Artigos

Introduction
Many African countries are already dealing with the double burden of malnutrition, characterized by the coexistence of undernutrition, excessive weight, and non-communicable diseases (NCDs)1. The growing problem of obesity on the African continent2 is related to economic changes, urbanization, the aggressive marketing by food industries which have accelerated changes in nutritional standards3.
However, undernutrition, in its distinctive signs – thinness, stunting, and micronutrient deficiency – remains a major challenge for public health, especially in sub-Saharan Africa4. The African continent was the only region in the world where there was an increase in the number of children with a deficit in stature between 2000 and 2018. In sub-Saharan Africa, the prevalence of stunting in children below 5 years of age was 33%, in 2018; meanwhile, the prevalence of thinness and overweigh were 9 % and 8 % respectively5. Moreover, anemia and vitamin A deficiency have shown high prevalence among children and adolescents in the region6 and coexist with NDC risk factors like hyperglycemia and dyslipidemia6,7. Both forms of malnutrition are related to structural metabolic alterations associated with a greater risk of chronic non-communicable diseases in adulthood8.
An underlying factor in all forms of malnutrition is diet, whether due to insufficient consumption, inadequate nutritional quality, or excessive consumption9. The obesity is heavily influenced by the increasing consumption of highly processed foods10. However, paradoxically, it is possible that the increase in consumption of those foods may contribute to the persistence of malnutrition in those countries11. Furthermore, the low dietary diversity can contribute to malnutrition, especially with the undernutrition in growth liner12. Malnutrition is also related to infectious diseases, and low socioeconomic conditions13.
Benin is a country located in Sub-Saharian Africa with an estimated population of 10 million people in 2017, of which 47% were below 15 years of age and almost 10% were in a state of nutritional insecurity14. Childhood malnutrition is the main problem in the country, although overweight and obesity are already present among schoolchildren15. However, little is known about the cardio-metabolic profile, and the food consumption habits of schoolchildren from Benin, especially in secondary towns. Therefore, the present study aimed to describe nutritional indicators (anthropometric and food intake) and cardiometabolic indicators in schoolchildren living in Lokossa, Benin.

Methods
Study design and included population
This is a cross-sectional study using data from the survey “Nutritional and Health Status of Primary Education Students in the City of Lokossa, Benin”. Data collected was performed between December-2018/January-2019 in a representative sample of sixth-year elementary schoolchildren, from public and private schools, from the rural and urban area of the town of Lokossa, Benin.
The sample was selected using a cluster-sampling plan, with stratification of the primary sampling units (schools) in two stratum based on their geographical location (urban/rural). The schools were selected in each stratum with probability proportional to the schoolchildren number. Among the 131 schools, 26 were selected. All 6th-year schoolchildren attending the selected schools on the data collection day were invited to participate.
To estimate the outcomes of interest were considered a significance level of 5%, 10% prevalence of the principal interest conditions16, and a maximum error of 3.25%. A 1.25 design effect, based on a previous survey, was also considered17. A calculation method accounting for maximum possible sample size (Sample Size for Finite Population, fcp) was adopted. Based on these parameters, the size of the required sample was 667 individuals. Only 660 students were at the selected schools and were eligible to paticipate. Out of those, 35 refused to participate and 10 were absent on the day of the survey. Therefore, the studied sample included 615 schoolchildren. For the present analysis, three out of 615 schoolchildren were excluded because of missing data on nutritional status. Thus, the final sample comprised 612 schoolchildren.
Due to technical, material, and logistical reasons related to a study conducted in a African country18, blood assessment biochemical measurements were performed in a subsample. We set a subsample of 180 schoolchildren selected from 615 schoolchildren that accept to participate. From the total of 26 schools included on the sample, 22 were included in this phase, given that 4 schools did not allow us to take blood samples. Thus, subsample included 165 schoolchildren who accepted to have their blood collected. No age, sex, and school area (urban x rural) differences were found between the refusals and those who accepted blood collection (p>0.05).
Participants and parents or substitute signed an Informed Assent Form and an Informed Consent Term. The study protocol was approved by National Committee of Ethics of Health Research from Benin, opinion no.28, of Sept 20, 2018.

Data collection
Instruments from studies conducted in Benin, Brazil, and Burkina Faso were used to construct the questionnaire of this study19. The questionnaire was adapted considering Benin’s language and cultural context, and the level of education of the participants. The questionnaire was translated to French by a native researcher (HS) and revised by a Senior French researcher (NK). The questionnaire comprised sociodemographic characteristics, household and school settings, health related behaviors, protective factors, mental health, and general appreciation of the questionnaire. The questionnaire was pre-tested in two classrooms at Lokossa, Benin. Two pilot studies were also performed.
Trained and certified undergraduate nutritional science students collected data. Filled out research questionnaires and forms were typed with double checking.
Study variables
The demographic variables included were sex and age in years.
Nutritional indicators
The nutritional indicators were 1) regular food consumption of markers healthy and unhealthy eating, and dietary diversity score; 2) antrophometrics indicators stunting, thinness, overweight, as well as the antrophometrics measures height-for-age, BMI-for-age, waist circumference, and arms circumference; 3) anemia.
