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Abstract

Objective:

To comprehensively analyze the factors associated with anemia among adolescent girls in Pekanbaru, with a focus on nutritional status and anthropometric factors.

Methods:

A cross-sectional study was conducted among 230 adolescent girls aged 10–19 years in Pekanbaru from six State Senior High Schools. Simple random sampling technique with proportional allocation to size was used to select the participants. Anemia was defined as hemoglobin levels below the WHO cut-off point. Data on age, menstrual patterns, iron supplementation, and knowledge of anemia were collected from structured interviews, dietary intake from 24-h food recall questionnaires, anthropometric measurements from height, weight, body mass index, mid-upper arm circumference.

Results:

The prevalence of anemia among adolescent girls in Pekanbaru was 19.9%. The average age of the adolescent girls was 15 years with a menarche age of 12 years. Although statistically insignificant through bivariate testing, it was found that negative attitudes toward iron supplementation, noncompliance in taking iron supplements, and low mid-upper arm circumference may increase the risk of anemia among respondents. Multivariate analysis showed that variables that significantly influenced anemia were low mid-upper arm circumference (PR = 1.951, 95% CI: 1.05–3.60), nutritional status underweight (PR = 0.506, 95% CI: 0.26–0.96), and vitamin B12 intake (PR = 0.558, 95% CI: 0.31–0.97). Adolescent girls with low mid-upper arm circumference had a 1.95 times higher risk of experiencing anemia after controlling for other variables.

Conclusion:

Anemia among adolescent girls in Pekanbaru were significantly associated with low mid-upper arm circumference (<22 cm), nutritional status (underweight), and vitamin B12 intake. A comprehensive and sustained approach is recommended, including enhanced nutrition education, and dietary diversification.

Keywords

Anemia adolescent girls nutritional status anthropometry mid-upper arm circumference

Introduction

Anemia remains a significant global public health issue, particularly affecting adolescent girls aged 10–19 years. According to the World Health Organization (WHO), anemia affects approximately 29.9% of nonpregnant women globally, with iron deficiency being the leading cause.¹ In Indonesia, anemia prevalence among adolescent girls remains high. Based on the Basic Health Research 2018 data, about 32% of adolescent girls were anemic, a condition that may impair cognitive development, reduce physical performance, and increase maternal and perinatal risks in the future.²

Adolescent anemia is a complex condition with both immediate and long-term consequences. It can impair physical endurance, cognitive development, academic performance, and increases the risk of complications in future pregnancies, such as premature birth, low birth weight, and maternal mortality. In fact, maternal mortality in Indonesia remains the second highest among Association of Southeast Asian Nations countries, underscoring the importance of early prevention.³

The most common cause of anemia is iron deficiency.⁴ However, the causes of anemia are multifactorial, including length of menstruation, knowledge about anemia, dietary intake, nutritional status, iron tablet supplementation,⁵ micronutrient deficiencies (vitamin A, zinc), inflammation, and hemoglobinopathies.⁶ In adolescent girls, menstrual periods longer than 5 days are strongly associated with iron loss and anemia risk.^7,8 Knowledge about anemia also influences health behavior; those with better awareness are more likely to adopt preventative practices.⁹ Nutritional diversity and intake of key nutrients such as iron and folic acid play a crucial role in maintaining hemoglobin levels.^10,11

Nutritional status can be assessed using anthropometric indicators. One reliable measure is mid-upper arm circumference (MUAC).¹² MUAC is considered more effective than body mass index (BMI) for detecting malnutrition in adolescents and is a practical tool in both epidemiological and clinical settings, which have been shown to correlate with hemoglobin levels, where each centimeter increase in MUAC is associated with a 0.11 g/dL rise in hemoglobin.¹³

Although iron supplementation programs have been implemented in Indonesia since the 1990s, their effectiveness remains limited due to low coverage, poor adherence, and lack of awareness among adolescents.¹⁴ These challenges point to systemic limitations in the current approach, including insufficient education, limited screening coverage, and lack of integration between schools and health services.

