Diet quality and anthropometric parameter of in-school female adolescents in Ogun State, Nigeria: Descriptive Cross Sectional Study

Diet quality and anthropometric parameter of in-school female adolescents in Ogun State, Nigeria: Descriptive Cross Sectional Study | 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 Diet quality and anthropometric parameter of in-school female adolescents in Ogun State, Nigeria: Descriptive Cross Sectional Study Dare D. Adémiluyi, Akinade E. Ogunniyi, Yewande O. Uthman-Akinhanmi, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8689293/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 14 You are reading this latest preprint version Abstract Background Adolescence is a critical period for establishing dietary patterns that influence growth, body composition, and long-term risk of non-communicable diseases. In Nigeria, limited population-based evidence exists on diet quality and its relationship with anthropometric status among female adolescents, particularly using standardized global diet quality indicators. Methods This descriptive cross-sectional study was conducted among 290 in-school female adolescents aged 13–17 years in public and private secondary schools in Odeda Local Government Area, Ogun State. Dietary intake was assessed using the Dietary Quality Questionnaire for Nigeria, generating Dietary Diversity Score (DDS), NCD-Protect, NCD-Risk, Global Dietary Recommendation (GDR) score, and Minimum Dietary Diversity for Women (MDD-W). Anthropometric indices were obtained using standardized procedures, and BMI-for-age and height-for-age z-scores were computed with WHO AnthroPlus. Independent t-tests, chi-square tests, and Pearson correlation analyses were performed. Results Approximately 60.7% of participants met the MDD-W threshold. Staple foods were widely consumed, whereas intake of fruits, vegetables, pulses, and nuts remained suboptimal. High consumption of baked sweets, deep-fried foods, and sugar-sweetened beverages was observed. The mean DDS and GDR scores were modest, with public school adolescents exhibiting significantly higher GDR scores than private school counterparts (p = 0.011). DDS was positively correlated with GDR (r = 0.279, p < 0.05). Anthropometrically, 80.0% had normal BMI-for-age, 15.9% were thin, and 4.1% were overweight or obese, indicating a double burden of malnutrition. No significant association was found between diet quality indicators and BMI-for-age z-scores. Conclusion Female adolescents in Ogun State exhibit moderate dietary diversity alongside high exposure to NCD-risk foods and a coexisting burden of thinness and emerging overweight. School-based nutrition policies and food-environment interventions are urgently needed to improve diet quality during this critical life stage. Diet quality Female adolescent nutrition Dietary diversity Anthropometry Food group consumption pattern Figures Figure 1 Figure 2 Figure 3 Figure 4 1 Introduction 1.1 Study background Adolescence represents a critical transitional period characterized by rapid physical growth, hormonal changes, and cognitive development, during which nutritional requirements are substantially elevated. Dietary behaviors established during this stage exert long-lasting influences on body composition, metabolic health, and risk of non-communicable diseases (NCDs) in adulthood [ 12 , 7 ]. Globally, however, adolescent diets are increasingly characterized by low consumption of fruits, vegetables, whole grains, and legumes alongside rising intake of energy-dense, ultra-processed foods, reflecting the ongoing global nutrition transition [ 18 , 15 ]. Diet quality has emerged as a more comprehensive indicator of nutritional adequacy than single-nutrient approaches, as it captures overall dietary patterns and alignment with dietary guidelines. Poor diet quality during adolescence has been consistently associated with micronutrient deficiencies, impaired growth, excess adiposity, and early metabolic risk [ 2 , 11 ]. Consequently, population-level assessment of diet quality using validated dietary diversity and dietary recommendation indicators is increasingly prioritized in global nutrition surveillance. Parallel to dietary challenges, adolescents in low- and middle-income countries (LMICs) are experiencing a double burden of malnutrition, where undernutrition coexists with emerging overweight and obesity [19; 16, 22]. Sub-Saharan Africa, in particular, is witnessing rapid urbanization and shifts in food environments that promote consumption of refined grains, sugar-sweetened beverages, fried foods, and processed snacks, while access to nutrient-dense foods remains limited [ 21 , 6 ]. These transitions place adolescents at heightened risk of both growth faltering and diet-related NCDs. In Nigeria, adolescents constitute nearly one-quarter of the population, yet remain under-represented in national nutrition surveillance. Existing evidence indicates widespread inadequacies in dietary diversity, high consumption of sugar-sweetened beverages, and rising prevalence of thinness and overweight among Nigerian adolescents [ 1 , 9 ]. Studies have further shown that school environment and socioeconomic context significantly shape adolescent dietary behaviors, with disparities observed between public and private school students [ 4 ]. Ogun State, located in southwest Nigeria, is undergoing rapid peri-urban expansion with increasing exposure of adolescents to modern food environments. Despite this, empirical data on adolescent diet quality and its relationship with anthropometric parameters in this region remain limited. Previous studies in Ogun State have primarily focused on nutrition knowledge and isolated dietary behaviors, with limited integration of standardized global diet quality indicators and anthropometric outcomes [ 4 ]. Moreover, few studies have jointly examined food group consumption patterns, NCD-risk foods, NCD-protective foods, minimum dietary diversity, and anthropometric indices within the same adolescent population. Anthropometric indicators such as height-for-age and BMI-for-age remain essential markers of nutritional status, reflecting both chronic and acute nutritional exposures. However, emerging evidence suggests that anthropometric measures alone may not fully capture diet-related health risks during adolescence, emphasizing the need to interpret them alongside comprehensive diet quality indicators [ 3 , 7 ]. Understanding how diet quality relates to anthropometric outcomes in adolescents is therefore crucial for informing targeted nutrition interventions and school-based policies. Recent work by Ademiluyi and colleagues in Ogun State has highlighted suboptimal nutrition knowledge, moderate dietary diversity, and early signs of the double burden of malnutrition among in-school adolescents [ 4 ]. However, there remains a critical gap in descriptive population-level evidence that integrates global diet quality indicators with anthropometric parameters among female adolescents, a group particularly vulnerable to micronutrient deficiencies and future maternal health risks. Addressing this gap is essential, as adolescent girls represent a pivotal population for intergenerational nutrition improvement. Poor diet quality and suboptimal growth during adolescence not only compromise current health but also influence future pregnancy outcomes, offspring growth, and long-term NCD risk [ 20 , 17 ]. Therefore, this study aimed to describe diet quality and anthropometric parameters among in-school female adolescents in Ogun State, Nigeria, using standardized global dietary quality indicators and WHO anthropometric references. By providing population-level evidence on food group consumption patterns, NCD-risk and NCD-protective foods, dietary diversity, and nutritional status, this study seeks to inform school-based nutrition policies and adolescent health interventions in southwest Nigeria and similar LMIC contexts. 2. Methodology 2.1 Study Design and Population A descriptive cross-sectional study design was employed to examine the associations between diet quality and anthropometric indices among in-school female adolescents in public and private secondary schools within Odeda LGA. 2.2 Study Area This study was conducted in Odeda Local Government Area (LGA), Ogun State, Nigeria, which is located along the Abeokuta-Ibadan Road, approximately 10 kilometers from Abeokuta, the state capital. The region comprises several towns and villages with a strong agricultural presence and a growing commitment to health and education, making it a suitable location for research on adolescent nutrition. The Odeda LGA has both, public and private secondary schools providing a diverse educational setting for the study. 2.3 Study Criteria The study included adolescents aged 13 to 17 years enrolled in public or private secondary schools in Odeda LGA. Participants had to be available during the study period, willing to provide informed assent, and have parental or guardian consent. Female adolescents with chronic illnesses affecting anthropometric measurements were excluded. Additional exclusion criteria included those with diagnosed eating disorders, diabetes, or other chronic conditions, unwillingness to participate, and lack of parental consent. 2.4 Sampling Techniques Odeda Local Government Area (LGA) comprises three administrative zones: Odeda, Ilugun, and Opeji. The Opeji Zone was randomly selected using a simple random sampling technique. Within this zone, five communities exist: Obantoko, Adao, Alabata, Opeji, and Obete. Obantoko was purposively chosen because it has the largest population and a diverse socio-demographic profile, encompassing both urban and peri-urban settings with a mix of public and private secondary schools representing varied socio-economic backgrounds. From the list of registered secondary schools in Obantoko, four (two public and two private) were purposively selected to capture this diversity. Within each school, students were stratified by class level (Senior Secondary 1–3), and proportional allocation determined the number of participants per class. Finally, students were selected through simple random sampling, yielding a total of 290 female adolescents aged 13–17 years. 2.5 Sample Size Determination The minimum sample size for this cross-sectional study was estimated using Cochran’s formula. The prevalence of adequate dietary diversity (56.5%) reported by [ 4 ] was used as the expected proportion. A 95% confidence level (Z = 1.96) and a margin of error of 5% (d = 0.05) were applied, resulting in an initial sample size of 379 participants. Because the study population of female adolescents in the selected schools was finite, the sample size was adjusted using the finite population correction (FPC). Based on the total female adolescent population in the selected schools, the corrected sample size was approximately 264. To account for potential non-response, a 10% adjustment was applied, yielding a final minimum sample size of 290 adolescents. 2.6 Data collection and study procedures Data were collected using a pretested, interviewer-administered semi-structured questionnaire designed to obtain information on sociodemographic characteristics, diet quality, and anthropometric indices. Trained research assistant adminstered questionaire within the school environment between August and November 2024, during regular academic sessions and outside examination periods to minimize disruption and participant fatigue. Class teachers assisted only with logistical coordination and were not present during data collection to reduce response bias. 2.7 Diet quality assessment Dietary intake was assessed using the Dietary Quality Questionnaire for Nigeria developed by the Global Diet Quality Project [ 10 , 11 ]. Dietary Diversity Score was derived from consumption of ten food groups, including grains, white roots and tubers, and plantains; pulses; nuts and seeds; dairy; meat, poultry, and fish; eggs; dark green leafy vegetables; vitamin A–rich fruits and vegetables; other vegetables; and other fruits. The NCD-Protect score reflected adherence to dietary recommendations on foods encouraged for consumption and was based on intake of whole grains, pulses, nuts and seeds, vitamin A–rich orange vegetables, dark green leafy vegetables, other vegetables, vitamin A–rich fruits, citrus fruits, and other fruits. The NCD-Risk score captured intake of foods recommended for limitation, including soft drinks, baked or grain-based sweets, other sweets, processed meats, unprocessed red meat, deep-fried foods, fast foods and instant noodles, and packaged ultra-processed salty snacks. Overall adherence to global dietary recommendations was quantified using the Global Dietary Recommendation score, calculated as the difference between the NCD-Protect and NCD-Risk scores plus a constant of nine, yielding a possible range of 0 to 18, with higher scores indicating greater adherence to dietary patterns protective against non-communicable diseases [ 10 , 4 ]. Minimum Dietary Diversity for Women was defined as consumption of at least five of the ten standard food groups within the reference period, and participants were classified accordingly to indicate achievement or non-achievement of minimum dietary diversity [ 23 , 10 ]. Diet quality indicators, including food group consumption patterns (derived from the Dietary Diversity Score), NCD-risk foods, NCD-protective foods, and Minimum Dietary Diversity for Women (MDD-W), were classified using the standard Global Diet Quality Indicator Guide based on the proportion of food groups consumed by participants [ 10 ]. Food group consumption patterns were summarized as proportions and illustrated graphically using bar charts to describe adherence to each diet quality indicator among female adolescents. Each indicator was defined by specific food groups consistent with global dietary quality frameworks. 