European Journal of Cardiovascular Medicine

Severe acute malnutrition (SAM) remains one of the leading causes of morbidity and mortality among children under five years of age and continues to pose a major public health challenge worldwide (1). It is particularly prevalent in low- and middle-income countries, where poverty, food insecurity, inadequate maternal nutrition, poor infant feeding practices, and recurrent infections collectively contribute to high rates of childhood malnutrition (2). SAM is clinically characterized by severe wasting (weight-for-height < −3 SD), nutritional edema, and critically reduced mid-upper-arm circumference, all of which reflect severe nutritional deprivation and profound alterations in metabolic, biochemical, and physiological processes (3). Children suffering from SAM have markedly compromised immunity, which increases their susceptibility to respiratory infections, diarrheal diseases, sepsis, and other life-threatening complications. Furthermore, SAM adversely affects growth trajectories, cognitive development, and overall survival (4).

Micronutrient deficiencies lie at the core of the pathophysiological changes observed in SAM. Chronic inadequate dietary intake, gastrointestinal infections, intestinal mucosal atrophy, impaired absorption, and increased physiological requirements make children with SAM particularly vulnerable to deficits in multiple micronutrients (5). Among the various micronutrients essential for optimal growth and development, Vitamin B12, folate, and iron play especially critical roles due to their involvement in DNA synthesis, red blood cell maturation, neural development, and immune function (6). Deficiency of any of these nutrients can lead to anemia, growth failure, neurological impairment, and increased susceptibility to infections—consequences that become even more severe when multiple deficiencies coexist, as is commonly observed in SAM.

Vitamin B12 (cobalamin) is a water-soluble vitamin indispensable for nucleic acid synthesis, methylation reactions, and myelin formation. Its absorption requires intrinsic factor produced by gastric parietal cells and an intact ileum, making young children highly vulnerable to deficiency in conditions such as chronic diarrhea, intestinal parasitic infestations, or mucosal injury secondary to malnutrition (7). Dietary inadequacy contributed by predominantly vegetarian feeding patterns, delayed introduction of animal-source foods, and poor complementary feeding practices further increases the risk of Vitamin B12 deficiency among infants and young children. In malnourished children, compromised absorption and repeated infections exacerbate this deficiency several-fold. Clinically, Vitamin B12 deficiency manifests as megaloblastic anemia, neurodevelopmental delay, hypotonia, irritability, lethargy, impaired cognitive function, and compromised immune response—all of which intensify the vulnerability of children suffering from SAM (8). The neurological consequences of Vitamin B12 deficiency, in particular, may be long-lasting and may not be fully reversible even after nutritional rehabilitation.

Folate, another crucial micronutrient, plays a vital role in one-carbon metabolism, DNA and RNA synthesis, and rapid cell turnover. It is essential for normal erythropoiesis, neural tube development, and overall growth. Folate deficiency is widespread among children with SAM because of low dietary intake, increased metabolic demand during infections, and rapid depletion of body stores. Folate deficiency results in megaloblastic anemia, mucosal atrophy, failure to thrive, and delayed cognitive development (3). Since folate and Vitamin B12 deficiencies produce overlapping hematologic manifestations, distinguishing between the two is important for appropriate management.

Iron is a fundamental micronutrient required for hemoglobin synthesis, oxygen transport, immune function, and cellular metabolism. Iron deficiency remains the most common micronutrient deficiency in the world and is a major contributor to anemia in infants and young children. In SAM, iron metabolism becomes significantly altered. Chronic inflammation associated with malnutrition increases hepcidin levels, reducing intestinal iron absorption. Additionally, inadequate intake of iron-rich foods, poor complementary feeding practice, and frequent infections further exacerbate iron deficiency (5). Consequently, iron deficiency anemia becomes highly prevalent and contributes to lethargy, decreased immunity, poor cognitive function, and delayed psychomotor development.