In Pekanbaru, Riau Province—a region with above-average income levels—the problem persists. Despite its economic advantages,¹⁵ 2023 screening data revealed that nearly 30% of adolescent girls in the city were anemic, significantly higher than the national average of 18%.¹⁶ This indicates that economic development alone does not resolve nutritional and health disparities, and that local-specific factors must be considered.¹⁷

To address these limitations, this study takes a more integrated approach. We simultaneously assessed anthropometric indicators (MUAC and BMI) and nutrient intake (specifically protein and vitamin B12 adequacy) and analyzed their relationship with anemia. MUAC was used to identify chronic energy deficiency, while dietary assessment was performed using a 24-h food recall. Although this method has its limitations, it remains a practical tool in settings where more advanced dietary assessment techniques are not feasible. Therefore, the objective of this study was to identify the predictors of anemia among adolescent girls in Pekanbaru by examining their anthropometric status (MUAC and BMI) and nutrient intake, with a particular focus on protein and vitamin B12 adequacy.

Materials and methods

Study area and period

This research was conducted in Pekanbaru in August 2024 for several reasons, namely: (1) As the capital city of the Province, the population in Pekanbaru is considered to be representative of the population in Riau Province, which is one of the provinces in Indonesia; (2) the results of research in Pekanbaru can provide an overview of public health conditions in the region, which can inform the Government of Indonesia in formulating health policies. Furthermore, recognizing the scarcity of local data, especially from secondary school settings in urban areas like Pekanbaru, six public high schools were selected purposively across four sub-districts in Pekanbaru, ensuring representation from both central and peripheral urban areas. Selection was based on sociodemographic diversity and accessibility. This approach aims to provide a more comprehensive picture of the nutritional factors associated with anemia.

Study design and participants

This study is an observational analytic study with a cross-sectional design. Using primary data from the research subjects, and secondary data from the Riau Provincial Health Office, Indonesia.

The study sample size was calculated using the proportion estimation formula from Sample Size Determination in Health Studies: A Practical Manual by Lwanga and Lemeshow.¹⁸ The proportion used as a reference in calculating the minimum sample size refers to anemia screening of adolescent girls in Pekanbaru City conducted by the Riau Provincial Health Office at the end of 2023. It was found that the prevalence of anemia in adolescent girls was around 29%.¹⁹ Total 230 adolescent girls were obtained. Proportional stratified random sampling was used to determine the number of participants from each school based on student population data. Within each school, participants were selected randomly from eligible female students meeting the inclusion criteria. Sample inclusion criteria: aged 10–19 years; had a normal menstrual cycle in the last 3 months; and willing to become a research subject by signing informed consent. While the sample exclusion criteria are adolescent girls who suffer from blood disorders.

The basis for selecting respondents aged 10–19 years is according to United Nation consensus, that an individual is called an adolescent if they are between 10 and 19 years old.²⁰ In addition, according to the Regulation of the Minister of Education and Culture of the Republic of Indonesia Number 1 of 2021 concerning the Admission of New Students, it is stated that prospective new students in grade 10 of senior high school must meet the requirements of having completed 9 years with details of 6 years in elementary school and 3 years in junior high school.²¹ If the minimum age for entering elementary school is 6 years, then the age for entering senior high school is 15 years. This is in accordance with the average age of respondents in this study, which is 15 years.

Anemia status and knowledge assessment

Anemia data were obtained from hemoglobin levels through capillary blood testing using an Hb meter (Easy Touch GCHb). Easy Touch GCHb works with the digital Point of Care Testing method where the chemical reaction that occurs between blood and reagents contained in the test strip will produce a color change which is then detected by the device and converted into a numerical value indicating hemoglobin levels. Data on age, menstrual pattern, iron supplementation, and knowledge about anemia were obtained from interviews using a structured questionnaire.