2.8 Anthropometric assessment Height and weight were measured using standardized WHO procedures. Height-for-age and BMI-for-age z-scores were generated using WHO AnthroPlus software. Anthropometric indices (weight, height, BMI, and MUAC) were assessed using standardised procedures recommended by the CDC (2020). BMI-for-age Z-scores were computed using WHO cut-offs: severe thinness ( < − 3SD), thinness (− 3SD to + 1SD to +2SD), and obesity ( > + 2SD) [ 4 ]. 2.9 Ethical considerations Ethical approval for the study was obtained from the Health Research Ethics Committee of the Federal Medical Centre, Abeokuta (FMCA/470/HREC/01/2023/56). Written informed consent was obtained from parents or guardians, and informed assent was obtained from all participating adolescents. Confidentiality was ensured through anonymized coding and restricted access to study records. Participation was voluntary, and the study adhered to internationally accepted ethical standards for research involving human participants. 2.9 Statistical analysis Data were analyzed using IBM SPSS version 27 and Microsoft Excel 2016. Continuous variables, including dietary diversity, Global Dietary Recommendation scores, nutrition knowledge scores, and anthropometric z-scores, were summarized using means and standard deviations, while categorical variables were summarized using frequencies and percentages. Independent-sample t tests were used to compare mean diet quality indicators between adolescents attending public and private schools. Chi-square tests were applied to examine differences in categorical anthropometric classifications by school setting. Pearson correlation analysis was conducted to assess relationships among dietary diversity, Global Dietary Recommendation scores, and BMI-for-age z-scores. All statistical tests were two-tailed, and statistical significance was set at p < 0.05. 3 Results 3.1 Sociodemographic and economic characteristics of the female adolescents The mean age of participants was 15.19 ± 1.05 years, with most aged 15 years. Slightly more than half attended public schools (55.2%). Participants were predominantly Yoruba (96.6%). Nearly half were in SS1 (46.2%). Most parents had at least secondary education, with approximately 47% attaining tertiary education. Over half of households reported monthly incomes above ₦100,000. The mean household size was 5.38 ± 1.84, and 69.7% lived with both parents. Table 1 Sociodemographic and economic characteristics of the female adolescents Variable Frequency Percent Age (years) 13 14 4.8 14 51 17.6 15 137 47.2 16 43 14.8 17 45 15.5 Total 290 100 Mean ± S.D: 15.19 ± 1.05 School Settings Public School 160 55.2 Private School 130 44.8 Total 290 100 Ethnic group Yoruba 280 96.6 Igbo 10 3.4 Total 290 100 Class SS1 134 46.2 SS2 70 24.1 SS3 86 29.7 Total 290 100 Father highest level of education No formal education 6 2.1 Primary education 12 4.1 Secondary education 134 46.2 Tertiary education 138 47.6 Total 290 100 Mother highest level of education No formal education 10 3.4 Primary education 28 9.7 Secondary education 116 40 Tertiary education 136 46.9 Total 290 100 Household's Monthly Income Less than 20,000 40 13.8 20,000–50,000 52 17.9 50,001–100,000 44 15.2 More than 100,000 154 53.1 Total 290 100 Number of Household 0–4 86 29.7 5–8 190 65.5 9–12 14 4.8 Total 290 100 Mean ± S.D: 5.38 ± 1.84 Who do you live with? Both Parent 202 69.7 Mother only 44 15.2 Father only 6 2.1 Guardian 20 6.9 Other relatives 18 6.2 Total 290 100 3.2 Food Group Consumption, NCD-Risk, NCD-Protective, and Minimum Dietary Diversity for Women by School Setting Figure 1 . presents the distribution of food group consumption among in-school adolescents by school setting. Consumption of grains, white roots and tubers, and plantains was nearly universal, reported by 97.2% of participants overall, with a higher prevalence among public school students (99%) compared with private school students (95%). Intake of pulses was reported by 33.4% of adolescents overall, with higher consumption among private school students (38%) than public school students (30%). Dark green leafy vegetables were consumed by 58.3% of participants, with similar patterns between private (62%) and public (56%) schools. Other vegetables were consumed by 56.2% overall, with a higher proportion among private school students (65%) compared with public school students (49%). Consumption of other vitamin A–rich fruits and vegetables was low across both school settings (32.4% overall). Other fruits were consumed by 35.9% of participants, with markedly higher intake among public school students (50%) compared with private school students (18%). Egg consumption was reported by 41.7% of adolescents, with comparable proportions between school types. Nearly half of the adolescents (47.9%) consumed dairy products, with similar distribution across school settings. Meat, poultry, and fish were widely consumed (87.9% overall), with higher prevalence among private school students (92%) than public school students (85%). Nuts and seeds had the lowest consumption frequency, reported by 29.0% of participants overall. Figure 2 . illustrates the consumption patterns of foods associated with increased non-communicable disease (NCD) risk. Baked or grain-based sweets were consumed by 79.7% of adolescents overall, with higher consumption among public school students (82.5%) compared with private school students (76.2%). Consumption of other sweets was equally distributed overall (50.0%), with higher prevalence among private school students (56.2%) than public school students (45.0%). Processed meats were consumed by 23.8% of participants, while unprocessed red meat consumption was reported by 44.8%, with slightly higher intake among private school students. Packaged ultra-processed salty snacks were consumed by 16.2% of adolescents, with minimal variation between school settings. More than half of the participants (54.8%) reported consuming deep-fried foods, with higher prevalence among public school students (58.1%) than private school students (50.8%). Consumption of soft drinks was reported by 44.8% of adolescents, with substantially higher intake among private school students (56.2%) compared with public school students (35.6%). Fast food consumption was reported by 27.9% overall, with a higher proportion among public school students (31.2%) than private school students (23.8%). Figure 3 . shows the consumption of food groups considered protective against NCDs. Whole grains were consumed by 23.8% of adolescents overall, with higher intake among public school students (27.5%) compared with private school students (19.2%). Pulse consumption was reported by 33.4% of participants, with higher prevalence among private school students (37.7%) than public school students (30.0%). Intake of vitamin A–rich orange vegetables was low across both groups (12.1% overall). Dark green leafy vegetables were consumed by 58.3%, with similar distribution across school settings. Other vegetables were consumed by 56.2% of adolescents, with higher intake among private school students (65.4%) compared with public school students (48.8%). Vitamin A–rich fruits were consumed by 21.4%, with slightly higher consumption among public school students. Consumption of citrus fruits was reported by 15.2% overall, with higher prevalence among public school students (22.5%) compared with private school students (6.2%). Other fruits were consumed by 27.6% of participants, with higher intake among public school students (36.3%) than private school students (16.9%). Nuts and seeds were consumed by 29.0% of adolescents, with similar proportions across school settings. Figure 4 . presents the distribution of adolescents according to Minimum Dietary Diversity for Women (MDD-W) classification. Overall, 60.7% of participants met the MDD-W criterion, while 39.3% fell below the minimum threshold. Among public school students, 74.0% met the MDD-W, compared with 62.0% of private school students. Conversely, 49.0% of public school students and 38.0% of private school students were classified as having dietary diversity below the MDD-W threshold. 3.3 Pearson Correlation among DDS, GDR, and BMI-for-Age Z-Score Table 2 presents the Pearson correlation matrix examining relationships among dietary diversity score (DDS), global dietary recommendation score (GDR), and BMI-for-age z-score (BAZ). A positive and statistically significant correlation was observed between DDS and GDR (r = 0.279, p < 0.05), indicating that higher dietary diversity was associated with higher adherence to global dietary recommendations. No significant correlations were observed between DDS and BAZ (r = 0.004) or between GDR and BAZ (r = 0.009), suggesting that dietary quality indicators were not linearly associated with BMI-for-age z-scores in this population. Table 2 Pearson Correlation among DDS, GDR, and BMI-for-Age Z-Score Variable DDS GDR BAZ DDS 1.000 GDR 0.279* 1.000 BMI for age 0.004 0.009 1.000 * Correlation is significant at p < 0.05. GDR, global dietary recommendations; DDS, Dietary Diversity Score 3.4 Diet Quality Indicators by School Setting Table 3 . compares diet quality indicators between adolescents attending public and private schools. The mean Dietary Diversity Score (DDS) was slightly higher among public school students (5.18 ± 2.08) compared with private school students (4.94 ± 1.90); however, the mean difference was not statistically significant (MD = 0.243; p = 0.304). Similarly, the NCD-Protect score was marginally higher among public school adolescents (2.83 ± 1.76) than private school adolescents (2.51 ± 1.41) were, though this difference did not reach statistical significance (MD = 0.324; p = 0.090). The NCD-Risk score was slightly higher among private school students (3.91 ± 2.22) compared with those in public schools (3.57 ± 2.06), with no statistically significant difference observed (MD = − 0.339; p = 0.179). In contrast, the Global Dietary Recommendation (GDR) score was significantly higher among public school adolescents (8.26 ± 2.17) than private school adolescents (7.60 ± 2.23), with a mean difference of 0.662 and a 95% confidence interval of 0.153 to 1.172 (p = 0.011). Table 3 Diet Quality Indicators 95% C.I of the Difference Indicator School Settings N M ± S.D MD Lower Upper P-Value ↕ DDS (0–10)a Public School 160 5.18 ± 2.08 0.243 -0.221 0.707 0.304 Private School 130 4.94 ± 1.90 NCD-Protect Score (0–9)b Public School 160 2.83 ± 1.76 0.324 -0.051 0.698 0.090 Private School 130 2.51 ± 1.41 NCD-Risk Score (0–9)c Public School 160 3.57 ± 2.06 -0.339 -0.834 0.156 0.179 Private School 130 3.91 ± 2.22 GDR Score (0–18)d Public School 160 8.26 ± 2.17 0.662 0.153 1.172 0.011* Private School 130 7.6 ± 2.23 ↕ Statistical analysis: Independent T test M, Mean; S.D, Standard Deviation; MD, Mean Difference; N, Frequency; GDR, global dietary recommendations; NCD, non-communicable disease. a Dietary diversity Score (DDS) includes ten food groups: (1) grains, white roots and tuber, and plantains; (2) pulses (beans, peas and lentils); (3) nuts and seeds; (4) dairy; (5) meat, poultry and fish; (6) eggs; (7) dark green leafy vegetables; (8) other vitamin A-rich fruits and vegetables; (9) other vegetables; (10) other fruits. b NCD – protect score measures adherence to global dietary recommendations on foods to consume: (1) whole grains; (2) pulses; (3) nuts and seeds; (4) vitamin A-rich orange vegetables; (5) dark green leafy vegetables; (6) other vegetables; (7) vitamin A-rich fruits; (8) citrus; (9) other fruits. c NCD – risk score measures adherence to global dietary recommendations on foods to limit including: (1) soft drinks; (2) baked/grain-based sweets; (3) other sweets; (4) processed meats; (5) unprocessed meat; (6) deep fried food; (7) fast food and instant noodles; (8) packaged ultra-processed salty snacks d GDR score = (NCD – Protect – NCD – Risk) + 9; measures adherence to global dietary recommendations protective against non-communicable diseases. 