The food consumption was assessed based on the frequency of consumption in the previous seven days using the question “In the last 7 days, on how many days did you eat fruit?” with the possible answers “I did not eat; 1 day; 2 days; 3 days; 4 days; 5 days; 6 days; every day”. The food markers of healthy eating were beans, green vegetables, fruits, fish, and meats; the markers of unhealthy eating were soft drinks, bagged salty snacks, deep-fried salty snacks, and sweets. The regular food consumption (no, yes) was defined as the intake on five or more days in the previous seven days20.
The dietary diversity score (DDS) was estimated from one 24-hour dietary recall data. Foods identified in recalls were 16 food items and aggregated with the Food and Agriculture Organization of the United Nations/Food and Nutrition Technical Assistance Project (FAO/FANTA 2010) Guidelines20.The food groups were grain, milk and dairy products, eggs, fat, vegetables rich in vitamin A, fish, other fruits and vegetables, dark-green vegetables, and meats. For score calculation purposes “1” or “0” were attributed to the presence or absence of each food group. We added the values, and the score was characterized into the dietary diversity levels: low (?3 food groups), medium (4-5 groups), and high (?6 groups)21.
Body weight was measured with the schoolchildren wearing light clothing and no shoes, with a portable electronic scale, with a 150 kg capacity and 0.1 kg precision (Seca 803, Hamburg-Germany). The height was measured by a mobile stadiometer (SECA 213, Hamburg-Germany) with a millimetric resolution, positioning the children standing upon a platform, with the back of the heels and the occipital against the stadiometer, and the eyes looking forward horizontally. The body mass index (BMI) was calculated by dividing the weight (kilograms) by the square (meters) of the height.
Height-for-age (HAZ) and BMI-for-age (BAZ) indices were standardized according to Z-scores and categorized according to World Health Organization (WHO) cutoffs for children and adolescents aged 5-19 years old22. Indicators were estimated using WHO AnthroPlus software (Version 1.0.4)23. Thinness was defined as BAZ<-2 Z-score, stunting by HAZ<-2 Z-score, and excess weight by BMI >1 Z-score.
The waist circumference was measured at the middle point between the lowest rib and the iliac crest with a flexible measuring tape, with millimetric precision and a length of 2 meters. The circumference of the arm was measured at the middle point between the acromion and the ollecranus, using the same tape, with participants keeping their arms relaxed alongside their bodies, and the metric tape was placed firmly around their arms24.
The anthropometric measurements were performed twice, and the average of the two measurements was used.
All of the schoolchildren (n=615) were invited for a collection of 10 µL of blood by means of a puncture in the fingertip by a microcuvette to measure their hemoglobin (Hb). Only 308 students accepted to undergo puncture in the fingertip. The samples were examined immediately after collection, using the Hemocue 201® device (HemoCue, Angelholm, Suécia). Anemia was defined by the hemoglobin cutoffs: 1) <11.5g/dl for schoolchildren <11 years of age; 2) <12 g/dl for boys aged 12-14 years old and for nonpregnant girls of 12-17 years; and 3) <13 g/dl for boys aged 15-17 years old25.

Cardio-metabolic indicators
The cardiometabolic indicators included were blood pressure (BP), high triglyceride, high total cholesterol, low HDL-cholesterol, and hyperglycemia.
The systolic BP (SBP) and the diastolic BP (DBP) were measured using the Omron® 705-IT automatic oscillometric device (Omron Healthcare, Bannockburn, IL, USA) using standardized procedures25 Three consecutive measurements were taken, with three minute interval, recording the average between the two last measurements. High blood pressure was defined as SBP and/or DBP > 90th percentile according to the reference values by sex, age (in complete years), and height26.
A 7 ml volume of venous blood was collected by venipuncture for the subsample (n=165) at the schools’ facilities. The material was stored in gel tubes for the analyses of cholesterol and triglycerides (5ml) and in tubes containing sodium fluoride for the analysis of glucose (2ml), both maintained in a cooler with ice and sent for analysis at the Toviklin hospital laboratory within one hour after the collection. Plasmatic concentrations of total cholesterol, high density lipoproteins (HDL) cholesterol, triglycerides, and glucose were enzymatically determined by the Erba Mannheim automatic analyzer (London-United Kingdom). The schoolchildren were advised to fast for eight hours. The concentration of LDL-cholesterol was calculated by the formula: LDL-cholesterol = totalcholesterol - (HDL + (triglycerides/5).
High triglyceride levels were defined by values ?90mg/dl for participants aged 8-9 years old and ?110mg/dl for those older than 9; high total cholesterol was defined by values ?170 mg/dl; low HDL-cholesterol was defined by HDL-cholesterol values <45 mg/dl and, for girls aged >16 years older, values of <50 mg/dl, according to cut-off points defined by the Brazilian Society of Cardiology for Children and Adolescents27. Hyperglycemia is defined by fasting glucose ?100 mg/28.