In Indonesia, anemia screening in the field often uses an Hb meter with the consideration that it is easy to carry, can be done by health workers without special training, results are obtained quickly and cost. Several studies have found no significant difference between the digital Hb method using capillary blood and the cyanmethemoglobin method using venous blood,²² and the hematology analyzer.²³ Although the technology and accuracy between the Hb examination in this study and the True Hb examination are slightly different, a study comparing the diagnostic accuracy between hemoglobin color strips Hemoglobin Colour Scale (HCS). Hindustan Lifecare Limited (HLL), digital hemoglobinometers (TrueHb), and noninvasive devices (TouchHb) found that the digital method is a good diagnostic method for anemia screening.²⁴

The anemia knowledge questionnaire was validated by a clinical nutritionist and a registered dietitian. Content validity was assessed using the content validity index, and internal consistency was tested with a pilot group of 30 students, yielding a Cronbach’s alpha of 0.78, indicating acceptable reliability. The questionnaire consists of 10 questions about definition, causes, symptoms, and risk factors of anemia. The participants were given “true,” “false,” or “not sure” to response the questions. Each correct response will receive 1 point, while incorrect answers or “not sure” will receive zero points; therefore, the maximum possible score is 10. A higher score reflects a greater level of knowledge about anemia. This assessment method has also been used to measure participants’ knowledge in previous health research.²⁵ After participants were selected based on the inclusion criteria, data were collected through interviews, hemoglobin examination, anthropometric assessment, and nutritional status assessment.

Anthropometric measurement

MUAC data was obtained by measuring the upper arm circumference using a MUAC measuring tape with an accuracy of 1 mm. The measurement was taken at the midpoint between the olecranon and the acromion of the nondominant upper arm, which was bent at a 90-degree angle. To ensure accuracy, the measurement was performed twice and conducted prior to weight and height measurements to minimize bias. BMI data was obtained through measurements of body weight and height. Body weight was measured using a Seca Digital Scale (Seca, Germany) with an accuracy of 0.1 kg and a capacity of up to 150 kg. Respondents were asked to remove their footwear during weighing, and weight was recorded to the nearest 0.1 kg. Height was measured using a microtoise device with an accuracy of 0.1 cm. During measurement, the respondent stood barefoot with their back and head against the wall, eyes looking straight ahead, and heels together to ensure a straight posture. Height was recorded to the nearest 0.1 cm.

Although anthropometric measurements such as MUAC and BMI alone are not sufficient to diagnose anemia, they are important tools in a comprehensive assessment to identify individuals at risk of anemia. In Indonesia, the assessment of nutritional status in adolescent girls commonly uses MUAC and BMI as anthropometric indicators. According to the Regulation of the Minister of Health of the Republic of Indonesia Number 6 of 2024, MUAC measurement is also included in risk factor screening services for individuals of productive age, which should be conducted at least once a year.²⁶

Nutritional measurement

Food intake data were obtained from interviews using the 24-h food recall method twice to ask about the eating history of adolescent girls on weekdays and weekends (including breakfast, morning snack, lunch, afternoon snack, and dinner). Furthermore, food intake data (qualitative) were converted into food and beverage data (quantitative) using household size tools such as spoons, plates, glasses that are commonly used daily. Visual aids and portion size estimation tools (standard household measures, food photo booklets) were used to minimize recall bias. The data wereanalyzed using NutriSurvey, a tool designed to assess daily nutrient intake through methods like food records. This software calculates the type and amount of nutrients in food using a built-in database. The results were then compared to the Indonesian Recommended Dietary Allowances (RDA) as outlined in the Ministry of Health Regulation No. 28 of 2019, based on age groups.²⁷