3.5 Anthropometric Indices of Female Adolescents by School Setting Table 4 describes the anthropometric status of the adolescents by school setting. Based on height-for-age classification, the majority of participants were normal (95.9%), with 4.1% classified as moderately stunted. The prevalence of normal height-for-age was similar between public (95.0%) and private (97.0%) school students, with no statistically significant difference (p = 0.891). The mean height-for-age z-score for the overall sample was − 0.25 ± 0.97. Regarding BMI-for-age classification, 80.0% of adolescents had a normal BMI-for-age, while 13.1% were moderately thin and 2.8% were severely thin. Overweight and obesity were observed in 3.4% and 0.7% of participants, respectively. Severe thinness was reported only among public school students (5.0%), whereas overweight and obesity were more prevalent among private school students. However, differences in BMI-for-age categories between school settings were not statistically significant (p = 0.162). The overall mean BMI-for-age z-score was − 0.71 ± 1.13. Table 4 Anthropometric Indices of the female adolescent School Setting Classification Overall Public School Private School P-Value ↕ Height for age N (%) N (%) N (%) Normal 278 (95.9) 152 (95) 126 (97) 0.891 Moderate Stunting 12 (4.1) 8 (5) 4 (3) Total 290 (100) 160 (100) 130 (100) Mean + S.D= -0.25 + 0.97 BMI for age Severe thinness 8 (2.8) 8 (5) 0 (0) 0.162 Moderate thinness 38 (13.1) 24 (15) 14 (11) Normal 232 (80) 126 (79) 106 (82) Overweight 10 (3.4) 2 (1) 8 (6) Obesity 2 (0.7) 0 (0) 2 (2) Total 290 (100) 160 (0) 130 (100) Mean + S.D = -0.71 + 1.13 ↕ Statistical analysis: Independent T test S.D, Standard Deviation; N, Frequency 4 Discussion This study provides a comprehensive evaluation of diet quality, dietary patterns, and anthropometric status among female in-school adolescents in southwest Nigeria, with explicit consideration of differences by school setting. By jointly examining food group consumption, exposure to dietary factors associated with non-communicable disease (NCD) risk, minimum dietary diversity, and anthropometric indicators, the findings contribute to the growing body of evidence on adolescent nutrition in low- and middle-income countries (LMICs) undergoing rapid nutrition transition. Overall, the results reveal persistent inadequacies in the consumption of nutrient-dense foods alongside substantial exposure to energy-dense, ultra-processed foods, highlighting the coexistence of undernutrition and emerging overnutrition during a critical developmental period. The sociodemographic characteristics of the study population particularly the mean age of 15.2 ± 1.1 years and the predominance of public school attendance are consistent with previous reports describing adolescent school enrollment patterns in urban Nigeria. The overwhelming representation of Yoruba ethnicity reflects the regional context of southwest Nigeria and, while limiting ethnic heterogeneity, provides focused insight into dietary and nutritional patterns within this population group [ 9 ]. 4.1 Food Group Consumption Pattern Food group consumption patterns demonstrated near-universal consumption of staple carbohydrate-rich foods, including grains, roots, tubers, and plantains, reflecting traditional dietary practices widely reported across sub-Saharan Africa [ 21 ]. In contrast, consumption of several nutrient-dense protective food groups particularly pulses, nuts and seeds, and vitamin A–rich fruits and vegetables was consistently low. Similar gaps in adolescent consumption of micronutrient-rich foods have been documented across LMIC settings and are indicative of limited diet quality despite apparent caloric adequacy [ 15 ]. The low intake of citrus fruits and vitamin A–rich orange vegetables is of particular concern given their established role in supporting micronutrient adequacy, immune function, and long-term health [ 20 ]. School setting emerged as an important contextual factor influencing specific dietary behaviors. Adolescents attending private schools reported higher consumption of pulses and other vegetables, whereas public school students had higher intake of grains and other fruits. These differences likely reflect underlying socioeconomic gradients in food access, affordability, and food environments, as well as differences in household food provisioning practices [ 5 , 6 ]. Such findings reinforce the notion that adolescent dietary behaviors are shaped by intersecting influences operating at household, school, and community levels. Of particular concern is the high prevalence of foods associated with increased NCD risk. Nearly four in five adolescents reported consumption of baked or grain-based sweets, and more than half consumed deep-fried foods, reflecting dietary shifts characteristic of the nutrition transition toward energy-dense, nutrient-poor diets [ 17 ]. The higher consumption of sugar-sweetened beverages among private school students aligns with evidence from urban Nigerian and other LMIC settings indicating greater access to commercially processed beverages among adolescents from relatively higher socioeconomic backgrounds [ 9 ]. Although processed meat consumption was comparatively low, its presence remains noteworthy given consistent evidence linking even modest intake to adverse cardiometabolic outcomes later in life [ 13 ]. 4.2 Dietary Diversity, Diet Quality, and Global Recommendations Approximately 61% of participants met the Minimum Dietary Diversity for Women (MDD-W) threshold, suggesting moderate dietary diversity at the population level. Nonetheless, nearly two-fifths of adolescents failed to achieve the minimum threshold, indicating substantial risk of inadequate micronutrient intake. Studies among adolescent girls show very low dietary diversity and substantial micronutrient inadequacy, contributing to deficiencies in iron, zinc, and calcium [ 14 ]. While MDD-W was originally validated for women of reproductive age, accumulating evidence supports its use as a proxy indicator of diet quality among adolescents, particularly in resource-limited settings where more detailed dietary assessment may be infeasible [ 2 , 8 ]. The observed positive association between the Dietary Diversity Score (DDS) and the Global Dietary Recommendation (GDR) score supports the construct validity of dietary diversity as an important component of overall diet quality. This finding is consistent with prior research demonstrating that greater dietary diversity is associated with improved alignment with dietary guidelines and higher micronutrient adequacy among adolescents [ 2 ]. In contrast, neither DDS nor GDR was significantly associated with BMI-for-age z-scores. This lack of association aligns with growing recognition that BMI may not adequately capture the nutritional implications of diet quality during adolescence, a period characterized by rapid growth, pubertal development, and substantial interindividual variability in body composition [ 3 ]. 4.3 Anthropometric Status and the Double Burden of Malnutrition Anthropometric assessment indicated a low prevalence of stunting, suggesting limited chronic undernutrition in this predominantly urban sample. However, the persistence of moderate and severe thinness among approximately 16% of participants points to ongoing vulnerability to undernutrition. At the same time, the presence of overweight and obesity particularly among private school students signals the emergence of overnutrition, reflecting the double burden of malnutrition increasingly observed among adolescents in sub-Saharan Africa [ 19 ]. The higher prevalence of overweight and obesity among private school students is consistent with global patterns in which excess adiposity initially emerges among more socioeconomically advantaged groups in LMICs, driven by greater exposure to energy-dense foods and more sedentary lifestyles [ 16 ]. Together, these findings underscore the need for integrated, dual-purpose strategies that address both undernutrition and diet-related NCD risk within adolescent populations. 4.4 Implications for Policy and Practice The findings highlight the critical role of schools as platforms for improving adolescent diet quality. School-based interventions that promote increased consumption of fruits, vegetables, legumes, and nuts, while simultaneously limiting access to ultra-processed foods and sugar-sweetened beverages, may offer substantial benefits. Integrating nutrition education with supportive food environment policies such as regulating food vendors within and around school premises has been shown to improve dietary behaviors among adolescents and warrants prioritization in urban Nigerian contexts [ 24 ]. 4.5 Strengths and Limitations Key strengths of this study include its relatively large sample size and the comparison of dietary and anthropometric indicators across school settings, which provides insight into socioeconomic influences on adolescent nutrition. However, the cross-sectional design limits causal inference, and reliance on self-reported dietary data may introduce recall bias. Future research employing longitudinal designs and objective measures of dietary intake and physical activity would strengthen understanding of diet–health relationships during adolescence. 5 Conclusion In this sample of female adolescents in southwest Nigeria, diet quality reflected moderate diversity while dietary pattern shows inadequate intake of nutrient-dense foods and high consumption of energy-dense, ultra-processed items. Although most participants were within normal growth ranges, the coexistence of thinness and emerging overweight underscores a double burden of malnutrition. Differences by school setting suggest socioeconomic influences on diet quality, while the lack of association between diet quality scores and BMI-for-age indicates that anthropometric measures alone may not capture diet-related health risks in this population. These findings highlight the need for school-based interventions and food environment policies that promote nutrient-rich diets and reduce exposure to unhealthy foods among adolescents in rapidly urbanizing LMIC settings. Declarations Acknowledgements None Author contributions declaration Dare D. Adémiluyi, Akinade E. Ogunniyi, Uthman-Akinhanmi Y.O, Ojo-Adalumo A. Rhoda, Esther D.Olubiyi. All authors contributed equally to the manuscript Funding None Data availability Statement All data produced are available within the manuscript. Clinical Trial Number: Not applicable Ethics approval and consent to participate Ethical approval for the study was obtained from the Health Research Ethics Committee of the Federal Medical Centre, Abeokuta, Nigeria (Approval No: FMCA/470/HREC/01/2023/56). All procedures involving human participants were conducted in accordance with the ethical standards of the institutional research committee and in compliance with the principles of the Declaration of Helsinki. Because the participants were adolescents under 18 years of age, written informed consent was obtained from parents or legal guardians prior to enrolment. In addition, written informed assent was obtained from all participating adolescents. Participation was entirely voluntary, and participants were informed of their right to withdraw at any time without consequence. Confidentiality was ensured through anonymized coding of data and restricted access to study records. Consent to publish Written informed consent for publication of anonymized data was obtained from parents or legal guardians of all participating adolescents. All authors reviewed and approved the final version of the manuscript and consented to its submission for publication. Competing interests The authors declare no competing interests References Adeomi AA, Fatusi A, Klipstein-Grobusch K. Food Security, Dietary Diversity, Dietary Patterns and the Double Burden of Malnutrition among School-Aged Children and Adolescents in Two Nigerian States. Nutrients. 2022;14(4):789. https://doi.org/10.3390/nu14040789 . Arimond M, Wiesmann D, Becquey E, Carriquiry A, Daniels MC, Deitchler M, Fanou-Fogny N, Joseph ML, Kennedy G, Martin-Prevel Y, Torheim LE. Simple Food Group Diversity Indicators Predict Micronutrient Adequacy of Women’s Diets in 5 Diverse, Resource-Poor Settings. J Nutr. 2010;140(11). https://doi.org/10.3945/jn.110.123414 . 2059S2069S. Christian P, Lee SE, Donahue Angel M, Adair LS, Arifeen SE, Ashorn P, Barros FC, Fall CH, Fawzi WW, Hao W, Hu G, Humphrey JH, Huybregts L, Joglekar CV, Kariuki SK, Kolsteren P, Krishnaveni GV, Liu E, Martorell R, Osrin D. Risk of childhood undernutrition related to small-for-gestational age and preterm birth in low- and middle-income countries. Int J Epidemiol. 2013;42(5):1340–55. https://doi.org/10.1093/ije/dyt109 . Ademiluyi DD, Uthman-Akinhanmi YO, Animasahun MO, Oyewunmi BT, Salaudeen OA, Olubiyi ED, Oluwaronke Mabayomije Sodiya. Anthropometric indices, nutrition knowledge and perceived dietary behaviours of adolescents attending private and public secondary schools in Odeda, Ogun State, Nigeria, based on the Health Belief Model. World Nutr. 2025;16(4):7–18. https://doi.org/10.26596/wn.20251647-18 . Darmon N, Drewnowski A. Does social class predict diet quality? Am J Clin Nutr. 2008;87(5):1107–17. https://doi.org/10.1093/ajcn/87.5.1107 . Downs SM, Ahmed S, Fanzo J, Herforth A. Food Environment Typology: Advancing an Expanded Definition, Framework, and Methodological Approach for Improved Characterization of Wild, Cultivated, and Built Food Environments toward Sustainable Diets. Foods. 2020;9(4):532. https://doi.org/10.3390/foods9040532 . Fall CH, Abera M, Chopra H, Hardy-Johnson P, Janha RE, Jesson J, Joglekar C, Joseph S, Kehoe SH, Mukoma G, Joshi-Reddy K, Kumaran K, Barker ME. Anthropometric nutritional status, and social and dietary characteristics of African and Indian adolescents taking part in the TALENT (Transforming Adolescent Lives through Nutrition) qualitative study. Public Health Nutr. 2020;24(16):5249–60. https://doi.org/10.1017/s1368980020001901 . Fongar A, Gödecke T, Qaim M. Various forms of double burden of malnutrition problems exist in rural Kenya. BMC Public Health. 2019;19(1). https://doi.org/10.1186/s12889-019-7882-y . Gbadebo AA, Sholeye OO, Gbadebo FA, Oladokun HA. (2024). ORIGINAL: Prevalence, Pattern and Factors Associated with Consumption of Sweetened Beverages Among Adolescents in Ogun State, Nigeria: West Afr J Med. 2024 August; 41(8): 894–903 PMID: 39737490. West Africa Journal of Medicine , 41 (8), 894–903. https://wajmed.com/index.php/wajmed/article/view/999 Global Diet Quality Project. DQQ results dataset 2021–2024 [dataset]. Harvard Dataverse. 2024. 10.7910/DVN/KY3W8A Herforth AW, Wiesmann D, Martínez-Steele E, Andrade G, Monteiro CA. Introducing a Suite of Low-Burden Diet Quality Indicators that Reflect Healthy Diet Patterns at Population Level. Curr Developments Nutr. 2020;4(12). https://doi.org/10.1093/cdn/nzaa168 . Madzorera I, Bromage S, Mwanyika-Sando M, Vandormael A, Sherfi H, Worku A, Shinde S, Noor RA, Bärnighausen T, Sharma D, Fawzi WW. Dietary intake and quality for young adolescents in sub‐Saharan Africa: Status and influencing factors. Maternal Child Nutr. 2023. https://doi.org/10.1111/mcn.13463 . Micha R, Michas G, Mozaffarian D. Unprocessed Red and Processed Meats and Risk of Coronary Artery Disease and Type 2 Diabetes – An Updated Review of the Evidence. Curr Atheroscler Rep. 2012;14(6):515–24. https://doi.org/10.1007/s11883-012-0282-8 . Moore L, Singer M, Qureshi M, Bradlee M, Daniels S. Food Group Intake and Micronutrient Adequacy in Adolescent Girls. Nutrients. 