Statistical analysis
Means and standard deviation for continuous variables and prevalence with a 95% confidence interval (95% CI) for categorical variables were estimated according to sex. The means were compared using the Wald test estimated by linear regression. The proportions were compared using the Pearson chi-square test. For all tests a level of 5% was considered as statistically significant.
Additionally, we estimated the prevalences and 95% CI for all indicators according to age range (8-11 and >12 years), geographic area (urban and rural), and school administration (public and private).
The analyses were carried out using the Stata 14.0 Program (Stata Corporation, College Station, USA). To account for the sampling design, the svy commands in Stata were used to consider sampling weighting designing effect, and finite sample size.

Results
The final sample comprised schoolchildren aged 8-17 years, who were mostly male (56.3%), from schools in urban areas (61.9%). The mean age was 11.6 years, with boys older than girls (p=0.022).
The anthropometric and laboratorial measurements and the blood pressure levels of boys and girls are shown in Table 1. The mean of the anthropometric, laboratory, and blood pressure measures were similar for boys and girls, except for glycemia, which was higher for boys (p=0.024). Both sexes showed negative means for the height-for-age and for BMI-for-age. Boys showed a height-for-age index lower than the girls (p< 0.01).
Figure 1 shows the distribution of the height-for-age index of boys and girls, placed at the left in comparison to the reference curves proposed by the WHO. The BMI-for-age index curve is also placed to the left for boys and girls (Figure 1B).
Stunting global prevalence was 25.5% (95%CI:20.6-31.2) and was higher in boys compared to girls (31.3%; 95%CI:24.8-38.5 vs 18.3%; 95%CI:13.0-25.0; p<0.001). However the distribution of thinness (13.1%; 95%CI:8.9-18.7) was similar for boys and girls (p=0.845). The prevalence of excess weight was 6.5% (95%CI: 3.9-10.6) and was not different according sex (p=0.197). Anemia was the most common nutritional deficit indicator, with a total prevalence of 48.7% (95%CI:41.2-56.7), and was evenly distributed among boys and girls (p=0.307) (Figure 2).
The markers of healthy foods consumed more often were fish and green vegetables. Fried salty snacks and sweet biscuits stood out as the most eaten markers of unhealthy foods. Half of the schoolchildren presented high dietary diversity and 7.1% (95%CI:4.1-12.0) had a low diversity diet. The consumption of sweet biscuits was more frequent among girls (p<0.001) (Table 2).
High PB was present in nearly one third of the schoolchildren, and did not vary between sexes (p=0.478). The most prevalent cardio-metabolic alteration was low HDL-cholesterol (34.3%; 95%CI: 20.0-52.1), with even distribution between the sexes (p=0.650). Hyperglycemia was present in less than 10% (8.3; 95%CI:3.9-16.6) of the schoolchildren; high total cholesterol, in nearly 12% (11.6; 95%CI:7.0-18.5), and triglycerides, in 2.3% (95% CI:0.9-5.7), with no variation between the sexes (p=0.607, p=0.957, p=0.731 respectively) (Figure 3)
The additional analysis showed that the prevalence of stunting, thinness, and anemia were higher in older schoolchildren than the younger (p<0.001, p<0.001, p=0.011 respectively), however, the prevalence of excess weight was greater in the younger (p<0.001). Stunting (p=0.031) and anemia (p<0.017) were more frequent among schoolchildren from rural than urban areas. In contrast, anemia (p<001), low dietary diversity (p=0.003), and high blood pressure (p<0,001) were more frequent in public schools.
Discussion
The current study, conducted with 6th-grade schoolchildren from primary schools from rural and urban areas of Lokossa, Benin, identified that thinness, and particularly stunting and anemia, persist as the main nutritional problems for both sex. However, stunting was more frequent among the boys, indicating a sex-specific difference in chronic malnutrition. Moreover, the results also revealed that excess weight and cardiometabolic alterations, such as high BP, hyperglycemia, and low HDL, were present among the Lokossa schoolchildren. The findings in the present study suggest the emergence of a double burden of malnutrition in the schoolchildren from Lokossa.
The deficit in the schoolchildren’s linear growth is evidenced by the steep shift of distribution in the Z-score concerning height for both sexes. Boys had a shorter stature than girls, finding similar to a previous study conducted with schoolchildren in Benin28 and to other studies conducted in Sub-Saharan Africa30,31. In fact, study conducted with school-age adolescents from eight Sub-Saharan African countries revealed more vulnerability to stunting among male adolescents when compared to female adolescents32. Compared to the girls, the boys present a higher vulnerability to premature birth and infectious diseases in early childhood, conditions that contribute to a more severe malnutrition for males in the early years of life33. Furthermore, the compensatory growth during adolescence happens at a later age for boys as compared to girls due to puberty32, thus contributing to the observed differences.
However, the results indicate a similar distribution of thinness in both sexes. Although consistent with findings from previous studies among schoolchildren in Pakistan34, this finding diverges from a meta-analysis that observed a higher prevalence of stunting and thinness in boys35. It is important to mention that the delay in pubertal maturation and reduction in muscle strength are some of thinness consequences36.