A hospital-based, cross-sectional analytical study was conducted in the Department of Pediatrics, Hassan institute of medical sciences, Hassan on children aged 6 months to 59 months who were admitted with a diagnosis of severe acute malnutrition based on WHO criteria. The study was conducted over a period of 2 Years. Ethical clearance was obtained from the Institutional Ethics Committee (IEC) prior to commencement of the study. Written informed consent was obtained from parents or guardians after explaining the purpose, procedure, and confidentiality of the study. All the participants were selected according to the exclusion and inclusion criteria. The sample size was 100 participants.

Inclusion Criteria Weight-for-height/length < −3 SD of the WHO growth standards, mid-upper arm circumference (MUAC) < 11.5 cm, presence of bilateral pitting edema of nutritional origin, age between 6 months and 59 months, admission to the Pediatric ward or NRC during the study period. parental or guardian consent for participation. A purposive sampling technique was used to allocate the participants.

Exclusion Criteria

Known hematological disorders (e.g., Hemolytic anemia, aplastic anemia, malignancy, acute blood loss), age less than 6 months and more than 60 months, refusal of consent by parents or caregivers.

Sample Size      

The sample size was calculated based on the expected prevalence of Vitamin B12 deficiency in children with SAM reported in previous studies.

Thus, a minimum of 92 children were required. To compensate for potential sample loss, hemolyzed samples, or incomplete laboratory results, the sample size was increased to 100 participants.

STUDY PROCEDURE

Step 1: Screening and Enrollment

All children admitted to the Pediatric ward or NRC were screened using WHO SAM diagnostic criteria. Eligible children were enrolled after obtaining written informed consent from parents or caregivers.

Step 2: Clinical Assessment

A thorough clinical evaluation was performed, including:

Detailed medical history

Dietary and breastfeeding assessment: Infant and young child feeding (IYCF) practices were assessed using a 24 hrs. dietary recall method. Minimum dietary diversity (MDD) was evaluated based on WHO guidelines using 8 groups (table1), each food group consumed was assigned a score of 1, and nonconsumption was scored as 0.A total score of more than equal to 5 was considered adequate dietary diversity.

MDD-IYCF Food Groups.

Examination for edema, dehydration, hepatomegaly, signs of micronutrient deficiencies

Clinical stabilization (management of hypoglycemia, hypothermia, dehydration, and infections) was ensured prior to sample collection to avoid biochemical distortions.

Step 3: Anthropometric Measurements

Anthropometric assessments were performed according to WHO protocols:

Weight measured with digital pediatric scale

Length/height using an infantometer or stadiometer

MUAC using non-stretchable tape
All measurements were taken in triplicate, and mean values were used for analysis.

Step 4: Blood Sample Collection

A 3–5 ml venous blood sample was collected under aseptic precautions. Samples were processed as follows:

Serum separated within 1 hour using centrifugation

Vitamin B12 and folate measured using spectrophotometric method.

Serum iron assessed using colorimetric or immunoturbidimetric methods

Complete blood count analyzed using automated hematology analyzer

Quality control protocols were strictly followed to ensure accuracy.

Descriptive Statistics of Age of SAM Children (n = 100)

Children with SAM were aged 5–59 months. The mean age was 36.54 ± 15.68 months, indicating that the study included children across the under-five age group with substantial variability in age distribution.

Table 2. Descriptive Statistics of Age of SAM Children

Gender Distribution of Study Participants (n = 100)

Among the 100 SAM children, 52% (n = 52) were female and 48% (n = 48) were male. This reflects an almost equal gender distribution, with a slight predominance of female participants.

Table 3. Gender Distribution of Study Participants

Distribution of Residence Among SAM Children (n = 100)

Residence distribution showed that 53% (n = 53) of SAM children belonged to urban areas, while 47% (n = 47) were from rural areas. This demonstrates a marginally higher representation of urban cases in the study sample.

Table 4. Distribution of RESIDENCE Among SAM Children

Distribution of Socioeconomic Status Among SAM Children (n = 100)

Socioeconomic status assessment revealed that 54% (n = 54) of SAM children were from the lower class, 33% (n = 33) from the lower-middle class, and 13% (n = 13) from the middle class. The distribution indicates predominance of lower socioeconomic strata.

