Association between anthropometric criteria and body composition among children aged 6–59 months with severe acute malnutrition: a cross-sectional assessment from India | BMC Nutrition

Children aged between 6 and 59 months with uncomplicated SAM using WHZ < -3 criteria included in this analysis are part of a larger multicentric, controlled study [20]. Children from the four intervention Indian states were recruited in the study soon after their enrolment in the community-based SAM management program while children from the control area were recruited from one Indian state where there is no community-based SAM management program. All the study participants were recruited during the year 2021 after obtaining written informed consent from their parents/guardians. Participation of children was voluntary and children could withdraw at any time without further obligations. Each child was assigned a unique identification number to ensure confidentiality.

Power calculations for the overall study indicated that a total sample of 83 children was needed per study state (expected cure rate of 50%, effect size of 25%, alpha error probability = 0.05, power = 0.80 and design effect = 1.5). Assuming an overall dropout-rate of 20%, the minimum sample size was determined to be 100 children per study state. In total, 659 children were recruited in the study during the baseline round. We achieved the minimum sample size of 100 in each study state.

During the baseline, anthropometric measurements (weight, height/length, mid-upper arm circumference) were taken alongside other socio-demographic information such as age, sex, mother’s age, mother’s years of education, caste, possession of different assets by the households and history of illness during the preceding 15 days on the day of survey. Body composition estimates for FM and FFM were also assessed via bioelectrical impedance analysis (BIA) using the InBody S10 machine, a lightweight portable scale that uses tetra-polar BIA with four electrodes at the arms and feet. Body composition estimates were taken from 504 children (111 children in lying position, 390 children in sitting position and 3 children in standing position) whose body weights were more than 5 kgs at the time of enrolment. Children wearing only light clothing were asked to lie down or sit or stand and electrodes were fitted to ensure optimal contact according to the device manufacturer’s instructions.

As per the manufacturer’s instructions, before conducting BIA assessment, it was ensured that the child remains in the sitting, standing or lying postures, in which the assessment was conducted, for about 10 minutes, so that body water may be dispersed evenly inside the body. The child’s arms did not touch the trunk part of body and were spread around 15-degree angle away from trunk. The child’s thighs did not touch each other, and legs were spread to shoulder width. When the assessment was conducted, we also made sure that the child’s bare feet do not touch the floor by putting a mat that does not conduct electricity. The machine was placed on a flat surface. Before the assessment, the child’s hands and feet were wiped with water to make them wet to get an accurate result. Any metallic devices which the child was wearing were removed and it was ensured the child has not eaten anything, preferably at least 1 hour before the test. Parameters of weight, height/length and age were entered in the InBody S10 machine. The reports on body composition parameters such as FM and FFM were autogenerated by the machine.

The Weighing scale (SECA 354: Capacity: 20 kg, Graduation: 5 g < 10 kg > 10 g) was used to assess body weight, to the nearest 0.1 kg of all children. Each child was measured twice. If the difference between first and second measurements were more than 100 g, then the third measurement was taken. The average of the two nearest measurements was taken as final weight of the child. Body length was taken for children aged 6 months to 2 years to the nearest 0.1 cm with each child lying straight on the infantometer (SECA 417: Measuring range: 10-100 cm/4–39 in., Graduation: 1 mm). Body height was taken for children aged above 2 years to the nearest 0.1 cm with each child standing with back erect and shoulders against a stadiometer (SECA 213: Measuring range: 20-205 cm/8–81 in., Graduation: 1 mm). Length or height of each child was also measured twice. If the difference in two measurements were found more than 0.7 cm then the third measurement was taken. The average of two nearest measurements were taken as final length/height of the child. Mid-upper arm circumference (MUAC) was taken of all children using non-stretchable fibre glass MUAC tape (Range upto 26.5 cm graduated with 1 mm, colour coded; Red: from 0 to 11.5 cm, Yellow: from 11.5 to 12.5 cm and Green: from 12.5 to 26.5 cm). Sex-specific weight-for-height, height-for-age, weight-for-age, body mass index (BMI) and MUAC z-scores were then computed from the World Health Organization (WHO) growth reference data [21]. The training and anthropometric standardisation exercise with the data collection staff and quality control measures were described elsewhere [20].

FM and FFM were taken as body composition parameters. As age was different of children included in the study, body composition parameters were adjusted for length/height to compute Fat Mass Index (FMI) and Fat Free Mass Index (FFMI). FMI and FFMI were taken as main body composition outcomes for the analyses [22].

Asset ownership (ownership of 25 durable assets such as electricity, mattress, pressure cooker, chair, table, cot/bed, electric fan, radio/transistor, television, sewing machine, mobile phone, landline phone, internet, computer, refrigerator, air-conditioner/cooler, washing machine, watch/clock, bicycle, motorcycle/scooter, animal-drawn cart, car, water pump, thresher and tractor) was measured with an index constructed using principal component analysis (PCA). Accordingly, a categorical variable for the household wealth index from 1 to 5 was created with 1 is categorised as Household Wealth Quintile 1 and was considered as poorest quintile and 5 was categorised as Household Wealth Quintile 5 as the least poor/richest quintile. Social/Caste status of the studied children were divided into two groups: Scheduled Tribe (ST) or Scheduled Caste (SC) and non-Scheduled Tribe/Scheduled Caste categories. The STs and SCs are officially designated groups of people and among the most disadvantaged socio-economic groups in India. Age of the studied children were divided into two groups: children aged 6–23 months and children aged 24–59 months.

The analyses include descriptive statistics (proportion and mean) to describe characteristics of the sample. Comparison of mean/average for anthropometry and body composition indices with age-group and sex-group were done using t-tests while with household wealth index using ANOVA test. Individual bivariate associations of anthropometric parameters (WHZ, WAZ, HAZ, BMIZ and MUACZ) with body composition parameters (FMI and FFMI) were assessed using Pearson Correlation analysis. Multiple linear regression models separately with WHZ, WAZ, HAZ,BMIZ and MUACZ as dependent variables were conducted to test their associations with each of these separate body composition estimates (FMI and FFMI) after adjusting for child’s age and sex, household wealth index and social/caste status. Associations of children with multiple deficits {both SAM (WHZ < -3 only criteria) and Severely Stunted (HAZ < -3)} separately with FMI and FFMI were tested to assess whether body composition indices are different in children with both severe wasting and severe stunting compared to children with severe wasting only. All statistical tests were performed using SPSS® 26 (IBM Corporation; Armonk, NY, USA) with statistical significance defined as p < 0.05.

Source link

Stay in Touch

To follow the best weight loss journeys, success stories and inspirational interviews with the industry's top coaches and specialists. Start changing your life today!


Related Articles