The excess weight is also a nutritional problem, although to a lesser scale. In fact, the survey conducted in 2016 with a representative sample of Benin schoolchildren, aged 10-19 years old, had already indicate a prevalence of excess weight of 8.1%15 and of 1.6% of obesity15. Meanwhile, the prevalence of excess weight observed in schoolchildren from the town of Parakou (Benin) reached 16.2% (overweight = 12.5% and obesity =3.7%) in 201737. The excess weight prevalence in African countries is variable38,39, since its occurrence is influenced by economic and sociocultural differences, as well as by the level of change in nutritional standards in each country39.
The anemia was the most important nutritional deficit identified in this study. A high prevalence of anemia in schoolchildren from Benin, 41%40 and 51%41, had already been described in 2005. According to the WHO, a prevalence of 40% for anemia indicates a serious public health problem42. Such a high prevalence may be attributed partially to the typical diet of Benin, poor in iron and abundant in foods that are rich in phytates and tannins, which inhibit the absorption of iron40. Other factors, such as malaria43, intestinal parasites, deficiency of other micronutrients, and poor socioeconomic conditions44 may exacerbate iron deficiency. It should be highlighted that only 308 adolescents were examined, and they did not differ from those who were not examined, in terms of age, sex, and school location (p>0.05).
The schoolchildren’s dietary intake still has a strong presence of traditional foods, especially fish and green vegetables, both the healthy food most regularly consumed, which might reflect the availability of those foods in the country. Fish is the Benin’s main source of animal protein45. Lokossa’s geographic position, in the vicinity of Lake Toho and Mono River, even further favors the consumption of fish46. Likewise, the consumption of green vegetables is common among the country in general47. However, we can already observe the influence of a Western diet, especially regarding the consumption of soft drinks and of sweet biscuits. Both, sweet biscuits and soft drinks have shown an increase in consumption in recent decades among African children and adolescents48.
The diet quality evaluated by food diversity was relatively high among the schoolchildren in this study, with half of them showing high food diversity. In part, the high prevalence of food diversity in this study may be attributed to the fact that most of the schoolchildren who participated in the study (61.9%) are from urban schools, and evidence suggests that the diet in urban areas of Sub-Saharan African countries is more diverse49.
High PB is a condition less commun in children and adolescents. However, another study indicated a substantial increase in the BP levels at this phase of life50. In our study, high BP was present in almost one-third of the schoolchildren, which proved to be much higher than the prevalence of 12.7% (95%CI:2.1-30.4) reported by a meta-analysis with 25 studies in children and adolescents on the African continent51. The increase in obesity is one of the main factors that increases blood pressure levels in children and adolescents51, along with the lack of physical activity and excessive intake of sodium28,30.
The cardio-metabolic change most prevalent among schoolchildren was low HDL-cholesterol (34.3%) with a similar distribution for both sexes. Elevated total cholesterol was observed in 12% and hyperglycemia, in less than 10% of the schoolchildren; the percentages did not vary between boys and girls. The comparison with other studies is limited by the divergence between the adopted cut-off points. Considering the same cut-off points, the prevalence of hyperglycemia was higher than results observed in schoolchildren from Ouagadougou (Burkina-Faso) (2%) in 200952 and in Brazilian adolescents from the Study of Cardiovascular Risk among Adolescents (ERICA, in Portuguese) (4.1%) in 2012-201353. On the other hand, the prevalence of high total cholesterol ranged from 8% in boys to 16% in girls from Ouagadougou (Burkina-Faso)52 and from 22.7% to 25.7% respectively among boys and girls in the ERICA study54.
Both, undernutrition and excess weight are associated with alterations in glycemic levels: reduction and increase in glycemic levels, respectively. However, children who presented simultaneously stunting and excess weight may have even higher glycemic levels than do obese children55. Thus, that the concomitance of the two nutritional problems may explain hyperglycemia prevalence in this study. The magnitude of dyslipidemia has increased in children and adolescents53, influenced by excess weight56 and physical inactivity, which may contribute especially to a higher magnitude of low HDL-cholesterol57.
The coexistence of stunting, thinness, and anemia combined with excess weight, as well as with cardio-metabolic alterations and with the presence of high BP disclose the double burden of malnutrition at an important phase of the schoolchildren cognitive and physical development. Malnutrition during school age may have damaging effects over the long term, such as compromising intellectual and school performance, and the reduction of stature in adult life36. Moreover, malnutrition is a risk factor for obesity, metabolic alterations, and diabetes lifelong58. Furthermore, excess weight, metabolic alterations, and high BP levels observed in this study confirm the exposure to risk factors for chronic non-communicable diseases52 and indicate a potential impact on the burden of these diseases.
The additional findings revealed a higher prevalence of undernutrition among older schoolchildren compared to younger ones. This may be attributed to the increased daily energy and nutrient requirements necessary for optimal growth, which can exacerbate existing nutritional deficiencies19. In contrast, the excess weight prevalence greater in the younger schoolchildren may reflect changes in food consumption and in environmental food. The schoolchildren from rural areas seems more vulnerable to undernutrition, likely as a result of the higher presence of food insecurity in rural areas19. Furthemore, low dietary diversity, anemia, and high blood pressure were more frequent in public schools. Thus, in addition to main results from this study, these further findings should be considered to enhance the interventions to promote the nutrition and health of the Lokossa schoolchildren59.