Table 5. Distribution of Socioeconomic Status Among SAM Children

Educational Status of Mothers of SAM Children (n = 100)

Maternal education status showed 30% (n = 30) were illiterate, 32% (n = 32) had primary education, 21% (n = 21) had secondary education, and 17% (n = 17) had higher secondary or above. Most mothers had education at primary level or below.

Table 6. Educational Status of Mothers of SAM Children

Distribution of Family Type Among Study Participants (n = 100)

Family type distribution indicated that 69% (n = 69) of SAM children belonged to nuclear families, whereas 31% (n = 31) belonged to joint families. This shows a higher proportion of nuclear family settings among the study participants.

Table 7. Distribution of Family Type Among Study Participants

Birth Order Distribution of SAM Children (n = 100)

Birth order distribution showed that 36% (n = 36) were first-born, 35% (n = 35) were second-born, 22% (n = 22) were third-born, and 7% (n = 7) were fourth-born children. The distribution indicates that the largest proportions were in birth orders one and two.

Table 8. Birth Order Distribution of SAM Children

Descriptive Statistics of Infant Feeding Practices (n = 100)

Exclusive breastfeeding (EBF) duration ranged from 0 to 6 months, with a mean of 3.19 ± 2.15 months. Complementary feeding initiation ranged from 4 to 8 months, with a mean start age of 5.96 ± 1.00 months, indicating variability in breastfeeding duration and complementary feeding timing.

Table 9. Descriptive Statistics of Infant Feeding Practices

Distribution of Food Taboos Among SAM Children (n = 100)

Food taboos were reported in 38% of participants, while 62% (n = 62) reported no taboos. Specific taboos included avoiding fruits during illness 11% (n = 11), avoiding meat 10% (n = 10), avoiding eggs 9% (n = 9), and avoiding certain cereals 8% (n = 8).

Table 10. Distribution of Food Taboos Among SAM Children

Bottle Feeding Practices Among Study Participants (n = 100)

Bottle feeding was practiced by 54% (n = 54) of SAM children, whereas 46% (n = 46) did not receive bottle feeding. The distribution demonstrates that bottle feeding was slightly more common than non–bottle feeding in the study sample.

 Table 11. Bottle Feeding Practices Among Study Participants

Distribution of Meal Frequency Among SAM Children (n = 100)

Meal frequency assessment showed 69% (n = 69) received 3 meals/day, 22% (n = 22) received >3 meals/day,9 % (n = 9) received 1-2meals/day. Most participants were clustered in the three-meal category.

Table 12. Distribution of Meal Frequency Among SAM Children

 Distribution of Minimum Dietary Diversity Score Among Study Participants (n = 100)

Minimum dietary diversity scores showed 36% (n = 36) had 2/8, 15% (n = 15) had 3/8, 20% (n = 20) had 4/8, and 29% (n = 29) had 5/8. The distribution indicates that the majority had diversity scores of 2/8 .

Table 13. Distribution of Minimum Dietary Diversity Score Among Study Participants

Adequacy of Complementary Feeding Among SAM Children (n = 100)

Complementary feeding adequacy assessment showed 71% (n = 71) had inadequate complementary feeding, whereas 29% (n = 29) had adequate complementary feeding. This distribution indicates that inadequate complementary feeding predominated among participants.

Table 14. Adequacy of Complementary Feeding Among SAM Children

Distribution of Weight-for-Height in SAM Children (n = 100)

All participants (100%, n = 100) had weight-for-height below −3 SD, confirming that the entire study sample met the anthropometric criterion for severe acute malnutrition based on WFH classification.

Table 15. Distribution of WFH in SAM Children

Descriptive Statistics of Hematological and Biochemical Parameters in SAM Children (n = 100)

Hemoglobin ranged 3.50–13.30 g/dL with a mean 9.42 ± 1.76 g/dL, and RBC ranged 0.96–5.73 million/mm³ with mean 3.86 ± 0.82. Indices showed MCV 78.40 ± 21.90 fL (8.30–119.40), MCH 24.21 ± 3.88 pg (12.90–36.30), and MCHC 31.87 ± 1.97% (25.20–42.20). Biochemical parameters showed serum vitamin B12 246.45 ± 108.29 ng/L (50–565), serum iron 49.97 ± 31.76 µg/dL (9.40–309), and serum folic acid 7.52 ± 4.51 µg/L (1–25).