Data analysis

Anthropometric measurements were converted into nutritional status indices based on WHO classification. Specifically, BMI-for-age Z-scores were calculated using the WHO Child Growth Standards.²⁸ The analysis classified adolescents as underweight if their BMI-for-age Z-score was less than −2 SD. Statistical analyses were conducted using SPSS version 26.0. Data analysis includes univariate, bivariate, and multivariate analysis. Bivariate analysis uses the Chi-square statistical test with a 95% confidence interval (CI). Poisson regression was used for the multivariate analysis because it fits the study’s cross-sectional design with binary outcomes (anemia or not). This method gives accurate estimates and is a good alternative for analyzing binary outcomes in cross-sectional studies. It also provides prevalence ratios (PRs), which are easier to understand and more accurate than odds ratios, especially when the condition is common.²⁹

Result

Characteristics of adolescent girls

This research found that 19.9% of adolescent girls experienced anemia, with an average hemoglobin concentration of 13.90 ± 1.99 g/dL. Most of the participants were in their mid-teen years and had experienced menarche by the age of 12. A large proportion reported having regular menstrual cycles with a normal duration and no occurrence of blood clots.

Although general awareness about anemia was relatively high, the majority of participants lacked adequate understanding of iron supplementation, held unfavorable views toward its use, and were not consistent in consuming iron tablets—particularly during menstruation.

In terms of anthropometric data, most adolescents had a normal MUAC, but their nutritional status varied, with cases of undernutrition, overweight, and obesity still observed. Alarmingly, all participants failed to achieve the recommended daily intake for both macro- and micronutrients. The most concerning deficiencies were seen in iron, vitamin B12, and folic acid—nutrients that are critical in preventing anemia.

Table 1 provides a comprehensive summary of these essential characteristics, covering sociodemographic information, parental background, economic status, clinical measurements (including MUAC and BMI), and nutrient adequacy. This table serves as a key reference for interpreting the broader context and identifying the complex contributors to anemia in this adolescent population.

Table 1.

Characteristics of adolescents girls.

Variable	Parameter	Number (n)	%
A. Sociodemographic variable
Age (years)	14	17	7.40
	15	170	73.90
	16	40	17.40
	17	3	1.30
Fathers’ education	Low (elementary-senior high)	14	6.10
Fathers’ education	High (diploma-doctoral)	216	93.90
Mothers’ education	Low (elementary-senior high)	12	5.20
Mothers’ education	High (diploma-doctoral)	218	94.80
Fathers’ occupation	Unemployed	11	4.80
Fathers’ occupation	Employed	219	95.20
Mothers’ occupation	Unemployed	108	47.00
Mothers’ occupation	Employed	122	53.00
Family income	<Minimum wage	71	30.90
Family income	⩾Minimum wage	159	69.10
B. Clinical characteristics
Mid-upper arm circumference	<22 cm (protein-energy malnutrition)	50	21.70
Mid-upper arm circumference	⩾22 cm (normal)	180	78.30
Nutritional status	BMI < 17 (very thin)	26	11.3
	BMI 17–<18.5 (thin)	45	19.60
	BMI 18.5–25.0 (normal)	127	55.20
	BMI >25–27 (overweight)	11	4.80
	BMI > 27 (obese)	21	9.10

Bivariate test results of predictors with the incidence of anemia in adolescent girls

Table 2 presents the results of the Chi-square analysis, including PR values and 95% CI, to estimate the strength of association between each predictor and anemia incidence. A p-value of <0.05 was considered statistically significant, and variables approaching this threshold are highlighted for further consideration in multivariate analysis. The bivariate analysis revealed no statistically significant associations between anemia and menstrual patterns, knowledge, attitude, or adherence to iron supplementation, knowledge about anemia, nutritional status, MUAC, and most food intake variables. However, an interesting finding emerged regarding vitamin A intake: adolescents who met the RDA for vitamin A had a higher prevalence of anemia (21.5%) compared to those who did not (17.1%), although the relationship was not statistically significant.

Table 2.

Bivariate analysis of predictor factors with anemia incidence in adolescent girls.