Among the strengths of the present study, we emphasize the representative sample of schoolchildren of Primary School, and the direct form of taking anthropometric, blood pressure, biochemical, and food intake measurements. One of the limitations of this study was the use of a single 24-hour food record to evaluate food diversity, which does not reflect the usual diet of the children, and also did not enable the evaluation of the micronutrient consumption60. Although the method is widely used for data collection, it may be affected by memory bias. Another limitation was the difficulty to obtain some parents’s consent to collect blood samples, possibly due to cultural or social barriers. Consequently, not all of the schoolchildren initially selected for the sub-sample were included in the study.

Conclusion
The present study contributes to filling in the gap regarding the missing data related to the nutritional state and to the health conditions of schoolchildren from a Benin middle-town. In general, the results indicate a multiple burden of malnutrition in that population, which manifests itself by high undernutrition and a high prevalence of anemia, coexisting with the presence of excess weight, high BP, and metabolic changes. Besides, the regular consumption of unhealthy eating indicates a relevant diet concern. We hope that the results of this study will support the development of health and nutrition policies based on evidence for the prevention and control of contextual and behavioral factors associated with indicators of nutritional and health status. The results may also subsidy public health interventions, such as the expansion of free school lunch programs and income transfer programs.


Collaborations
H.S. collected and entered data, contributed to data analysis and interpretation and wrote the manuscript. L.G. and S.M.B. conceptualized and designed the study and supervised data acquisition, contributed to data analysis and interpretation and writing of the manuscript. L.L.M. and N.K. participated in data interpretation and final manuscript approval. All authors have approved the final manuscript version.


Acknowledgments
The authors thank the participants and the staff of the Research on Nutritional and Health Status of Primary Education Students in the City of Lokossa-Benin for their important contributions. The authors also thank the staff of the ELSA Study in Minas Gerais and the Postgraduate Program in Public Health, Faculty of Medicine, Universidade Federal de Minas Gerais for their support. H.S. was a PhD candidate, supported by CAPES-BR (Finance Code 001). L.G., S.M.B. are research fellows of CNPq, Brazil. The funder had no role in the design and collection, analysis or writing of this article.

References

1- Popkin BM, Corvalan C, Grummer-Strawn LM. Dynamics of the double burden of malnutrition and the changing nutrition reality. Lancet. 2020; 395(10217):65-74.
2- World Health Organization (WHO). Obesity. [Accessed July 2022]. Geneva: WHO; 2020Available from: https://www.who.int/health-topics/obesity#tab=tab_1.
3- Klingberg S, Draper CE, Micklesfield LK, Benjamin-Neelon SE, van Sluijs EMF. Childhood Obesity Prevention in Africa: A Systematic Review of Intervention Effectiveness and Implementation. Int J Environ Res Public Health. 2019;16(7):1212.
4- Irisarri-Gutiérrez MJ, Acosta L, Parker LA, Toledo R, Bornay-Llinares FJ, Esteban JG, Muñoz-Antolí C. Anemia and undernutrition in intestinally parasitized schoolchildren from Gakenke district, Northern Province of Rwanda. PLoS One. 2022;17(1):e0262361.
5- United Nations Children’s Fund; World Health Organization; World Bank (2018) The State of the World’s Children 2019. Children, Food and Nutrition: Growing well in a changing world. [Accessed August 2024]. Available from: https://www.unicef.org/brazil/media/5581/file/SOWC2019_relatorio_completo_em_ingles.pdf .
6-Etaniamhe Orimadegun B. Dyslipidaemia in African Children and Adolescents [Internet]. Management of Dyslipidemia. IntechOpen; 2021. Available from: http://dx.doi.org/10.5772/intechopen.96804;
7-Traoré, S.G., Kouassi, K.B., Coulibaly, J.T. et al. Dietary diversity in primary schoolchildren of south-central Côte d’Ivoire and risk factors for non-communicable diseases. BMC Pediatr 2022; 22: 651.
8- Wells JC, Sawaya AL, Wibaek R, Mwangome M, Poullas MS, Yajnik CS, Demaio A. The double burden of malnutrition: aetiological pathways and consequences for health. Lancet. 2020;395(10217):75-88.
9- Development Initiatives. Global Nutrition Report: Shining a light to spur action on nutrition. [Accessed June 2022]. Global Nutrition Report; 2018. Available from: https://www.who.int/nutrition/globalnutritionreport/2018_Global_Nutrition_Report.pdf.
10- Dicken, S.J., Batterham, R.L. Ultra-processed Food and Obesity: What Is the Evidence?. Curr Nutr Rep. 2024; 13: 23–38.