Table 16. Descriptive Statistics of Hematological and Biochemical Parameters in SAM Children

Table. Distribution of Hematological and Biochemical Parameters among SAM Children (n = 100)

Peripheral Smear Findings Among SAM Children (n = 100)

Peripheral smear findings showed microcytic hypochromic anemia in 51% (n = 51), dimorphic pattern in 25% (n = 25), macrocytic (megaloblastic) picture in 15% (n = 15), and normocytic normochromic picture in 9% (n = 9), indicating four distinct smear patterns within the cohort.

Table 17. Peripheral Smear Findings Among SAM Children

Correlation Between Anthropometric Parameters and Hemoglobin & RBC Levels (n = 100)

Height showed a positive correlation with hemoglobin (r = 0.145, p = 0.150) and a negative correlation with RBC (r = −0.086, p = 0.398). Weight showed a positive correlation with hemoglobin (r = 0.153, p = 0.129) and a negative correlation with RBC (r = −0.165, p = 0.100). MUAC showed minimal correlations with hemoglobin (r = 0.018, p = 0.856) and RBC (r = 0.078, p = 0.438).

Table 18. Correlation Between Anthropometric Parameters and Hemoglobin & RBC Levels

Correlation Between Anthropometric Parameters and Red Blood Cell Indices (MCV, MCH, MCHC) (n = 100)

Height correlated with MCV (r = 0.206, p = 0.039)* and MCH (r = 0.225, p = 0.025)**, while correlation with MCHC was r = 0.054 (p = 0.596). Weight correlated with MCV (r = 0.395, p < 0.001) and *MCH (r = 0.286, p = 0.004)**, with MCHC r = 0.081 (p = 0.424). MUAC correlations were MCV r = −0.068 (p = 0.504), MCH r = 0.038 (p = 0.708), and MCHC r = −0.033 (p = 0.741).

Table 19. Correlation Between Anthropometric Parameters and Red Blood Cell Indices (MCV, MCH, MCHC)

Correlation Between Anthropometric Parameters and Serum Micronutrient Levels (Vitamin B12, Iron and Folic Acid) (n = 100)

Height showed weak correlations with serum vitamin B12 (r = 0.065, p = 0.521), iron (r = −0.072, p = 0.475), and folic acid (r = −0.104, p = 0.301). Weight correlated negatively with folic acid (r = −0.266, p = 0.007)* and showed weak correlations with vitamin B12 (r = −0.025, p = 0.807) and iron (r = −0.149, p = 0.139). MUAC correlated positively with folic acid *(r = 0.338, p < 0.001)**, with weak correlations for vitamin B12 (r = 0.153, p = 0.128) and iron (r = 0.068, p = 0.503).

Table 20. Correlation Between Anthropometric Parameters and Serum Micronutrient Levels (Vitamin B12, Iron and Folic Acid)

*Indicates Significance (p-value<0.05)

TABLES

Table 1. Descriptive Statistics of Age of SAM Children

Table 2. Gender Distribution of Study Participants

Table 3. Distribution of RESIDENCE Among SAM Children

Table 4. Distribution of Socioeconomic Status Among SAM Children

Table 5. Educational Status of Mothers of SAM Children

Table 6. Distribution of Family Type Among Study Participants

Table 7. Birth Order Distribution of SAM Children

Table 8. Descriptive Statistics of Infant Feeding Practices

Table 9. Distribution of Food Taboos Among SAM Children

Table 10. Bottle Feeding Practices Among Study Participants

Table 11. Distribution of Minimum Dietary Diversity Score Among Study Participants

Table 12. Adequacy of Complementary Feeding Among SAM Children

Table 13. Distribution of WFH in SAM Children

Table 14. Descriptive Statistics of Hematological and Biochemical Parameters in SAM Children