2012;4(11):1692–708. https://doi.org/10.3390/nu4111692 . Murray CJL, Aravkin AY, Zheng P, Abbafati C, Abbas KM, Abbasi-Kangevari M, Abd-Allah F, Abdelalim A, Abdollahi M, Abdollahpour I, Abegaz KH, Abolhassani H, Aboyans V, Abreu LG, Abrigo MRM, Abualhasan A, Abu-Raddad LJ, Abushouk AI, Adabi M, Adekanmbi V. Global Burden of 87 Risk Factors in 204 Countries and territories, 1990–2019: a Systematic Analysis for the Global Burden of Disease Study 2019. Lancet. 2020;396(10258):1223–49. https://doi.org/10.1016/s0140-6736(20)30752-2 . Onyango AW, Jean-Baptiste J, Samburu B, Mahlangu TLM. Regional Overview on the Double Burden of Malnutrition and Examples of Program and Policy Responses: African Region. Ann Nutr Metab. 2019;75(2):127–30. https://doi.org/10.1159/000503671 . Popkin BM. Relationship between shifts in food system dynamics and acceleration of the global nutrition transition. Nutr Rev. 2017;75(2):73–82. https://doi.org/10.1093/nutrit/nuw064 . Popkin BM, Ng SW. The nutrition transition to a stage of high obesity and noncommunicable disease prevalence dominated by ultra-processed foods is not inevitable. Obes Rev. 2021;23(1). https://doi.org/10.1111/obr.13366 . Prentice AM. The Double Burden of Malnutrition in Countries Passing through the Economic Transition. Annals Nutr Metabolism. 2018;72(3):47–54. https://doi.org/10.1159/000487383 . Ruel MT, Quisumbing AR, Balagamwala M. Nutrition-sensitive agriculture: What have we learned so far? Global Food Secur. 2018;17:128–53. https://doi.org/10.1016/j.gfs.2018.01.002 . Steyn NP, Mchiza ZJ. Obesity and the nutrition transition in Sub-Saharan Africa. Ann N Y Acad Sci. 2014;1311(1):88–101. https://doi.org/10.1111/nyas.12433 . Gabriel TS, Kasim M, Oluma FA, Muka T, Erand Llanaj. &. (2024). Adolescent nutrition in Nigeria: a systematic review. Journal of Nutritional Science , 13 . https://doi.org/10.1017/jns.2024.34 Uyar BTM, Talsma EF, Herforth AW, Trijsburg LE, Vogliano C, Pastori G, Bekele TH, Huong LT, Brouwer ID. The DQQ is a Valid Tool to Collect Population-Level Food Group Consumption Data: A Study Among Women in Ethiopia, Vietnam, and Solomon Islands. J Nutr. 2023;153(1):340–51. https://doi.org/10.1016/j.tjnut.2022.12.014 . Wang D, Fawzi WW. Impacts of school feeding on educational and health outcomes of school-age children and adolescents in low- and middle-income countries: protocol for a systematic review and meta-analysis. Syst Reviews. 2020;9(1). https://doi.org/10.1186/s13643-020-01317-6 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 14 Mar, 2026 Reviews received at journal 04 Mar, 2026 Reviews received at journal 03 Mar, 2026 Reviews received at journal 26 Feb, 2026 Reviewers agreed at journal 25 Feb, 2026 Reviewers agreed at journal 25 Feb, 2026 Reviews received at journal 21 Feb, 2026 Reviewers agreed at journal 20 Feb, 2026 Reviewers agreed at journal 19 Feb, 2026 Reviewers invited by journal 17 Feb, 2026 Editor assigned by journal 17 Feb, 2026 Editor invited by journal 29 Jan, 2026 Submission checks completed at journal 29 Jan, 2026 First submitted to journal 29 Jan, 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-8689293","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":594060996,"identity":"90abb382-19d2-4a8f-9a40-efdbbb1ae218","order_by":0,"name":"Dare D. Adémiluyi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9ElEQVRIiWNgGAWjYFCCBCAqYGBgAzEYKoCYmbmBCC0GMC1nQFoYidDCYABlMLaBGAS08LcnH93wwOBwYh978rMPH+fVRvO3A7X8qNiGU4vEmWdpNxKAWtp4nhnPnLnteO6Mw4wNjD1nbuO25kaOGUSLRIIxM++2Y7kNQC3MjG24tcjfyP8G1ZL+mfnvnGO58wlpMbiRwwbVkmMMDKua3A2EtBieeQZyWLpxG8+bYsaeYwdyNwK1HMTnF7njyc9u/qiwlp3fnr6Z4UdNXe6884cPPvhRgcf7UODYAKEPg8kDBNUDgT2UriNG8SgYBaNgFIwwAAB8qGMzlQAN4wAAAABJRU5ErkJggg==","orcid":"","institution":"Department of Human Nutrition and Dietetics, College of Medicine, University of Ibadan","correspondingAuthor":true,"prefix":"","firstName":"Dare","middleName":"D.","lastName":"Adémiluyi","suffix":""},{"id":594060997,"identity":"42c7d065-72db-4ca4-b8ab-25b9f3c35886","order_by":1,"name":"Akinade E. Ogunniyi","email":"","orcid":"","institution":"Department of Human Nutrition and Dietetics, College of Medicine, University of Ibadan","correspondingAuthor":false,"prefix":"","firstName":"Akinade","middleName":"E.","lastName":"Ogunniyi","suffix":""},{"id":594060998,"identity":"28507815-67c5-4f3e-84a4-fc5fcd22d845","order_by":2,"name":"Yewande O. Uthman-Akinhanmi","email":"","orcid":"","institution":"Nutrition and Dietetics/department of Home Science and Hospitality Management. Olabisi Onabanjo University","correspondingAuthor":false,"prefix":"","firstName":"Yewande","middleName":"O.","lastName":"Uthman-Akinhanmi","suffix":""},{"id":594060999,"identity":"e22f958f-e070-4a2c-876a-943f2a1e7c59","order_by":3,"name":"Ayobami Rhoda Ojo-Adalumo","email":"","orcid":"","institution":"College of Nursing Science, Idi Aba","correspondingAuthor":false,"prefix":"","firstName":"Ayobami","middleName":"Rhoda","lastName":"Ojo-Adalumo","suffix":""},{"id":594061000,"identity":"14033b48-5f9f-4045-b2c9-0e04e87efc31","order_by":4,"name":"Esther D. Olubiyi","email":"","orcid":"","institution":"Department of Nutrition and Dietetics, Federal Medical Centre, Abeokuta","correspondingAuthor":false,"prefix":"","firstName":"Esther","middleName":"D.","lastName":"Olubiyi","suffix":""}],"badges":[],"createdAt":"2026-01-24 22:38:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8689293/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8689293/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103176047,"identity":"ce6b7fdd-9125-4d83-bd31-52b722c9483d","added_by":"auto","created_at":"2026-02-22 16:29:46","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":81059,"visible":true,"origin":"","legend":"\u003cp\u003ePercentage of Food Group Consumption\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8689293/v1/203e67654996852b031f2403.jpg"},{"id":103176048,"identity":"955dbb66-3527-4574-836e-a0ca3340e704","added_by":"auto","created_at":"2026-02-22 16:29:46","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":78179,"visible":true,"origin":"","legend":"\u003cp\u003eNCD-Risk\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8689293/v1/c4b0e8620920c31b74b322c2.jpg"},{"id":103504882,"identity":"38e4b5d8-db0f-40bf-8735-ead202995dfb","added_by":"auto","created_at":"2026-02-26 13:21:55","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":75748,"visible":true,"origin":"","legend":"\u003cp\u003eNCD-Protect\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8689293/v1/168b9c561769fae1dcfd0c45.jpg"},{"id":103504473,"identity":"1c525c69-2f46-40d1-b37d-79434848771c","added_by":"auto","created_at":"2026-02-26 13:20:09","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":49756,"visible":true,"origin":"","legend":"\u003cp\u003eMDDS-Women\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8689293/v1/8bd951bb99dbd820d95ad097.jpg"},{"id":103509204,"identity":"66a5cf55-43a3-4239-b8b5-c30a1ab43310","added_by":"auto","created_at":"2026-02-26 13:57:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1550144,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8689293/v1/447feb71-3ada-4b7e-b23c-b03aa1ea4ebb.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Diet quality and anthropometric parameter of in-school female adolescents in Ogun State, Nigeria: Descriptive Cross Sectional Study","fulltext":[{"header":"1 Introduction","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003e1.1 Study background\u003c/h2\u003e \u003cp\u003eAdolescence represents a critical transitional period characterized by rapid physical growth, hormonal changes, and cognitive development, during which nutritional requirements are substantially elevated. Dietary behaviors established during this stage exert long-lasting influences on body composition, metabolic health, and risk of non-communicable diseases (NCDs) in adulthood [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Globally, however, adolescent diets are increasingly characterized by low consumption of fruits, vegetables, whole grains, and legumes alongside rising intake of energy-dense, ultra-processed foods, reflecting the ongoing global nutrition transition [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Diet quality has emerged as a more comprehensive indicator of nutritional adequacy than single-nutrient approaches, as it captures overall dietary patterns and alignment with dietary guidelines. Poor diet quality during adolescence has been consistently associated with micronutrient deficiencies, impaired growth, excess adiposity, and early metabolic risk [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Consequently, population-level assessment of diet quality using validated dietary diversity and dietary recommendation indicators is increasingly prioritized in global nutrition surveillance.\u003c/p\u003e \u003cp\u003eParallel to dietary challenges, adolescents in low- and middle-income countries (LMICs) are experiencing a double burden of malnutrition, where undernutrition coexists with emerging overweight and obesity [19; 16, 22]. Sub-Saharan Africa, in particular, is witnessing rapid urbanization and shifts in food environments that promote consumption of refined grains, sugar-sweetened beverages, fried foods, and processed snacks, while access to nutrient-dense foods remains limited [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. These transitions place adolescents at heightened risk of both growth faltering and diet-related NCDs.\u003c/p\u003e \u003cp\u003eIn Nigeria, adolescents constitute nearly one-quarter of the population, yet remain under-represented in national nutrition surveillance. Existing evidence indicates widespread inadequacies in dietary diversity, high consumption of sugar-sweetened beverages, and rising prevalence of thinness and overweight among Nigerian adolescents [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Studies have further shown that school environment and socioeconomic context significantly shape adolescent dietary behaviors, with disparities observed between public and private school students [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOgun State, located in southwest Nigeria, is undergoing rapid peri-urban expansion with increasing exposure of adolescents to modern food environments. Despite this, empirical data on adolescent diet quality and its relationship with anthropometric parameters in this region remain limited. Previous studies in Ogun State have primarily focused on nutrition knowledge and isolated dietary behaviors, with limited integration of standardized global diet quality indicators and anthropometric outcomes [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Moreover, few studies have jointly examined food group consumption patterns, NCD-risk foods, NCD-protective foods, minimum dietary diversity, and anthropometric indices within the same adolescent population.\u003c/p\u003e \u003cp\u003eAnthropometric indicators such as height-for-age and BMI-for-age remain essential markers of nutritional status, reflecting both chronic and acute nutritional exposures. However, emerging evidence suggests that anthropometric measures alone may not fully capture diet-related health risks during adolescence, emphasizing the need to interpret them alongside comprehensive diet quality indicators [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Understanding how diet quality relates to anthropometric outcomes in adolescents is therefore crucial for informing targeted nutrition interventions and school-based policies.\u003c/p\u003e \u003cp\u003eRecent work by Ademiluyi and colleagues in Ogun State has highlighted suboptimal nutrition knowledge, moderate dietary diversity, and early signs of the double burden of malnutrition among in-school adolescents [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. However, there remains a critical gap in descriptive population-level evidence that integrates global diet quality indicators with anthropometric parameters among female adolescents, a group particularly vulnerable to micronutrient deficiencies and future maternal health risks. Addressing this gap is essential, as adolescent girls represent a pivotal population for intergenerational nutrition improvement. Poor diet quality and suboptimal growth during adolescence not only compromise current health but also influence future pregnancy outcomes, offspring growth, and long-term NCD risk [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTherefore, this study aimed to describe diet quality and anthropometric parameters among in-school female adolescents in Ogun State, Nigeria, using standardized global dietary quality indicators and WHO anthropometric references. By providing population-level evidence on food group consumption patterns, NCD-risk and NCD-protective foods, dietary diversity, and nutritional status, this study seeks to inform school-based nutrition policies and adolescent health interventions in southwest Nigeria and similar LMIC contexts.\u003c/p\u003e \u003c/div\u003e"},{"header":"2. Methodology","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study Design and Population\u003c/h2\u003e \u003cp\u003eA descriptive cross-sectional study design was employed to examine the associations between diet quality and anthropometric indices among in-school female adolescents in public and private secondary schools within Odeda LGA.