11- Reardon T, Tschirley D, Liverpool-Tasie LSO, Awokuse T, Fanzo J, Minten B, Vos R, Dolislager M, Sauer C, Dhar R, Vargas C, Lartey A, Raza A, Popkin BM. The Processed food revolution in African food systems and the Double Burden of Malnutrition. Glob Food Sec. 2021; 28 (3):100466
12- Zeinalabedini, M., Zamani, B., Nasli-Esfahani, E. et al. A systematic review and meta-analysis of the association of dietary diversity with undernutrition in school-aged children. BMC Pediatr. 2023; 23:269.
13- Ahmad, D., Afzal, M. & Imtiaz, A. Effect of socioeconomic factors on malnutrition among children in Pakistan. Futur Bus J 6, 30 (2020).
14- World Food Programme (WFP). Benin Country Brief. [Accessed June 2021].WFP; 2020. Available from: https://www.wfp.org/countries/benin
15- Houinato O, Kpozehouen A, Hounkpatin B, Gbédji K, Robin H, Mizéhoun-Adissoda C, Guédou F, Houinato D, Perrin R. Prevalence and Factors Associated with Overweight and Obesity among Adolescents in Schools in Benin in 2016. Open Journal of Epidemiology. 2019; 9 (7): 213-242.
16- Ministere de la Sante (BJ). Rapport final: enquête globale sur la santé des élèves au Bénin en 2009. [Accessed Setember 2024]. MS; 2009. Available from: http://www.who.int/ncds/surveillance/gshs/Country_Report_GSHS_BENIN_2009.pdf
17- Sagbo H, Ekouevi DK, Ranjandriarison DT, Niangoran S, Bakai TA, Afanvi A, Dieudonné S, Kassankogno Y, Vanhems P, Khanafer N. Prevalence and factors associated with overweight and obesity among children from primary schools in urban areas of Lomé, Togo. Public Health Nutr. 2018; 21(6):1048-1056.
18- Daboné C, Delisle HF, Receveur O. Poor nutritional status of schoolchildren in urban and peri-urban areas of Ouagadougou (Burkina Faso). Nutr J. 2011; 10 (19): 34.
19- Sagbo H, Barreto SM, Costa ABP, Mendes LL, Khanafer N, Giatti L. Undernutrition and associated factors in primary schoolchildren in Lokossa, Benin: a cross-sectional study. Rev Bras Enferm. 2021;75(3):e20210254.
20- I.R. Castro, L.O. Cardoso, E.M. Engstrom, R.B. Levy, C.A. Monteiro. Surveillance of risk factors for non-communicable diseases among adolescents: the experience in Rio de Janeiro, Brazil. Cad. Saude Publica, 24 (2008), pp. 2279-2288.
21- Food and Agriculture Organization. United Nations (FAO). Guidelines for Measuring Household and Individual Dietary Diversity. Rome, Italy. FAO; 2010
22- de Onis M, Onyango AW, Borghi E, Siyam A, Nishida C, Siekmann J. Development of a WHO growth reference for school-aged children and adolescents. Bull World Health Organ. 2007; 85(9):660-7.
23- World Health Organization (WHO). AnthroPlus for Personal Computers Manual: Software for Assessing Growth of the World’s Children and Adolescents. Geneva: WHO; 2009.
24- Kuschnir MC, Bloch KV, Szklo M, Klein CH, Barufaldi LA et al. ERICA: prevalence of metabolic syndrome in Brazilian adolescents. Rev Saude Publica. 2016;50 (Suppl 1):11s.
25- World Health Organization. (WHO) Guideline on haemoglobin cutoffs to define anaemia in individuals and populations. Geneva: WHO; 2024.
26- Flynn JT, Kaelber DC, Baker-Smith CM, Blowey D, Carroll AE, Daniels SR, de Ferranti SD, Dionne JM, Falkner B, Flinn SK, Gidding SS, Goodwin C, Leu MG, Powers ME, Rea C, Samuels J, Simasek M, Thaker VV, Urbina EM. Clinical Practice Guideline for Screening and Management of High Blood Pressure in Children and Adolescents. Pediatrics. 2017; 140(3):e20171904.
27- Faludi AA, Izar MCO, Saraiva JFK, Chacra APM, Bianco HT, Afiune A Neto, Bertolami A, Pereira AC, Lottenberg AM, Sposito AC, Chagas ACP, Casella A Filho, Simão AF, Alencar AC Filho, Caramelli B, Magalhães CC, Negrão CE, Ferreira CEDS, Scherr C, Feio CMA, Kovacs C, Araújo DB, Magnoni D, Calderaro D, Gualandro DM, Mello EP Junior, Alexandre ERG, Sato EI, Moriguchi EH, Rached FH, Santos FCD, Cesena FHY, Fonseca FAH, Fonseca HARD, Xavier HT, Mota ICP, Giuliano ICB, Issa JS, Diament J, Pesquero JB, Santos JED, Faria JR Neto, Melo JX Filho, Kato JT, Torres KP, Bertolami MC, Assad MHV, Miname MH, Scartezini M, Forti NA, Coelho OR, Maranhão RC, Santos RDD Filho, Alves RJ, Cassani RL, Betti RTB, Carvalho T, Martinez TLDR, Giraldez VZR, Salgado W Filho. Atualização da Diretriz Brasileira de Dislipidemias e Prevenção da Aterosclerose – 2017. Arq Bras Cardiol. 2017;109(2 Supl 1):1-76.
