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. 2016 Aug;36(8):835-44.
doi: 10.1016/j.nutres.2016.04.007. Epub 2016 Apr 24.

Mid-upper arm circumference is associated with biochemically determined nutritional status indicators among adolescent girls in Central Mozambique

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Mid-upper arm circumference is associated with biochemically determined nutritional status indicators among adolescent girls in Central Mozambique

Sangita Kulathinal et al. Nutr Res. 2016 Aug.

Abstract

Biochemically determined nutritional status measurements in low-income countries are often too expensive. Therefore, we hypothesized that some anthropometrical or functional measurements (handgrip) could reflect nutritional status measured by specific biochemical indicators. We did a population-based study from 1 urban area and 2 rural districts in Zambézia Province of Mozambique. The participants (n=386) were non-pregnant adolescent girls between 15 and 18 years of age. 96% had a normal BMI-for-age score. Weight and mid-upper arm circumference (MUAC) were highly correlated (r>0.8) with each other and with total body muscle mass, body mass index (BMI), and with waist circumference, as well as with skinfolds (r>0.6). Upper and total arm lengths were correlated (r>0.7) with height and with each other, and right and left handgrip were correlated only with each other, as were triceps and subscapular skinfolds (r>0.7). Serum albumin correlated negatively with waist circumference (P<.001) and positively with MUAC (P=.007). Stepwise regressions showed that waist circumference, MUAC, weight, and handgrip were important nutritional status indicators in the models using hemoglobin, serum albumin, ferritin, zinc, and plasma retinol concentrations as dependent variables. MUAC could be a valuable anthropometric marker of the overall nutritional status of adolescent girls in low-income countries. When nutrition transition proceeds, waist circumference together with MUAC could form tools for the prediction of worsening of nutritional status.

Keywords: Adolescent girls; Anthropometric measurements; Mozambique; Nutrition biomarkers; Regression analysis.

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