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Study Area\u003c/h2\u003e \u003cp\u003eThis study was conducted in Odeda Local Government Area (LGA), Ogun State, Nigeria, which is located along the Abeokuta-Ibadan Road, approximately 10 kilometers from Abeokuta, the state capital. The region comprises several towns and villages with a strong agricultural presence and a growing commitment to health and education, making it a suitable location for research on adolescent nutrition. The Odeda LGA has both, public and private secondary schools providing a diverse educational setting for the study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Study Criteria\u003c/h2\u003e \u003cp\u003eThe study included adolescents aged 13 to 17 years enrolled in public or private secondary schools in Odeda LGA. Participants had to be available during the study period, willing to provide informed assent, and have parental or guardian consent. Female adolescents with chronic illnesses affecting anthropometric measurements were excluded. Additional exclusion criteria included those with diagnosed eating disorders, diabetes, or other chronic conditions, unwillingness to participate, and lack of parental consent.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Sampling Techniques\u003c/h2\u003e \u003cp\u003eOdeda Local Government Area (LGA) comprises three administrative zones: Odeda, Ilugun, and Opeji. The Opeji Zone was randomly selected using a simple random sampling technique. Within this zone, five communities exist: Obantoko, Adao, Alabata, Opeji, and Obete. Obantoko was purposively chosen because it has the largest population and a diverse socio-demographic profile, encompassing both urban and peri-urban settings with a mix of public and private secondary schools representing varied socio-economic backgrounds. From the list of registered secondary schools in Obantoko, four (two public and two private) were purposively selected to capture this diversity. Within each school, students were stratified by class level (Senior Secondary 1\u0026ndash;3), and proportional allocation determined the number of participants per class. Finally, students were selected through simple random sampling, yielding a total of 290 female adolescents aged 13\u0026ndash;17 years.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Sample Size Determination\u003c/h2\u003e \u003cp\u003eThe minimum sample size for this cross-sectional study was estimated using Cochran\u0026rsquo;s formula. The prevalence of adequate dietary diversity (56.5%) reported by [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] was used as the expected proportion. A 95% confidence level (Z\u0026thinsp;=\u0026thinsp;1.96) and a margin of error of 5% (d\u0026thinsp;=\u0026thinsp;0.05) were applied, resulting in an initial sample size of 379 participants. Because the study population of female adolescents in the selected schools was finite, the sample size was adjusted using the finite population correction (FPC). Based on the total female adolescent population in the selected schools, the corrected sample size was approximately 264. To account for potential non-response, a 10% adjustment was applied, yielding a final minimum sample size of 290 adolescents.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Data collection and study procedures\u003c/h2\u003e \u003cp\u003eData were collected using a pretested, interviewer-administered semi-structured questionnaire designed to obtain information on sociodemographic characteristics, diet quality, and anthropometric indices. Trained research assistant adminstered questionaire within the school environment between August and November 2024, during regular academic sessions and outside examination periods to minimize disruption and participant fatigue. Class teachers assisted only with logistical coordination and were not present during data collection to reduce response bias.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Diet quality assessment\u003c/h2\u003e \u003cp\u003eDietary intake was assessed using the Dietary Quality Questionnaire for Nigeria developed by the Global Diet Quality Project [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Dietary Diversity Score was derived from consumption of ten food groups, including grains, white roots and tubers, and plantains; pulses; nuts and seeds; dairy; meat, poultry, and fish; eggs; dark green leafy vegetables; vitamin A\u0026ndash;rich fruits and vegetables; other vegetables; and other fruits. The NCD-Protect score reflected adherence to dietary recommendations on foods encouraged for consumption and was based on intake of whole grains, pulses, nuts and seeds, vitamin A\u0026ndash;rich orange vegetables, dark green leafy vegetables, other vegetables, vitamin A\u0026ndash;rich fruits, citrus fruits, and other fruits. The NCD-Risk score captured intake of foods recommended for limitation, including soft drinks, baked or grain-based sweets, other sweets, processed meats, unprocessed red meat, deep-fried foods, fast foods and instant noodles, and packaged ultra-processed salty snacks. Overall adherence to global dietary recommendations was quantified using the Global Dietary Recommendation score, calculated as the difference between the NCD-Protect and NCD-Risk scores plus a constant of nine, yielding a possible range of 0 to 18, with higher scores indicating greater adherence to dietary patterns protective against non-communicable diseases [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Minimum Dietary Diversity for Women was defined as consumption of at least five of the ten standard food groups within the reference period, and participants were classified accordingly to indicate achievement or non-achievement of minimum dietary diversity [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDiet quality indicators, including food group consumption patterns (derived from the Dietary Diversity Score), NCD-risk foods, NCD-protective foods, and Minimum Dietary Diversity for Women (MDD-W), were classified using the standard Global Diet Quality Indicator Guide based on the proportion of food groups consumed by participants [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Food group consumption patterns were summarized as proportions and illustrated graphically using bar charts to describe adherence to each diet quality indicator among female adolescents. Each indicator was defined by specific food groups consistent with global dietary quality frameworks.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Anthropometric assessment\u003c/h2\u003e \u003cp\u003eHeight and weight were measured using standardized WHO procedures. Height-for-age and BMI-for-age z-scores were generated using WHO AnthroPlus software. Anthropometric indices (weight, height, BMI, and MUAC) were assessed using standardised procedures recommended by the CDC (2020). BMI-for-age Z-scores were computed using WHO cut-offs: severe thinness (\u0026thinsp;\u0026lt;\u0026thinsp;\u0026minus;\u0026thinsp;3SD), thinness (\u0026minus;\u0026thinsp;3SD to \u0026lt;\u0026thinsp;\u0026minus;\u0026thinsp;2SD), normal weight (\u0026minus;\u0026thinsp;2SD to +1SD), overweight (\u0026thinsp;\u0026gt;\u0026thinsp;+\u0026thinsp;1SD to +2SD), and obesity (\u0026thinsp;\u0026gt;\u0026thinsp;+\u0026thinsp;2SD) [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.9 Ethical considerations\u003c/h2\u003e \u003cp\u003e \u003cstrong\u003eEthical approval\u003c/strong\u003e \u003cp\u003e for the study was obtained from the Health Research Ethics Committee of the Federal Medical Centre, Abeokuta (FMCA/470/HREC/01/2023/56). Written informed consent was obtained from parents or guardians, and informed assent was obtained from all participating adolescents. Confidentiality was ensured through anonymized coding and restricted access to study records. Participation was voluntary, and the study adhered to internationally accepted ethical standards for research involving human participants.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.9 Statistical analysis\u003c/h2\u003e \u003cp\u003eData were analyzed using IBM SPSS version 27 and Microsoft Excel 2016. Continuous variables, including dietary diversity, Global Dietary Recommendation scores, nutrition knowledge scores, and anthropometric z-scores, were summarized using means and standard deviations, while categorical variables were summarized using frequencies and percentages. Independent-sample t tests were used to compare mean diet quality indicators between adolescents attending public and private schools. Chi-square tests were applied to examine differences in categorical anthropometric classifications by school setting. Pearson correlation analysis was conducted to assess relationships among dietary diversity, Global Dietary Recommendation scores, and BMI-for-age z-scores. All statistical tests were two-tailed, and statistical significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Sociodemographic and economic characteristics of the female adolescents\u003c/h2\u003e \u003cp\u003eThe mean age of participants was 15.19\u0026thinsp;\u0026plusmn;\u0026thinsp;1.05 years, with most aged 15 years. Slightly more than half attended public schools (55.2%). Participants were predominantly Yoruba (96.6%). Nearly half were in SS1 (46.2%). Most parents had at least secondary education, with approximately 47% attaining tertiary education. Over half of households reported monthly incomes above ₦100,000. The mean household size was 5.38\u0026thinsp;\u0026plusmn;\u0026thinsp;1.84, and 69.7% lived with both parents.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSociodemographic and economic characteristics of the female adolescents\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFrequency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercent\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eMean\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;S.D: 15.19\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;1.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSchool Settings\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePublic School\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrivate School\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEthnic group\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYoruba\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e280\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e96.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIgbo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eClass\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSS2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSS3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFather highest level of education\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo formal education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTertiary education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMother highest level of education\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo formal education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTertiary education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHousehold's Monthly Income\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLess than 20,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20,000\u0026ndash;50,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50,001\u0026ndash;100,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMore than 100,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNumber of Household\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u0026ndash;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u0026ndash;8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u0026ndash;12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eMean\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;S.D: 5.38\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;1.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWho do you live with?\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBoth Parent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMother only\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFather only\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGuardian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther relatives\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Food Group Consumption, NCD-Risk, NCD-Protective, and Minimum Dietary Diversity for Women by School Setting\u003c/h2\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. presents the distribution of food group consumption among in-school adolescents by school setting. Consumption of grains, white roots and tubers, and plantains was nearly universal, reported by 97.2% of participants overall, with a higher prevalence among public school students (99%) compared with private school students (95%). Intake of pulses was reported by 33.4% of adolescents overall, with higher consumption among private school students (38%) than public school students (30%). Dark green leafy vegetables were consumed by 58.3% of participants, with similar patterns between private (62%) and public (56%) schools. Other vegetables were consumed by 56.2% overall, with a higher proportion among private school students (65%) compared with public school students (49%). Consumption of other vitamin A\u0026ndash;rich fruits and vegetables was low across both school settings (32.4% overall). Other fruits were consumed by 35.9% of participants, with markedly higher intake among public school students (50%) compared with private school students (18%). Egg consumption was reported by 41.7% of adolescents, with comparable proportions between school types. Nearly half of the adolescents (47.9%) consumed dairy products, with similar distribution across school settings. Meat, poultry, and fish were widely consumed (87.9% overall), with higher prevalence among private school students (92%) than public school students (85%). Nuts and seeds had the lowest consumption frequency, reported by 29.0% of participants overall.