28- Zimmet P, Alberti KG, Kaufman F, Tajima N, Silink M, Arslanian S, Wong G, Bennett P, Shaw J, Caprio S; IDF Consensus Group. The metabolic syndrome in children and adolescents - an IDF consensus report. Pediatr Diabetes. 2007 ; 8(5) :299-306.
29- Azandjeme CS, Alihonou F, Sossa CJ, Gbatcho U, Gounongbe F, Michel Makoutode F, Agueh VD. Factors Associated with the Nutritional Status of Schoolchildren in the Main City of Benin Republic, Sub-Saharan Africa. Int Arch Public Health Community Med. 2020; 4:036.
30- Mitsunaga A, Yamauchi T. Evaluation of the nutritional status of rural children living in Zambia. J Physiol Anthropol. 2020;39(1):34
31- Bazie GW, Seid M, Egata G. Prevalence and Predictors of Stunting among Primary School Children in Northeast Ethiopia. J Nutr Metab. 2021; 2021:8876851.
32- Thurstans, S.; Opondo, C.; Seal, A.; Wells, J.C.; Khara, T.; Dolan, C.; Briend, A.; Myatt, M.; Garenne, M.; Mertens, A.; et al. Understanding Sex Differences in Childhood Undernutrition: A Narrative Review. Nutrients 2022, 14:948.
33- Wells JC. Natural selection and sex differences in morbidity and mortality in early life. J Theor Biol. 2000; 202: 65-76.
34- Rahimi BA, Khalid AA, Lali WM, Khalid WA, Rahimi JA, Taylor WR. Prevalence and associated risk factors of stunting, wasting/thinness, and underweight among primary school children in Kandahar City, Afghanistan: a cross-sectional analytical study. BMC Public Health. 2024;24(1):2321.
35- Saavedra JM, Prentice AM. Nutrition in school-age children: a rationale for revisiting priorities. Nutr Rev. 2023;81(7):823-843.
36- Best C, Neufingerl N, van Geel L, van den Briel T, Osendarp S. The nutritional status of school-aged children: why should we care? Food Nutr Bull. 2010; 31(3):400-17.
37- Akombi BJ, Agho KE, Hall JJ, Wali N, Renzaho AMN, Merom D. Stunting, Wasting and Underweight in Sub-Saharan Africa: A Systematic Review. Int J Environ Res Public Health. 2017; 14(8):863.
38- Kiosia A, Dagbasi A, Berkley JA, Wilding JPH, Prendergast AJ, Li JV, Swann J, Mathers JC, Kerac M, Morrison D, Drake L, Briend A, Maitland K, Frost G. The double burden of malnutrition in individuals: Identifying key challenges and re-thinking research focus. Nutr Bull. 2024;49(2):132-145.
39- Tadesse Y, Derso T, Alene KA, Wassie MM. Prevalence and factors associated with overweight and obesity among private kindergarten school children in Bahirdar Town, Northwest Ethiopia : cross-sectional study. BMC Res Notes. 2017; 10(1):22.
40- Alaofè H, Zee J, Dossa R, Turgeon O'Brien H. Iron status of adolescent girls from two boarding schools in southern Benin. Public Health Nutr. 2008; 11(7):737-46.
41- Alaofè H, Zee J, Turgeon O'Brien H. Apports alimentaires en fer et anémie ferriprive chez des adolescentes au Bénin. Rev Epidemiol Sante Publique. 2007 ; 55(3) :187-96.
42- World Health Organization (?WHO). Nutrition Landscape Information System country profile indicators: interpretation guide, Geneva: WHO; 2019.
43- Alkholy UM, El Gebaly SM, Morsi WEMA, Elawamy WE, Etewa SE, Yousef AM. The Impact of Parasitic Infestation on Nutritional Status and Micronutrients among Children. J Parasitol Res. 2024; 2024:6996968.
44- Ncogo P, Romay-Barja M, Benito A, Aparicio P, Nseng G, Berzosa P, Santana-Morales MA, Riloha M, Valladares B, Herrador Z. Prevalence of anemia and associated factors in children living in urban and rural settings from Bata District, Equatorial Guinea, 2013. PLoS One. 2017; 12(5):e0176613.
45- Mitchikpe E, Biaou E, Kpenavoun A, Gandonou E, Gounou E. Etude sur les normes de consommation des principaux produits vivriers et l’amélioration des conditions de vie au Bénin.2020. Available from: file:///C:/Users/Windows/Downloads/Consommation_Produits_Vivrier__Benin_Rapprt_Final_Sept.2010.pdf
46- Codjo V, Zannou A, Biaou G. Déterminants socio-économiques de l’utilisation des engins et pratiques de pêche destructives des ressources halieutiques sur le lac Toho au Bénin (Afrique de l’Ouest). International Journal of Biological and Chemical Sciences. 2020; 14: 2670-2683
47- Vodouhe SE,Tossou RC, Soumanou MM. Perception des consommateurs sur la qualité nutritionnelle et sanitaire de quelques légumes feuilles locaux produits dans la zone côtière du Sud-Benin. Bulletin de la Recherche Agronomique du Benin. 2012; 13-23. Available from: http://www.slire.net/download/2123/article_2_vodouh_et_al_perceptionbrabnspcialprdvancosocrurdc_2012.pdf
48- Reardon T, Tschirley D, Liverpool-Tasie LSO, Awokuse T, Fanzo J, Minten B, Vos R, Dolislager M, Sauer C, Dhar R, Vargas C, Lartey A, Raza A, Popkin BM. The Processed food revolution in African food systems and the Double Burden of Malnutrition. Glob Food Sec. 2021; 28:100466.
