweight, obesity, and subsequent non-communicable diseases. Similarly, [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] found that unhealthy dietary habits, especially frequent consumption of processed foods high in sugar and unhealthy fats, were associated with increased BMI, waist circumference, waist-to-height ratio, and cardiovascular risk. Differences between these studies and the present findings may reflect variations in study populations, dietary assessment methods, and cumulative exposure to unhealthy diets. While dietary patterns may not significantly influence systolic blood pressure at this stage of life, they may play a role in shaping diastolic blood pressure, an important early cardiovascular risk indicator. These results underscore the need for comprehensive interventions that integrate healthy dietary practices with weight management and physical activity. Future longitudinal studies incorporating detailed dietary composition and lifestyle factors are warranted to better elucidate the long-term impact of dietary patterns on blood pressure and cardiovascular risk among adolescents.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study identified three distinct dietary patterns among children and adolescents in Ibadan, with younger children showing greater adherence to traditional/plant-based diets and no significant sex-based differences in dietary behaviors. While dietary patterns were not significantly associated with systolic blood pressure, diastolic blood pressure showed a significant association as adherence to a traditional/plant-based diet was associated with a lower likelihood of elevated diastolic blood pressure, suggesting a potential protective effect. These findings highlight the complex role of diet in early cardiovascular regulation and underscore the importance of integrated lifestyle interventions, including healthy eating, physical activity, and weight management, for the prevention of future cardiovascular risk.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eLimitations:\u003c/h2\u003e \u003cp\u003eThe cross-sectional design of the study limits the ability to establish causal relationships between nutritional status and blood pressure; therefore, only associations can be inferred. Although blood pressure was measured twice and the average value was used for analysis, measurements were obtained during a single visit. Temporary factors such as emotional state or recent physical activity may still have influenced blood pressure readings.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical approval:\u0026nbsp;\u003c/strong\u003eThe study protocol was reviewed and approved by the UI/UCH Ethics Committee, College of Medicine, University of Ibadan (Approval number: UI/EC/25/0662), in accordance with the National Code for Health Research Ethics. All procedures involving human participants were conducted in accordance with the ethical standards of the institutional research committee and with the 1964 Declaration of Helsinki and its subsequent amendments.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate:\u0026nbsp;\u003c/strong\u003eVoluntary \u003cstrong\u003eparental informed consent\u003c/strong\u003e was obtained for all respondents in the study. For respondents who are 12 years and above, an additional freely-given individual assent was obtained before their participation in the study. Participants who wish to exit/withdraw from the study for any reason or other were granted the right to withdraw with no consequences or punishment. All information provided by the participants was handled with strict confidentiality. All procedures involving human participants were conducted in accordance with the ethical standards of the institutional research committee and with the 1964 Declaration of Helsinki and its subsequent amendments.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish:\u0026nbsp;\u003c/strong\u003eThe data collected for this manuscript does not include any information that leads to the identification of the participants. However, intention to publish was made known to the parents and guardians of the participants, and they agreed to.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement:\u0026nbsp;\u003c/strong\u003eThe datasets generated and analyzed for this study are available from the corresponding author upon reasonable request\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Declaration:\u0026nbsp;\u003c/strong\u003eThe publication of this finding is not funded by any grant. However, it should be reported that the data used in this study was obtained from the data collected in the project \u0026ndash; Development of an Automated System for Dynamic Prediction of High Blood Pressure in School Children and Adolescents which is a funded study by TETFUND. Therefore, the project has no provision for payment for the publication of manuscripts.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial Number\u003c/strong\u003e: Clinical Trial Number not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest:\u0026nbsp;\u003c/strong\u003eThe authors declare no conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAdeomi AA, Adelusi IO, Adedeji PO, Awofeso AE, Oroleye OO, Gbadegesin DL. Nutritional status and Cardiometabolic health among adolescents: Findings from southwestern Nigeria. BMC Nutr. 2019;5(1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s40795-019-0308-5\u003c/span\u003e\u003cspan address=\"10.1186/s40795-019-0308-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAkinola AO. The Food Consumption Pattern of Adolescents in Ibadan. World Nutr. 2023;14(4):26\u0026ndash;32. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.26596/wn.202314426-32\u003c/span\u003e\u003cspan address=\"10.26596/wn.202314426-32\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAsaolu S, Zhang B, Mary AM, Kibenja D, Ma J, Said S, Adeniyi I, Barrow LF. Exploring the relationship between dietary patterns and obesity among Nigerian adults: a cross-sectional study. BMC Public Health. 2024;24(1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12889-024-18792-4\u003c/span\u003e\u003cspan address=\"10.1186/s12889-024-18792-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAtoh I, Ezeogu J, Okeke CV, Umeh SI, Ekure E, Omokhodion SI, Njokanma FO. High blood pressure pattern amongst adolescents in Lagos, South West Nigeria. Pan Afr Med J. 2023;44:206. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.11604/pamj.2023.44.206.38670\u003c/span\u003e\u003cspan address=\"10.11604/pamj.2023.44.206.38670\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIzadi A, Khedmat L, Tavakolizadeh R, Motahedi SY. The intake assessment of diverse dietary patterns on childhood hypertension: Alleviating the blood pressure and lipidemic factors with low-sodium seafood rich in omega-3 fatty acids. Lipids Health Dis. 2020;19(1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12944-020-01245-3\u003c/span\u003e\u003cspan address=\"10.1186/s12944-020-01245-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLobstein T, Powis J, Jackson-Leach R. (2024). \u003cem\u003eWorld Obesity Atlas 2024\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://data.worldobesity.org/publications/?cat=22\u003c/span\u003e\u003cspan address=\"https://data.worldobesity.org/publications/?cat=22\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNational Heart, Lung, and Blood Institute. (2005). \u003cem\u003eThe fourth report on the diagnosis, evaluation, and treatment of high blood pressure in children and adolescents\u003c/em\u003e (NIH Publication No. 05-5267). U.S. Department of Health and Human Services. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.nhlbi.nih.gov/files/docs/resources/heart/hbp_ped.pdf\u003c/span\u003e\u003cspan address=\"https://www.nhlbi.nih.gov/files/docs/resources/heart/hbp_ped.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNational Population Commission. (2009). \u003cem\u003e2006 population and housing census of the Federal Republic of Nigeria: National and state population and housing tables: Priority tables, volume 1\u003c/em\u003e (Priority Tables). Retrieved from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://catalog.ihsn.org//catalog/3340/download/48519\u003c/span\u003e\u003cspan address=\"https://catalog.ihsn.org//catalog/3340/download/48519\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOluwayemi IO, Oyedeji OA. (2021). A ten-year review of childhood obesity in a teaching hospital, South West Nigeria. In \u003cem\u003eNigerian Journal of Clinical Practice\u003c/em\u003e (Vol. 24, Issue 6, pp. 841\u0026ndash;846). Wolters Kluwer Medknow Publications. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4103/njcp.njcp_595_20\u003c/span\u003e\u003cspan address=\"10.4103/njcp.njcp_595_20\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePapka NY, Babaniyi IB, Aikhionbare HA, Oladele JT, Chinawa JM. Blood Pressure Pattern and Prevalence of Hypertension amongst Apparently Healthy Primary School Pupils in Abuja, Nigeria. Nigerian Postgrad Med J. 2024;31(2):111\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4103/npmj.npmj_254_23\u003c/span\u003e\u003cspan address=\"10.4103/npmj.npmj_254_23\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eP\u0026eacute;rez-Gimeno G, Rup\u0026eacute;rez AI, V\u0026aacute;zquez-Cobela R, Herr\u0026aacute;iz-Gastesi G, Gil-Campos M, Aguilera CM, Moreno LA, Trabazo MRL, Bueno-Lozano G. Energy-dense salty food consumption frequency is associated with diastolic hypertension in Spanish children. Nutrients. 2020;12(4). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/nu12041027\u003c/span\u003e\u003cspan address=\"10.3390/nu12041027\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRabi, D. M., McBrien, K. A., Sapir-Pichhadze, R., Nakhla, M., Ahmed, S. B., Dumanski, S. M.,Butalia, S., Leung, A. A., Harris, K. C., Cloutier, L., Zarnke, K. B., Ruzicka, M.,Hiremath, S., Feldman, R. D., Tobe, S. W., Campbell, T. S., Bacon, S. L., Nerenberg,K. A., Dresser, G. K., \u0026hellip; Daskalopoulou, S. S. (2020). Hypertension Canada\u0026rsquo;s 2020 Comprehensive Guidelines for the Prevention, Diagnosis, Risk Assessment, and Treatment of Hypertension in Adults and Children. \u003cem\u003eCanadian Journal of Cardiology\u003c/em\u003e, \u003cem\u003e36\u003c/em\u003e(5), 596\u0026ndash;624. https://doi.org/10.1016/j.cjca.2020.02.086.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTang X, Liu Y, Hu J, Zhai L, Jia L, Ding N, Ma Y, Wen D. Association of waist circumference with blood pressure and familial dietary habits in preschool children: a cross-sectional study in northeastern China. Ital J Pediatr. 2022;48(1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s13052-022-01236-3\u003c/span\u003e\u003cspan address=\"10.1186/s13052-022-01236-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThomas J, Stonebrook E, Kallash M. Pediatric hypertension: Review of the definition, diagnosis, and initial management. Int J Pediatr Adolesc Med. 2022;9(1):1\u0026ndash;6. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ijpam.2020.09.005\u003c/span\u003e\u003cspan address=\"10.1016/j.ijpam.2020.09.005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFlynn JT, Kaelber DC, Baker-Smith CM, Blowey D, Carroll AE, Daniels SR, de Ferranti SD, Dionne JM, Falkner B, Flinn SK, Gidding SS, Goodwin C, Leu MG, Powers ME, Rea C, Samuels J, Simasek M, Thaker VV, Urbina EM. \u0026amp; Subcommittee on Screening and Management of High Blood Pressure in Children. (2017). Clinical practice guideline for screening and management of high blood pressure in children and adolescents. Pediatrics, 140(3), e20171904. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1542/peds.2017-1904\u003c/span\u003e\u003cspan address=\"10.1542/peds.2017-1904\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Public Health](https://link.springer.com/journal/12982)","snPcode":"12982","submissionUrl":"https://submission.springernature.com/new-submission/12982/3","title":"Discover Public Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Child \u0026 adolescent health, blood pressure, cardiovascular disease burden, Nigeria","lastPublishedDoi":"10.21203/rs.3.rs-9303561/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9303561/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eDietary intake of foods high in fat and sugar in children and adolescents can potentially increase the risk of developing elevated blood pressure at a younger age. Studying the dietary factors associated with elevated blood pressure in children and adolescents is crucial for early detection of modifiable risk factors. This study determines the dietary correlates of elevated blood pressure among children and adolescents in Ibadan, Nigeria.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eIn a descriptive cross-sectional study, 335 adolescents were enrolled using a multi-stage sampling technique. Data collected from the respondents was analysed using Statistical Package for the Social Sciences (IBM SPSS Version 27.0) and R Studio. Descriptive statistics were used to report the findings. Principal Component Analysis was used to identify the dietary patterns. Chi-square tests, independent samples t-tests, one-way ANOVA, and regression analysis were used to test the association and relationships between variables. The significance level was set at 5% at a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eTraditional/plant-based diet, sweetened beverage, and westernized diet were identified. Adherence to these patterns includes: low (33.1%), moderate (33.4%), and high (33.4%). Mean SBP ranges from 102.3\u0026thinsp;\u0026plusmn;\u0026thinsp;15.3 mmHg to 104.4\u0026thinsp;\u0026plusmn;\u0026thinsp;14.4 mmHg for the traditional/plant-based pattern, 102.3\u0026thinsp;\u0026plusmn;\u0026thinsp;14.1 mmHg to 103.7\u0026thinsp;\u0026plusmn;\u0026thinsp;15.8 mmHg for the sweetened beverage pattern, and 102.5\u0026thinsp;\u0026plusmn;\u0026thinsp;16.2 mmHg to 103.4\u0026thinsp;\u0026plusmn;\u0026thinsp;14.4 mmHg for the protein/Westernized pattern. Children and adolescents with high adherence to the traditional/plant-based diet had significantly lower odds (OR\u0026thinsp;=\u0026thinsp;0.43, 95% CI: 0.20\u0026ndash;0.92, p\u0026thinsp;=\u0026thinsp;0.029) of elevated diastolic blood pressure compared to those with low adherence. Therefore, findings support the promotion of traditional, plant-based diets among adolescents and children.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eWhile no association was observed between dietary patterns and systolic blood pressure, adherence to a traditional/plant-based diet was associated with reduced odds of elevated diastolic blood pressure, indicating a potential protective effect.\u003c/p\u003e","manuscriptTitle":"Dietary Correlates of Elevated Blood Pressure Among Children and Adolescents in Ibadan, Southwestern, Nigeria","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-13 06:18:30","doi":"10.21203/rs.3.rs-9303561/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-18T21:14:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"61889609200732449618346709748287749836","date":"2026-05-12T15:03:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"244115685192398419429469285411658365333","date":"2026-05-10T16:29:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"22952619269758064618063049101004178168","date":"2026-05-09T07:23:25+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-05T04:43:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"188288588805932027976022420914509070623","date":"2026-05-04T14:55:51+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-05-04T14:14:46+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-13T05:35:41+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-11T03:52:26+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-09T13:04:25+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Public Health","date":"2026-04-09T11:34:06+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Public Health](https://link.springer.com/journal/12982)","snPcode":"12982","submissionUrl":"https://submission.springernature.com/new-submission/12982/3","title":"Discover Public Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0523d825-0c1b-4369-831d-dbea7f4ab33c","owner":[],"postedDate":"May 13th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-18T21:14:44+00:00","index":74,"fulltext":""},{"type":"reviewerAgreed","content":"61889609200732449618346709748287749836","date":"2026-05-12T15:03:00+00:00","index":73,"fulltext":""},{"type":"reviewerAgreed","content":"244115685192398419429469285411658365333","date":"2026-05-10T16:29:28+00:00","index":70,"fulltext":""},{"type":"reviewerAgreed","content":"22952619269758064618063049101004178168","date":"2026-05-09T07:23:25+00:00","index":58,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-05T04:43:59+00:00","index":40,"fulltext":""},{"type":"reviewerAgreed","content":"188288588805932027976022420914509070623","date":"2026-05-04T14:55:51+00:00","index":38,"fulltext":""},{"type":"reviewersInvited","content":"40","date":"2026-05-04T14:14:46+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-13T06:18:30+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-13 06:18:30","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9303561","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9303561","identity":"rs-9303561","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}