\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. illustrates the consumption patterns of foods associated with increased non-communicable disease (NCD) risk. Baked or grain-based sweets were consumed by 79.7% of adolescents overall, with higher consumption among public school students (82.5%) compared with private school students (76.2%). Consumption of other sweets was equally distributed overall (50.0%), with higher prevalence among private school students (56.2%) than public school students (45.0%). Processed meats were consumed by 23.8% of participants, while unprocessed red meat consumption was reported by 44.8%, with slightly higher intake among private school students. Packaged ultra-processed salty snacks were consumed by 16.2% of adolescents, with minimal variation between school settings. More than half of the participants (54.8%) reported consuming deep-fried foods, with higher prevalence among public school students (58.1%) than private school students (50.8%). Consumption of soft drinks was reported by 44.8% of adolescents, with substantially higher intake among private school students (56.2%) compared with public school students (35.6%). Fast food consumption was reported by 27.9% overall, with a higher proportion among public school students (31.2%) than private school students (23.8%).\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. shows the consumption of food groups considered protective against NCDs. Whole grains were consumed by 23.8% of adolescents overall, with higher intake among public school students (27.5%) compared with private school students (19.2%). Pulse consumption was reported by 33.4% of participants, with higher prevalence among private school students (37.7%) than public school students (30.0%). Intake of vitamin A\u0026ndash;rich orange vegetables was low across both groups (12.1% overall). Dark green leafy vegetables were consumed by 58.3%, with similar distribution across school settings. Other vegetables were consumed by 56.2% of adolescents, with higher intake among private school students (65.4%) compared with public school students (48.8%). Vitamin A\u0026ndash;rich fruits were consumed by 21.4%, with slightly higher consumption among public school students. Consumption of citrus fruits was reported by 15.2% overall, with higher prevalence among public school students (22.5%) compared with private school students (6.2%). Other fruits were consumed by 27.6% of participants, with higher intake among public school students (36.3%) than private school students (16.9%). Nuts and seeds were consumed by 29.0% of adolescents, with similar proportions across school settings.\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. presents the distribution of adolescents according to Minimum Dietary Diversity for Women (MDD-W) classification. Overall, 60.7% of participants met the MDD-W criterion, while 39.3% fell below the minimum threshold. Among public school students, 74.0% met the MDD-W, compared with 62.0% of private school students. Conversely, 49.0% of public school students and 38.0% of private school students were classified as having dietary diversity below the MDD-W threshold.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Pearson Correlation among DDS, GDR, and BMI-for-Age Z-Score\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the Pearson correlation matrix examining relationships among dietary diversity score (DDS), global dietary recommendation score (GDR), and BMI-for-age z-score (BAZ). A positive and statistically significant correlation was observed between DDS and GDR (r\u0026thinsp;=\u0026thinsp;0.279, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), indicating that higher dietary diversity was associated with higher adherence to global dietary recommendations.\u003c/p\u003e \u003cp\u003eNo significant correlations were observed between DDS and BAZ (r\u0026thinsp;=\u0026thinsp;0.004) or between GDR and BAZ (r\u0026thinsp;=\u0026thinsp;0.009), suggesting that dietary quality indicators were not linearly associated with BMI-for-age z-scores in this population.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePearson Correlation among DDS, GDR, and BMI-for-Age Z-Score\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDDS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGDR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBAZ\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGDR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.279*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI for age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e*\u003c/b\u003e Correlation is significant at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003cp\u003eGDR, global dietary recommendations; DDS, Dietary Diversity Score\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Diet Quality Indicators by School Setting\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. compares diet quality indicators between adolescents attending public and private schools. The mean Dietary Diversity Score (DDS) was slightly higher among public school students (5.18\u0026thinsp;\u0026plusmn;\u0026thinsp;2.08) compared with private school students (4.94\u0026thinsp;\u0026plusmn;\u0026thinsp;1.90); however, the mean difference was not statistically significant (MD\u0026thinsp;=\u0026thinsp;0.243; p\u0026thinsp;=\u0026thinsp;0.304).\u003c/p\u003e \u003cp\u003eSimilarly, the NCD-Protect score was marginally higher among public school adolescents (2.83\u0026thinsp;\u0026plusmn;\u0026thinsp;1.76) than private school adolescents (2.51\u0026thinsp;\u0026plusmn;\u0026thinsp;1.41) were, though this difference did not reach statistical significance (MD\u0026thinsp;=\u0026thinsp;0.324; p\u0026thinsp;=\u0026thinsp;0.090). The NCD-Risk score was slightly higher among private school students (3.91\u0026thinsp;\u0026plusmn;\u0026thinsp;2.22) compared with those in public schools (3.57\u0026thinsp;\u0026plusmn;\u0026thinsp;2.06), with no statistically significant difference observed (MD\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.339; p\u0026thinsp;=\u0026thinsp;0.179).\u003c/p\u003e \u003cp\u003eIn contrast, the Global Dietary Recommendation (GDR) score was significantly higher among public school adolescents (8.26\u0026thinsp;\u0026plusmn;\u0026thinsp;2.17) than private school adolescents (7.60\u0026thinsp;\u0026plusmn;\u0026thinsp;2.23), with a mean difference of 0.662 and a 95% confidence interval of 0.153 to 1.172 (p\u0026thinsp;=\u0026thinsp;0.011).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDiet Quality Indicators\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e95% C.I of the Difference\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndicator\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSchool Settings\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eM\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;S.D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLower\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUpper\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eP-Value\u003csup\u003e↕\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDDS \u003csup\u003e(0\u0026ndash;10)a\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePublic School\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.18\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;2.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.243\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.707\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.304\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrivate School\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.94\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;1.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNCD-Protect Score \u003csup\u003e(0\u0026ndash;9)b\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePublic School\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.83\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;1.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.698\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.090\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrivate School\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.51\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;1.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNCD-Risk Score \u003csup\u003e(0\u0026ndash;9)c\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePublic School\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.57\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;2.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.339\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.834\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.179\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrivate School\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.91\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;2.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGDR Score \u003csup\u003e(0\u0026ndash;18)d\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePublic School\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.26\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;2.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.662\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.011*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrivate School\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.6\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;2.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003csup\u003e↕\u003c/sup\u003eStatistical analysis: Independent T test\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eM, Mean; S.D, Standard Deviation; MD, Mean Difference; N, Frequency; GDR, global dietary recommendations; NCD, non-communicable disease.\u003c/p\u003e \u003cp\u003e \u003csup\u003ea\u003c/sup\u003e Dietary diversity Score (DDS) includes ten food groups: (1) grains, white roots and tuber, and plantains; (2) pulses (beans, peas and lentils); (3) nuts and seeds; (4) dairy; (5) meat, poultry and fish; (6) eggs; (7) dark green leafy vegetables; (8) other vitamin A-rich fruits and vegetables; (9) other vegetables; (10) other fruits.\u003c/p\u003e \u003cp\u003e \u003csup\u003eb\u003c/sup\u003e NCD \u0026ndash; protect score measures adherence to global dietary recommendations on foods to consume: (1) whole grains; (2) pulses; (3) nuts and seeds; (4) vitamin A-rich orange vegetables; (5) dark green leafy vegetables; (6) other vegetables; (7) vitamin A-rich fruits; (8) citrus; (9) other fruits.\u003c/p\u003e \u003cp\u003e \u003csup\u003ec\u003c/sup\u003e NCD \u0026ndash; risk score measures adherence to global dietary recommendations on foods to limit including: (1) soft drinks; (2) baked/grain-based sweets; (3) other sweets; (4) processed meats; (5) unprocessed meat; (6) deep fried food; (7) fast food and instant noodles; (8) packaged ultra-processed salty snacks\u003c/p\u003e \u003cp\u003e \u003csup\u003ed\u003c/sup\u003e GDR score = (NCD \u0026ndash; Protect \u0026ndash; NCD \u0026ndash; Risk)\u0026thinsp;+\u0026thinsp;9; measures adherence to global dietary recommendations protective against non-communicable diseases.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Anthropometric Indices of Female Adolescents by School Setting\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e describes the anthropometric status of the adolescents by school setting. Based on height-for-age classification, the majority of participants were normal (95.9%), with 4.1% classified as moderately stunted. The prevalence of normal height-for-age was similar between public (95.0%) and private (97.0%) school students, with no statistically significant difference (p\u0026thinsp;=\u0026thinsp;0.891). The mean height-for-age z-score for the overall sample was \u0026minus;\u0026thinsp;0.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.97.\u003c/p\u003e \u003cp\u003eRegarding BMI-for-age classification, 80.0% of adolescents had a normal BMI-for-age, while 13.1% were moderately thin and 2.8% were severely thin. Overweight and obesity were observed in 3.4% and 0.7% of participants, respectively. Severe thinness was reported only among public school students (5.0%), whereas overweight and obesity were more prevalent among private school students. However, differences in BMI-for-age categories between school settings were not statistically significant (p\u0026thinsp;=\u0026thinsp;0.162). The overall mean BMI-for-age z-score was \u0026minus;\u0026thinsp;0.71\u0026thinsp;\u0026plusmn;\u0026thinsp;1.13.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAnthropometric Indices of the female adolescent\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eSchool Setting\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClassification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePublic School\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePrivate School\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-Value\u003csup\u003e↕\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHeight for age\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e278 (95.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e152 (95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e126 (97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.891\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate Stunting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (4.