Risk factor	Anemia		Non anemia		p-Value*	Prevalence ratio (95% CI)
Risk factor	n	%	n	%	p-Value*	Prevalence ratio (95% CI)
Menstrual pattern
Menstrual frequency
>Once per month	1	8.3	11	91.7	0.471	0.422 (0.063–2.81)
Once per month	43	19.7	175	80.3		Reference
Menstrual cycle
<21	14	17.1	68	82.9	0.583	Reference
21–35 days	29	21	109	79		0.812 (0.457–1.446)
>35 days	1	10	9	90		1.707 (0.2501–1.641)
Duration of menstruation each month
>7 days	10	20.8	38	79.2	0.896	1.115 (0.59–2.09)
⩽7 days	34	18.7	148	81.3		Reference
Blood clots during menstruation
Yes	19	18.3	85	81.7	0.894	0.921 (0.538–1.57)
No	25	19.8	101	80.2		Reference
Iron supplementations
Knowledge
Poor	32	18	146	82	0.534	0.779 (0.433–1.401)
Good	12	23.1	40	76.9		Reference
Attitudes toward iron supplement tablets
Negative	43	19.5	178	80.5	0.848	1.750 (0.271–11.331)
Positive	1	11.1	8	88,9		Reference
Adherence to iron supplement tablet consumption
No	38	21.2	141	78.8	0.189	1.800 (0.809–4.027)
Yes	6	11.8	45	88.2		Reference
Consumption of iron supplement tablets during menstruation
No	43	20.9	163	79.1	0.050	5.010 (0.72–34.75)
Yes	1	4.2	23	95.8		Reference
Knowledge about anemia
Poor	5	13.2	33	86.8	0.424	0.648 (0.273–1.536)
Good	39	20.3	153	79.7
Nutritional status
Very thin	3	11.5	23	88.5	0.733	Reference
Thin	7	15.6	38	84.4		0.742 (0.21–2.624)
Normal	27	21.3	100	78.7		0.543 (0.178–1.656)
Overweight	2	18.2	9	81.8		0.635 (0.123–3.286)
Obesity	5	23.8	16	76.2		0.485 (0.131–1.797)
Mid-upper arm circumference
Protein-energy malnutrition	12	24	38	76	0.432	1.350 (0.752–2.423)
Normal	32	17.8	148	82.2		Reference

Significant at p < 0.05 (Chi-square test).

Moreover, adolescent girls with insufficient intake of carbohydrates, proteins, fats, vitamin C, zinc, and copper demonstrated a higher risk of anemia, as reflected by a PR greater than 1. These findings suggest a trend where deficiencies in multiple macronutrients and micronutrients may contribute to anemia risk, although statistical significance was not achieved.

Multivariate test results of anemia incidence in adolescent girls

Table 3 presents the results of the multivariate Poisson regression analysis, including adjusted PRs, 95% CI, and p-values. Statistical significance was set at p < 0.05. Multivariate analysis using Poisson regression identified three independent factors significantly associated with anemia incidence among adolescent girls: MUAC < 22 cm, underweight nutritional status, and insufficient vitamin B12 intake. Among these, low MUAC emerged as the strongest predictor, with an adjusted prevalence ratio (aPR) of 1.951. This indicates that adolescent girls with MUAC below 22 cm had a 1.95 times higher risk of developing anemia compared to those with normal MUAC, after controlling for other variables.

Table 3.

Multivariate analysis of anemia incidence in adolescent girls.

Predictor	p-Value	Adjusted prevalence ratio	95% CI
Mid-upper arm circumference (<22 cm)	0.033*	1.951	1.05–3.60
Nutritional status (underweight)	0.038*	0.506	0.26–0.96
Vitamin B12 intake (insufficient)	0.041*	0.558	0.31–0.97

Significant at p < 0.05 (Poisson regression).

Underweight nutritional status was also associated with a lower risk of anemia (aPR: 0.506), which may reflect complex interactions between BMI, inflammation, and micronutrient metabolism. Additionally, insufficient vitamin B12 intake was associated with a significantly increased risk of anemia (aPR: 0.558), highlighting the importance of adequate micronutrient intake beyond just iron.

