Armuda Project 1

1. Nutrient intake and systemic metabolism impact normal growth and development in children.
2. While anthropometric measures are used to define stunting, wasting associated with malnutrition, increasing evidence has illustrated their limitations.
3. Information on body composition and systemic-metabolism in children across populations is lacking in India.
4. Population-wide national surveys, NFHS and CNNS, have clearly illustrated geographic and population-specific prevalence of stunting, wasting and obesity in children. Evaluation of stunting/wasting and obesity are currently based on WHO standards of child growth and anthropometric measures.
5. Increasing evidence in the field have clearly highlighted these as being inaccurate at estimating physiological fitness/deficits.
6. Further, most of the WHO standards have been derived largely from Caucasian populations/studies and do not take into account genetic, ethnic and dietary variations, which are otherwise well-known to impact childhood growth and development.
7. Among others, body composition measures (fat and lean mass) analyses have emerged as more reliable estimates of fitness, given the insufficiencies of anthropometric measurements in determining physiological fitness.
8. This has laid to the potential risk of either underestimating or overestimating the double burden of malnutrition.
9. This might lead to inappropriate or unnecessary interventions making populations vulnerable to develop non-communicable diseases.
10. Therefore, there is an urgent need to define normative baselines of childhood development in the country. Specifically, it is necessary to capture population-specific variations in normal childhood development, beyond anthropometric measures.

To generate normative baseline data on growth, body composition and metabolic deficiencies among children and adolescents (4-16y old)

Preliminary outcomes:

1. Body composition
2. Metabolic parameters

We will also identify biomarkers that will help define normal child-growth/development beyond anthropometry.

1. Anthropometry
2. Body composition
3. Macro and micronutrient intake estimation
4. Metabolic parameters
5. Lipids, glucose, hemogram

Data will be presented as mean, standard deviation (SD) and range. Statistical significance of the difference between measurements obtained by three methods (anthropometry, BIA and dilution) will be tested by the paired t-test. Difference between group means will be tested by the independent t-test. The body fat measurements by anthropometry, BIA and dilution will be correlated) by Pearson’s method. The bias and limits of agreement will be assessed by Bland Altman plots. We will also use multiple linear regression to study the relationship between body fat measurements by various methods. Additional statistical measures/analyses will be utilized for phase-2 especially to generate phenome-for-age-Z scores (PAZ).