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e290 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e160 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e130 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMean\u0026thinsp;+\u0026thinsp;S.D= -0.25\u0026thinsp;+\u0026thinsp;0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI for age\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSevere thinness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (2.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.162\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate thinness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38 (13.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14 (11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e232 (80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e126 (79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e106 (82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (3.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObesity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e290 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e160 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e130 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMean\u0026thinsp;+\u0026thinsp;S.D = -0.71\u0026thinsp;+\u0026thinsp;1.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003csup\u003e↕\u003c/sup\u003eStatistical analysis: Independent T test\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eS.D, Standard Deviation; N, Frequency\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eThis study provides a comprehensive evaluation of diet quality, dietary patterns, and anthropometric status among female in-school adolescents in southwest Nigeria, with explicit consideration of differences by school setting. By jointly examining food group consumption, exposure to dietary factors associated with non-communicable disease (NCD) risk, minimum dietary diversity, and anthropometric indicators, the findings contribute to the growing body of evidence on adolescent nutrition in low- and middle-income countries (LMICs) undergoing rapid nutrition transition. Overall, the results reveal persistent inadequacies in the consumption of nutrient-dense foods alongside substantial exposure to energy-dense, ultra-processed foods, highlighting the coexistence of undernutrition and emerging overnutrition during a critical developmental period. The sociodemographic characteristics of the study population particularly the mean age of 15.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1 years and the predominance of public school attendance are consistent with previous reports describing adolescent school enrollment patterns in urban Nigeria. The overwhelming representation of Yoruba ethnicity reflects the regional context of southwest Nigeria and, while limiting ethnic heterogeneity, provides focused insight into dietary and nutritional patterns within this population group [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Food Group Consumption Pattern\u003c/h2\u003e \u003cp\u003eFood group consumption patterns demonstrated near-universal consumption of staple carbohydrate-rich foods, including grains, roots, tubers, and plantains, reflecting traditional dietary practices widely reported across sub-Saharan Africa [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. In contrast, consumption of several nutrient-dense protective food groups particularly pulses, nuts and seeds, and vitamin A\u0026ndash;rich fruits and vegetables was consistently low. Similar gaps in adolescent consumption of micronutrient-rich foods have been documented across LMIC settings and are indicative of limited diet quality despite apparent caloric adequacy [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The low intake of citrus fruits and vitamin A\u0026ndash;rich orange vegetables is of particular concern given their established role in supporting micronutrient adequacy, immune function, and long-term health [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSchool setting emerged as an important contextual factor influencing specific dietary behaviors. Adolescents attending private schools reported higher consumption of pulses and other vegetables, whereas public school students had higher intake of grains and other fruits. These differences likely reflect underlying socioeconomic gradients in food access, affordability, and food environments, as well as differences in household food provisioning practices [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Such findings reinforce the notion that adolescent dietary behaviors are shaped by intersecting influences operating at household, school, and community levels. Of particular concern is the high prevalence of foods associated with increased NCD risk. Nearly four in five adolescents reported consumption of baked or grain-based sweets, and more than half consumed deep-fried foods, reflecting dietary shifts characteristic of the nutrition transition toward energy-dense, nutrient-poor diets [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The higher consumption of sugar-sweetened beverages among private school students aligns with evidence from urban Nigerian and other LMIC settings indicating greater access to commercially processed beverages among adolescents from relatively higher socioeconomic backgrounds [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Although processed meat consumption was comparatively low, its presence remains noteworthy given consistent evidence linking even modest intake to adverse cardiometabolic outcomes later in life [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Dietary Diversity, Diet Quality, and Global Recommendations\u003c/h2\u003e \u003cp\u003eApproximately 61% of participants met the Minimum Dietary Diversity for Women (MDD-W) threshold, suggesting moderate dietary diversity at the population level. Nonetheless, nearly two-fifths of adolescents failed to achieve the minimum threshold, indicating substantial risk of inadequate micronutrient intake. Studies among adolescent girls show very low dietary diversity and substantial micronutrient inadequacy, contributing to deficiencies in iron, zinc, and calcium [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. While MDD-W was originally validated for women of reproductive age, accumulating evidence supports its use as a proxy indicator of diet quality among adolescents, particularly in resource-limited settings where more detailed dietary assessment may be infeasible [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The observed positive association between the Dietary Diversity Score (DDS) and the Global Dietary Recommendation (GDR) score supports the construct validity of dietary diversity as an important component of overall diet quality. This finding is consistent with prior research demonstrating that greater dietary diversity is associated with improved alignment with dietary guidelines and higher micronutrient adequacy among adolescents [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In contrast, neither DDS nor GDR was significantly associated with BMI-for-age z-scores. This lack of association aligns with growing recognition that BMI may not adequately capture the nutritional implications of diet quality during adolescence, a period characterized by rapid growth, pubertal development, and substantial interindividual variability in body composition [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Anthropometric Status and the Double Burden of Malnutrition\u003c/h2\u003e \u003cp\u003eAnthropometric assessment indicated a low prevalence of stunting, suggesting limited chronic undernutrition in this predominantly urban sample. However, the persistence of moderate and severe thinness among approximately 16% of participants points to ongoing vulnerability to undernutrition. At the same time, the presence of overweight and obesity particularly among private school students signals the emergence of overnutrition, reflecting the double burden of malnutrition increasingly observed among adolescents in sub-Saharan Africa [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe higher prevalence of overweight and obesity among private school students is consistent with global patterns in which excess adiposity initially emerges among more socioeconomically advantaged groups in LMICs, driven by greater exposure to energy-dense foods and more sedentary lifestyles [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Together, these findings underscore the need for integrated, dual-purpose strategies that address both undernutrition and diet-related NCD risk within adolescent populations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Implications for Policy and Practice\u003c/h2\u003e \u003cp\u003eThe findings highlight the critical role of schools as platforms for improving adolescent diet quality. School-based interventions that promote increased consumption of fruits, vegetables, legumes, and nuts, while simultaneously limiting access to ultra-processed foods and sugar-sweetened beverages, may offer substantial benefits. Integrating nutrition education with supportive food environment policies such as regulating food vendors within and around school premises has been shown to improve dietary behaviors among adolescents and warrants prioritization in urban Nigerian contexts [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e4.5 Strengths and Limitations\u003c/h2\u003e \u003cp\u003eKey strengths of this study include its relatively large sample size and the comparison of dietary and anthropometric indicators across school settings, which provides insight into socioeconomic influences on adolescent nutrition. However, the cross-sectional design limits causal inference, and reliance on self-reported dietary data may introduce recall bias. Future research employing longitudinal designs and objective measures of dietary intake and physical activity would strengthen understanding of diet\u0026ndash;health relationships during adolescence.\u003c/p\u003e \u003c/div\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eIn this sample of female adolescents in southwest Nigeria, diet quality reflected moderate diversity while dietary pattern shows inadequate intake of nutrient-dense foods and high consumption of energy-dense, ultra-processed items. Although most participants were within normal growth ranges, the coexistence of thinness and emerging overweight underscores a double burden of malnutrition. Differences by school setting suggest socioeconomic influences on diet quality, while the lack of association between diet quality scores and BMI-for-age indicates that anthropometric measures alone may not capture diet-related health risks in this population. These findings highlight the need for school-based interventions and food environment policies that promote nutrient-rich diets and reduce exposure to unhealthy foods among adolescents in rapidly urbanizing LMIC settings.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDare D. Ad\u0026eacute;miluyi, \u0026nbsp;Akinade E. Ogunniyi, Uthman-Akinhanmi Y.O, Ojo-Adalumo A. Rhoda, Esther D.Olubiyi.\u003c/p\u003e\n\u003cp\u003eAll authors contributed equally to the manuscript\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data produced are available within the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial Number:\u0026nbsp;\u003c/strong\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval for the study was obtained from the Health Research Ethics Committee of the Federal Medical Centre, Abeokuta, Nigeria (Approval No: FMCA/470/HREC/01/2023/56). All procedures involving human participants were conducted in accordance with the ethical standards of the institutional research committee and in compliance with the principles of the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003eBecause the participants were adolescents under 18 years of age, written informed consent was obtained from parents or legal guardians prior to enrolment. In addition, written informed assent was obtained from all participating adolescents. Participation was entirely voluntary, and participants were informed of their right to withdraw at any time without consequence. Confidentiality was ensured through anonymized coding of data and restricted access to study records.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWritten informed consent for publication of anonymized data was obtained from parents or legal guardians of all participating adolescents. All authors reviewed and approved the final version of the manuscript and consented to its submission for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAdeomi AA, Fatusi A, Klipstein-Grobusch K. Food Security, Dietary Diversity, Dietary Patterns and the Double Burden of Malnutrition among School-Aged Children and Adolescents in Two Nigerian States. Nutrients. 2022;14(4):789. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/nu14040789\u003c/span\u003e\u003cspan address=\"10.3390/nu14040789\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArimond M, Wiesmann D, Becquey E, Carriquiry A, Daniels MC, Deitchler M, Fanou-Fogny N, Joseph ML, Kennedy G, Martin-Prevel Y, Torheim LE. Simple Food Group Diversity Indicators Predict Micronutrient Adequacy of Women\u0026rsquo;s Diets in 5 Diverse, Resource-Poor Settings. J Nutr. 2010;140(11). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3945/jn.110.123414\u003c/span\u003e\u003cspan address=\"10.3945/jn.110.123414\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. 2059S2069S.