Table. 15 Distribution of Hematological and Biochemical Parameters among SAM Children

(n = 100)

Table 16. Peripheral Smear Findings Among SAM Children

 Table 17. Correlation Between Anthropometric Parameters and Hemoglobin & RBC Levels

 Table 18. Correlation Between Anthropometric Parameters and Red Blood Cell Indices (MCV, MCH, MCHC)

 Table 19. Correlation Between Anthropometric Parameters and Serum Micronutrient Levels (Vitamin B12, Iron and Folic Acid)

The present study highlights the high burden of anaemia among children with severe acute malnutrition (SAM), with anaemia observed in 78% of participants. Similar findings were reported by Yaikhomba et al., Venigalla et al., and Verma et al., who also documented a high prevalence of anaemia among children with SAM [10,13,14]. These findings emphasize the importance of comprehensive haematological evaluation in addition to anthropometric assessment.

The haematological profile demonstrated considerable variation in red blood cell indices, with microcytic hypochromic anaemia being the predominant peripheral smear pattern (51%), followed by dimorphic anaemia (25%) and macrocytic/megaloblastic anaemia (15%). The coexistence of these patterns suggests that anaemia in SAM is multifactorial and cannot be attributed to iron deficiency alone. Similar mixed haematological patterns associated with multiple micronutrient deficiencies have been reported by Shukla et al. and Venigalla et al. [12,13].

Micronutrient assessment revealed low serum iron levels in 81% of children, while 37% had low vitamin B12 levels and 27% had borderline vitamin B12 levels. Although folate deficiency was observed in 9%, another 34% had borderline levels. These findings indicate that iron deficiency and suboptimal folate and vitamin B12 status may contribute to the haematological abnormalities observed in SAM. Previous studies have similarly highlighted the importance of vitamin B12, iron, and folate deficiencies in the development of anaemia among malnourished children [10,12–14]. Therefore, evaluation of micronutrient status is essential for identifying deficiencies that may otherwise remain undetected.

Furthermore, anthropometric parameters did not consistently correlate with haemoglobin, RBC count, or serum vitamin B12 and iron levels. This suggests that the severity of wasting alone may not reliably indicate the presence or extent of anaemia and micronutrient deficiencies. Similar observations have been reported by Shukla et al. and Yaikhomba et al. [10,12]. These findings support the combined use of anthropometric measurements, haemoglobin estimation, RBC indices, peripheral smear examination, and biochemical assessment for comprehensive evaluation of children with SAM.

The high prevalence of inadequate complementary feeding (71%), low dietary diversity, and suboptimal feeding practices further emphasizes the importance of addressing modifiable nutritional factors. Previous studies have highlighted the role of inappropriate breastfeeding and complementary feeding practices, dietary inadequacy, and caregiver education in the prevention and management of SAM [9,11,13]. Accordingly, nutritional rehabilitation should incorporate caregiver counselling, adequate feeding frequency, dietary diversification, appropriate complementary feeding, and correction of harmful food taboos. Systematic identification and correction of micronutrient deficiencies, together with appropriate follow-up, may help improve nutritional recovery and reduce persistent nutritional risk.

Overall, the findings emphasize that anaemia in children with SAM requires a multifactorial approach, integrating comprehensive haematological and biochemical assessment with nutritional rehabilitation and improvement of feeding practices.

The results support the clinical importance of evaluating anemia beyond hemoglobin alone by incorporating RBC indices and peripheral smear, and emphasize that micronutrient disturbances—particularly involving iron and folate with possible vitamin B12 depletion in subsets—are relevant co-morbidities in SAM that may not be reliably inferred from anthropometry alone; accordingly, comprehensive nutritional rehabilitation should integrate feeding practice correction (adequate frequency and diversity, appropriate complementary feeding, avoidance of harmful taboos, reduction of bottle feeding) and systematic identification and correction of micronutrient deficiencies to improve recovery and reduce persistent nutritional risk in under-five children with severe wasting.

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