Discussion

Upper arm circumference with anemia in adolescent girls

This study found a significant association between MUAC and the incidence of anemia among adolescent girls. Most of the adolescent girls involved in this study had normal MUAC (78.30%). MUAC data were obtained from upper arm measurements using an upper arm circumference measuring tape which is standardized by the Ministry of Health of the Republic of Indonesia.³⁰ While bivariate analysis did not show statistical significance, multivariate analysis confirmed that adolescents with MUAC < 22 cm were nearly twice as likely to be anemic. This supports previous research indicating that low MUAC is a proxy for chronic energy deficiency, which in turn impairs the absorption and metabolism of key nutrients involved in erythropoiesis, particularly iron and protein.^31,32

Currently, there are no cutoff points of MUAC that are universally applicable for all adolescents due to the different characteristics of adolescents in each country and the lack of research and data on upper arm circumference measurement in adolescents.³¹ This can be seen in the cut off points of MUAC for underweight status in adolescent girls in Sudan < 22.5 cm³³; in Indonesia 19.7 cm³²; and in India between 17.5 and 20.9 cm.³⁴ While the cutoff points of MUAC for overnutrition status in Ethiopia were 27.9 cm³⁵; in India 23.3 cm³⁶; in Indonesia 27.1 cm³²; and in Nigeria ranged from 24.8 to 27.8 cm.³⁷ The cutoff used in this study (22 cm) was selected based on national guidelines, but the heterogeneity of adolescent physiology suggests a need for age-specific references.³¹

Measurement of upper arm circumference is one way of anthropometric measurement to assess nutritional status, and chronic energy deficiency. It can be performed on children, pregnant women, and women of childbearing age.³⁸ The advantages of the measurement are that it is noninvasive, simple, inexpensive, easy to perform, and effective for assessing nutritional status if done with precision. Studies show that upper arm circumference measurement has comparable accuracy with BMI to identify undernutrition,^32,33,39 good nutrition in adolescent girls aged 15–19 years with 100% sensitivity and 88.2% specificity,³⁵ and overnutrition (obesity).⁴⁰ Further, analysis of basic health research data in 2018 also found an association between less upper arm circumference and the incidence of anemia in nonpregnant women of reproductive age in Indonesia.⁴¹

Less upper arm circumference indicates that a person has chronic energy deficiency for a long time which can be caused by a lack of food intake or impaired absorption of macronutrients (protein) and micronutrients (iron, folic acid, and vitamin B12). From the results of food intake interviews in this study, it was found that more than 50% of adolescent girls were deficient in protein intake. While iron transportation to the bone marrow requires protein in the form of transferrin.⁴² Protein in the form of ferritin also plays an important role in iron homeostasis. Ferritin functions as an iron reserve by oxidizing excess Fe(II) to Fe(III). This process is important to prevent oxidative damage caused by excess Fe(II). This makes ferritin known as cytoprotective against intracellular oxidative damage. When the body is deficient in iron, ferritin will release iron reserves to be reused.^43,44

Nutritional status with anemia in adolescent girls

Additionally, the role of BMI in anemia was more nuanced. Although no significant association was found in bivariate analysis, multivariate analysis revealed that thin adolescents were at lower risk of anemia than those with normal BMI. This counterintuitive finding diverges from much of the literature, which generally associates low BMI with increased anemia risk due to poor dietary intake.⁴⁵ One possible explanation is that thin adolescents in this study may engage in more physical activity, increasing muscle mass and improving iron metabolism and absorption. These findings align with previous studies in Azerbaijan,⁶ Egypt,⁴⁶ Bangladesh,⁴⁷ and Indonesia.⁴¹ Highlighting the multifactorial nature of anemia that is not solely dictated by BMI status.