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChristian P, Lee SE, Donahue Angel M, Adair LS, Arifeen SE, Ashorn P, Barros FC, Fall CH, Fawzi WW, Hao W, Hu G, Humphrey JH, Huybregts L, Joglekar CV, Kariuki SK, Kolsteren P, Krishnaveni GV, Liu E, Martorell R, Osrin D. Risk of childhood undernutrition related to small-for-gestational age and preterm birth in low- and middle-income countries. Int J Epidemiol. 2013;42(5):1340\u0026ndash;55. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/ije/dyt109\u003c/span\u003e\u003cspan address=\"10.1093/ije/dyt109\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAdemiluyi DD, Uthman-Akinhanmi YO, Animasahun MO, Oyewunmi BT, Salaudeen OA, Olubiyi ED, Oluwaronke Mabayomije Sodiya. Anthropometric indices, nutrition knowledge and perceived dietary behaviours of adolescents attending private and public secondary schools in Odeda, Ogun State, Nigeria, based on the Health Belief Model. World Nutr. 2025;16(4):7\u0026ndash;18. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.26596/wn.20251647-18\u003c/span\u003e\u003cspan address=\"10.26596/wn.20251647-18\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDarmon N, Drewnowski A. Does social class predict diet quality? Am J Clin Nutr. 2008;87(5):1107\u0026ndash;17. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/ajcn/87.5.1107\u003c/span\u003e\u003cspan address=\"10.1093/ajcn/87.5.1107\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDowns SM, Ahmed S, Fanzo J, Herforth A. Food Environment Typology: Advancing an Expanded Definition, Framework, and Methodological Approach for Improved Characterization of Wild, Cultivated, and Built Food Environments toward Sustainable Diets. Foods. 2020;9(4):532. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/foods9040532\u003c/span\u003e\u003cspan address=\"10.3390/foods9040532\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFall CH, Abera M, Chopra H, Hardy-Johnson P, Janha RE, Jesson J, Joglekar C, Joseph S, Kehoe SH, Mukoma G, Joshi-Reddy K, Kumaran K, Barker ME. Anthropometric nutritional status, and social and dietary characteristics of African and Indian adolescents taking part in the TALENT (Transforming Adolescent Lives through Nutrition) qualitative study. Public Health Nutr. 2020;24(16):5249\u0026ndash;60. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1017/s1368980020001901\u003c/span\u003e\u003cspan address=\"10.1017/s1368980020001901\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFongar A, G\u0026ouml;decke T, Qaim M. Various forms of double burden of malnutrition problems exist in rural Kenya. BMC Public Health. 2019;19(1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12889-019-7882-y\u003c/span\u003e\u003cspan address=\"10.1186/s12889-019-7882-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGbadebo AA, Sholeye OO, Gbadebo FA, Oladokun HA. (2024). ORIGINAL: Prevalence, Pattern and Factors Associated with Consumption of Sweetened Beverages Among Adolescents in Ogun State, Nigeria: West Afr J Med. 2024 August; 41(8): 894\u0026ndash;903 PMID: 39737490. \u003cem\u003eWest Africa Journal of Medicine\u003c/em\u003e, \u003cem\u003e41\u003c/em\u003e(8), 894\u0026ndash;903. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://wajmed.com/index.php/wajmed/article/view/999\u003c/span\u003e\u003cspan address=\"https://wajmed.com/index.php/wajmed/article/view/999\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGlobal Diet Quality Project. DQQ results dataset 2021\u0026ndash;2024 [dataset]. Harvard Dataverse. 2024. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.7910/DVN/KY3W8A\u003c/span\u003e\u003cspan address=\"10.7910/DVN/KY3W8A\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHerforth AW, Wiesmann D, Mart\u0026iacute;nez-Steele E, Andrade G, Monteiro CA. Introducing a Suite of Low-Burden Diet Quality Indicators that Reflect Healthy Diet Patterns at Population Level. Curr Developments Nutr. 2020;4(12). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/cdn/nzaa168\u003c/span\u003e\u003cspan address=\"10.1093/cdn/nzaa168\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMadzorera I, Bromage S, Mwanyika-Sando M, Vandormael A, Sherfi H, Worku A, Shinde S, Noor RA, B\u0026auml;rnighausen T, Sharma D, Fawzi WW. Dietary intake and quality for young adolescents in sub‐Saharan Africa: Status and influencing factors. Maternal Child Nutr. 2023. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/mcn.13463\u003c/span\u003e\u003cspan address=\"10.1111/mcn.13463\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMicha R, Michas G, Mozaffarian D. Unprocessed Red and Processed Meats and Risk of Coronary Artery Disease and Type 2 Diabetes \u0026ndash; An Updated Review of the Evidence. Curr Atheroscler Rep. 2012;14(6):515\u0026ndash;24. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11883-012-0282-8\u003c/span\u003e\u003cspan address=\"10.1007/s11883-012-0282-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoore L, Singer M, Qureshi M, Bradlee M, Daniels S. Food Group Intake and Micronutrient Adequacy in Adolescent Girls. Nutrients. 2012;4(11):1692\u0026ndash;708. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/nu4111692\u003c/span\u003e\u003cspan address=\"10.3390/nu4111692\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMurray CJL, Aravkin AY, Zheng P, Abbafati C, Abbas KM, Abbasi-Kangevari M, Abd-Allah F, Abdelalim A, Abdollahi M, Abdollahpour I, Abegaz KH, Abolhassani H, Aboyans V, Abreu LG, Abrigo MRM, Abualhasan A, Abu-Raddad LJ, Abushouk AI, Adabi M, Adekanmbi V. Global Burden of 87 Risk Factors in 204 Countries and territories, 1990\u0026ndash;2019: a Systematic Analysis for the Global Burden of Disease Study 2019. Lancet. 2020;396(10258):1223\u0026ndash;49. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/s0140-6736(20)30752-2\u003c/span\u003e\u003cspan address=\"10.1016/s0140-6736(20)30752-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOnyango AW, Jean-Baptiste J, Samburu B, Mahlangu TLM. Regional Overview on the Double Burden of Malnutrition and Examples of Program and Policy Responses: African Region. Ann Nutr Metab. 2019;75(2):127\u0026ndash;30. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1159/000503671\u003c/span\u003e\u003cspan address=\"10.1159/000503671\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePopkin BM. Relationship between shifts in food system dynamics and acceleration of the global nutrition transition. Nutr Rev. 2017;75(2):73\u0026ndash;82. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/nutrit/nuw064\u003c/span\u003e\u003cspan address=\"10.1093/nutrit/nuw064\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePopkin BM, Ng SW. The nutrition transition to a stage of high obesity and noncommunicable disease prevalence dominated by ultra-processed foods is not inevitable. Obes Rev. 2021;23(1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/obr.13366\u003c/span\u003e\u003cspan address=\"10.1111/obr.13366\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePrentice AM. The Double Burden of Malnutrition in Countries Passing through the Economic Transition. Annals Nutr Metabolism. 2018;72(3):47\u0026ndash;54. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1159/000487383\u003c/span\u003e\u003cspan address=\"10.1159/000487383\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRuel MT, Quisumbing AR, Balagamwala M. Nutrition-sensitive agriculture: What have we learned so far? Global Food Secur. 2018;17:128\u0026ndash;53. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.gfs.2018.01.002\u003c/span\u003e\u003cspan address=\"10.1016/j.gfs.2018.01.002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSteyn NP, Mchiza ZJ. Obesity and the nutrition transition in Sub-Saharan Africa. Ann N Y Acad Sci. 2014;1311(1):88\u0026ndash;101. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/nyas.12433\u003c/span\u003e\u003cspan address=\"10.1111/nyas.12433\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGabriel TS, Kasim M, Oluma FA, Muka T, Erand Llanaj. \u0026amp;. (2024). Adolescent nutrition in Nigeria: a systematic review. \u003cem\u003eJournal of Nutritional Science\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1017/jns.2024.34\u003c/span\u003e\u003cspan address=\"10.1017/jns.2024.34\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUyar BTM, Talsma EF, Herforth AW, Trijsburg LE, Vogliano C, Pastori G, Bekele TH, Huong LT, Brouwer ID. The DQQ is a Valid Tool to Collect Population-Level Food Group Consumption Data: A Study Among Women in Ethiopia, Vietnam, and Solomon Islands. J Nutr. 2023;153(1):340\u0026ndash;51. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.tjnut.2022.12.014\u003c/span\u003e\u003cspan address=\"10.1016/j.tjnut.2022.12.014\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang D, Fawzi WW. Impacts of school feeding on educational and health outcomes of school-age children and adolescents in low- and middle-income countries: protocol for a systematic review and meta-analysis. Syst Reviews. 2020;9(1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s13643-020-01317-6\u003c/span\u003e\u003cspan address=\"10.1186/s13643-020-01317-6\" 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":true,"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":"Diet quality, Female adolescent nutrition, Dietary diversity, Anthropometry, Food group consumption pattern","lastPublishedDoi":"10.21203/rs.3.rs-8689293/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8689293/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAdolescence is a critical period for establishing dietary patterns that influence growth, body composition, and long-term risk of non-communicable diseases. In Nigeria, limited population-based evidence exists on diet quality and its relationship with anthropometric status among female adolescents, particularly using standardized global diet quality indicators.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis descriptive cross-sectional study was conducted among 290 in-school female adolescents aged 13\u0026ndash;17 years in public and private secondary schools in Odeda Local Government Area, Ogun State. Dietary intake was assessed using the Dietary Quality Questionnaire for Nigeria, generating Dietary Diversity Score (DDS), NCD-Protect, NCD-Risk, Global Dietary Recommendation (GDR) score, and Minimum Dietary Diversity for Women (MDD-W). Anthropometric indices were obtained using standardized procedures, and BMI-for-age and height-for-age z-scores were computed with WHO AnthroPlus. Independent t-tests, chi-square tests, and Pearson correlation analyses were performed.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eApproximately 60.7% of participants met the MDD-W threshold. Staple foods were widely consumed, whereas intake of fruits, vegetables, pulses, and nuts remained suboptimal. High consumption of baked sweets, deep-fried foods, and sugar-sweetened beverages was observed. The mean DDS and GDR scores were modest, with public school adolescents exhibiting significantly higher GDR scores than private school counterparts (p\u0026thinsp;=\u0026thinsp;0.011). DDS was positively correlated with GDR (r\u0026thinsp;=\u0026thinsp;0.279, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Anthropometrically, 80.0% had normal BMI-for-age, 15.9% were thin, and 4.1% were overweight or obese, indicating a double burden of malnutrition. No significant association was found between diet quality indicators and BMI-for-age z-scores.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eFemale adolescents in Ogun State exhibit moderate dietary diversity alongside high exposure to NCD-risk foods and a coexisting burden of thinness and emerging overweight. School-based nutrition policies and food-environment interventions are urgently needed to improve diet quality during this critical life stage.\u003c/p\u003e","manuscriptTitle":"Diet quality and anthropometric parameter of in-school female adolescents in Ogun State, Nigeria: Descriptive Cross Sectional Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-22 16:29:41","doi":"10.21203/rs.3.rs-8689293/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-14T17:12:52+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-04T12:27:29+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-03T19:48:55+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-26T15:31:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"57823995983994976663565814898632125187","date":"2026-02-25T20:26:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"176262294262884378392581110656569313245","date":"2026-02-25T15:14:47+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-21T06:13:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"195378225698912361406361660039796909234","date":"2026-02-21T04:39:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"31931730363953853831780412085828157927","date":"2026-02-19T07:17:41+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-17T16:46:44+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-17T16:46:23+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-30T03:17:42+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-29T12:50:07+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Public Health","date":"2026-01-29T12:09:36+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":"dc0dc831-d617-462c-a9e7-e463924ff61e","owner":[],"postedDate":"February 22nd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-05T08:09:44+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-22 16:29:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8689293","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8689293","identity":"rs-8689293","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}