49-Avesani M, Calcaterra G, Sabatino J, Pelaia G, Cattapan I, Barillà F, Martino F, Pedrinelli R, Bassareo PP, Di Salvo G; Working Group on Congenital Heart Disease, Cardiovascular Prevention in Paediatric Age of the Italian Society of Cardiology (SIC). Pediatric Hypertension: A Condition That Matters. Children (Basel). 2024;11(5):518.
50- Letswalo BP, Schmid-Zalaudek K, Brix B, Matjuda EN, Klosz F, Obernhumer N, Gaisl M, Engwa GA, Sewani-Rusike C, Fredriksen PM, Nkeh-Chungag B, Goswami N. Cardiometabolic risk factors and early indicators of vascular dysfunction: a cross-sectional cohort study in South African adolescents. BMJ Open. 2021;11(3):e042955.
51- Noubiap JJ, Essouma M, Bigna JJ, Jingi AM, Aminde LN, Nansseu JR. Prevalence of elevated blood pressure in children and adolescents in Africa : a systematic review and meta-analysis. Lancet Public Health. 2017 ; 2(8) :e375-e386.
52- Daboné C, Delisle H, Receveur O. Cardiometabolic Risk Factors and Associated Features in 5th Grade Schoolchildren in Ouagadougou, Burkina Faso (West Africa). International Journal of Child Health and Nutrition. 2012; (1):104-112
53- Kuschnir MC, Bloch KV, Szklo M, Klein CH, Barufaldi LA, Abreu Gde A, Schaan B, da Veiga GV, da Silva TL, de Vasconcellos MT, de Moraes AJ, Borges AL, de Oliveira AM, Tavares BM, de Oliveira CL, Cunha Cde F, Giannini DT, Belfort DR, Santos EL, de Leon EB, Fujimori E, Oliveira ER, Magliano Eda S, Vasconcelos Fde A, Azevedo GD, Brunken GS, Guimarães IC, Faria Neto JR, Oliveira JS, de Carvalho KM, Gonçalves LG, Monteiro MI, Santos MM, Muniz PT, Jardim PC, Ferreira PA, Montenegro RM Jr, Gurgel RQ, Vianna RP, Vasconcelos SM, Martins SM, Goldberg TB. ERICA: prevalence of metabolic syndrome in Brazilian adolescents. Rev Saude Publica. 2016;50 (Suppl 1):11s.
54- Faria-Neto JR, Bento VFR, Baena CP, Olandoski M, Gonçalves LGO, Abreu GA, Kuschnir MCC, Bloch KV. ERICA: prevalência de dislipidemia em adolescentes brasileiros. Rev Saude Publica. 2016;50(supl 1):10s.
55- de Moraes RCS, Sawaya AL, Vieira ACA, Pereira JKG, de Brito Alves JL, de Luna Freire MO, Filgueiras AR, Martins VJB. Food addiction symptoms and metabolic changes in children and adolescents with the double burden of malnutrition. Br J Nutr. 2021;126(12):1911-1918.
56- Sbaraini M, Cureau FV, Sparrenberger K et al. (2020) Severity of obesity is associated with worse cardiometabolic risk profile in adolescents: Findings from a Brazilian national study (ERICA). Nutrition, 75-76.
57- Singleton CMH, Brar S, Robertson N, DiTommaso L, Fuchs GJ 3rd, Schadler A, Radulescu A, Attia SL. Cardiometabolic risk factors in South American children: A systematic review and meta-analysis. PLoS One. 2023;18(11):e0293865
58- Grey K, Gonzales GB, Abera M, Lelijveld N, Thompson D, Berhane M, Abdissa A, Girma T, Kerac M. Severe malnutrition or famine exposure in childhood and cardiometabolic non-communicable disease later in life: a systematic review. BMJ Glob Health. 2021; 6(3):e003161.
59- World Health Organization (?WHO). Benin: Nutrition and health monitoring to bolster children’s health. [Accessed Setember 2024]. Geneva: WHO; 2024. Available from: https://www.afro.who.int/photo-story/benin-nutrition-and-health-monitoring-bolster-childrens-health
60- Basak Tukun A, Rowe S, Johnson LK, Love DC, Belury M, Conrad Z. Micronutrient intake from three popular diet patterns in the United States: modeled replacement of foods highest in added sugar and sodium using the National Health and Nutrition Examination Survey, 2005-2018. Front Nutr. 2023;10:1217774.