Conversely, higher rates of anemia were observed among obese adolescents. Obesity may contribute to functional iron deficiency due to chronic inflammation. Pro-inflammatory cytokines, particularly IL-6, stimulate hepcidin synthesis, which inhibits both iron absorption in the gut and its release from macrophages.⁴⁸ This mechanism, known as anemia of chronic inflammation, could explain the higher prevalence of anemia in overweight adolescents despite their higher nutrient intake.⁴⁹

Vitamin B12 adequacy and anemia in adolescent girls

Vitamin B12 deficiency was common in this study, with only 17% of adolescents achieving adequate intake. Despite this, paradoxically, adolescents with adequate intake showed higher anemia prevalence than those with inadequate intake. This result contradicts the well-established role of vitamin B12 in red blood cell formation and DNA synthesis other factors that may affect vitamin B12 adequacy are consumption of processed foods, and digestive system disorders.⁵⁰

Several hypotheses may explain this anomaly. First, vitamin B12 adequacy based on dietary recall may not reflect true physiological status due to potential malabsorption. Gut microbiota composition plays a significant role in the synthesis and bioavailability of vitamin B12. Overgrowth of B12-consuming bacteria or impaired gastrointestinal function may limit absorption despite sufficient intake.^51
–53 Second, the body’s ability to store B12 in the liver may mask early deficiency symptoms. Serum B12 levels decline gradually and clinical manifestations may only appear after prolonged deficiency.⁵⁴ Third, different types of anemia (e.g., megaloblastic vs iron-deficiency anemia) may be present, but our study only assessed hemoglobin levels without further classification. This limits the ability to differentiate whether anemia was caused by B12 deficiency or other etiologies such as iron or folate deficiency. Nonetheless, studies have consistently shown that increasing serum B12 levels correlates with decreased anemia risk.^55,56

According to the Regulation of the Minister of Health of the Republic of Indonesia Number 28 of 2019 concerning Recommended Nutrient Intake for Indonesians, the RDA of vitamin B12 for women aged 10–19 years is 3.5–4.0 µg per day.²⁷ Efforts to treat vitamin B12 deficiency depend on the indication. If it is due to dietary intake that lacks B12 or vegetarians and vegans, it is recommended to take B12 supplements containing cyanocobalamin 20–50 µg or 50–150 µg once a day.⁴⁷ Indonesia has also conducted food fortification, but it is still limited to iron fortification in wheat flour, vitamin A fortification in cooking oil and iodine fortification in salt.⁵⁷ WHO recommends B12 supplementation when deficiency has become a public health problem.⁵⁸ Some studies have shown a decrease in the prevalence of B12 deficiency and an increase in B12 concentrations after food interventions (nutrient bars and yogurt)⁵⁹ or micronutrient-enriched milk-based drinks.⁶⁰ Given these complexities, dietary adequacy alone is insufficient to determine B12 status. Serum biomarkers (e.g., serum B12, homocysteine, and methylmalonic acid) would provide a clearer picture, yet these were not feasible in this study due to resource constraints.

Strengths and limitations

The findings of this study have several important clinical and public health implications. First, the significant association between MUAC and anemia suggests that MUAC can be utilized as a simple, low-cost screening tool in schools and primary health care settings to identify adolescents at risk of chronic energy deficiency and related anemia. Second, the increased risk of anemia among overweight adolescents highlights the need for clinicians to consider inflammation-induced functional iron deficiency, even among those with adequate or excess caloric intake. Third, the paradoxical relationship between vitamin B12 adequacy and anemia underscores the limitations of relying solely on dietary recall to assess micronutrient status. This points to the need for more comprehensive diagnostic approaches, including serum biomarkers, to accurately evaluate B12 deficiency and guide appropriate interventions. Collectively, these insights support the integration of broader nutritional assessments and targeted education into adolescent health programs, along with consideration for food fortification and micronutrient supplementation policies.

However, several limitations must be acknowledged. First, the study employed a cross-sectional design, which restricts causal inference. Second, we only measured hemoglobin levels, without further analysis of iron status (e.g., serum ferritin, transferrin saturation), B12 levels, or red cell indices to differentiate anemia types. Third, dietary intake was assessed via 24-h recall, which may not capture habitual intake and is subject to recall bias.

We attempted to mitigate these limitations by using validated questionnaires, standard anthropometric protocols, and by sampling from multiple schools representing different sub-districts in Pekanbaru to improve generalizability. Although thalassemia was not ruled out through laboratory testing, its exclusion was not feasible due to cost and accessibility constraints; epidemiologically, thalassemia prevalence in this region remains low.

The findings highlight the multifactorial etiology of anemia among adolescent girls, underscoring the need for integrated strategies. Interventions should not focus solely on iron but also consider protein, vitamin B12, and chronic inflammation. Schools can play a critical role by enhancing nutrition education and monitoring adolescent growth through regular anthropometric screenings. Primary health centers should strengthen community outreach and early detection, while government policies must support micronutrient supplementation and food fortification. Given the unclear relationship between B12 intake and anemia in this population, further studies incorporating serum biomarkers and examining gut health are warranted. Experimental designs could also help evaluate the effects of B12 supplementation and fortified foods.

Conclusion

Anemia among adolescent girls in Pekanbaru was significantly associated with low MUAC (<22 cm), nutritional status (underweight), and vitamin B12 sufficiency. Adolescent girls with MUAC < 22 cm had a 1.95 times higher risk of anemia compared to those with normal MUAC.

Supplemental Material

sj-docx-1-smo-10.1177_20503121251355406 – Supplemental material for Exploring the role of nutritional status and anthropometric factors in anemia among adolescent girls in Pekanbaru, Indonesia

Supplemental material, sj-docx-1-smo-10.1177_20503121251355406 for Exploring the role of nutritional status and anthropometric factors in anemia among adolescent girls in Pekanbaru, Indonesia by Fachriani Putri, Suyanto Suyanto, Ridha Restila, Agung Dwi Laksono and Tonny Sundjaya in SAGE Open Medicine

Footnotes

Acknowledgements

This article is an output of research approved and funded by the Faculty of Medicine, University of Riau and publication by Nutricia. For that the Authors would like to thank the Faculty of Medicine, University of Riau and Sari Husada. The Authors would also like to thank all the adolescent girls who have participated and their schools that have allowed their students. We gratefully acknowledge the Varians Statistik Kesehatan for providing the bootcamp and mentoring to improve the manuscript writing.

ORCID iD

Suyanto Suyanto

Ethical considerations

The study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Review Board (or Ethics Committee) of the Faculty of Medicine, University of Riau, under approval number B/081/UN19.5.1.1.8/UEPKK/2024.

Consent to participate

After obtaining approval from the Ethics Committee of the Faculty of Medicine, University of Riau, the researcher secured the necessary permits from the Education Office and informed the participating schools. The researcher then explained the study’s purpose and significance to both the adolescent participants and their homeroom teachers, who acted as official representatives of the students. Once the participants fully understood their role and the study objectives, written informed consent was obtained from all participants before data collection. As the study involved minors, additional written consent was also obtained from their legally authorized representatives (parents or guardians).

Author contributions

All authors contributed to the conception and design of study. FP contributed to the conception, drafting, and revised the manuscript for important intellectual content. SS contributed to the conception and language editing of the manuscript. RR contributed to data collection, statistical analyses, and interpretation of data. ADL and TS contributed to review the manuscript. All authors read and approved the final manuscript and agreed to be accountable for all aspects of the work.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was funded by Faculty of Medicine, University of Riau, grant number 34/UN19.5.1.1.8/AL.04/UPPM/2024. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Supplemental material

Supplemental material for this article is available online.

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Ministry of Health of the Republic of Indonesia. Indonesian Health Profile 2021. Pusdatin. Jakarta: Ministry of Health of the Republic of Indonesia, 2022.

WHO. Guideline: daily iron supplementation in adult women and adolescent girls. Geneva, Switzerland: World Health Organization, 2016.

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Exploring the role of nutritional status and anthropometric factors in anemia among adolescent girls in Pekanbaru,Indonesia