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Abstract：
Undernutrition in infants and young children is a major problem leading to millions of deaths every year. The objective of this study was to provide a new model for body composition assessment using near-infrared reflectance (NIR) to help correctly identify low body fat in infants and young children. Eligibility included infants and young children from 3-24 months of age. Fat mass values were collected from dual-energy x-ray absorptiometry (DXA), deuterium dilution (DD) and skin fold thickness (SFT) measurements, which were then compared to NIR predicted values. Anthropometric measures were also obtained. We developed a model using NIR to predict fat mass and validated it against a multi compartment model. One hundred and sixty-four infants and young children were included. The evaluation of the NIR model against the multi compartment reference method achieved an r value of 0.885, 0.904, and 0.818 for age groups 3-24 months (all subjects), 0-6 months, and 7-24 months, respectively. Compared with conventional methods such as SFT, body mass index and anthropometry, performance was best with NIR. NIR offers an affordable and portable way to measure fat mass in South African infants for growth monitoring in low-middle income settings.
摘要：
婴幼儿营养不良是一个主要问题，每年导致数百万人死亡。这项研究的目的是提供一种使用近红外反射（NIR）进行身体成分评估的新模型，以帮助正确识别婴幼儿的低体脂。资格包括3-24个月大的婴儿和幼儿。从双能X射线吸收法(DXA)收集脂肪质量值,氘稀释（DD）和皮肤褶皱厚度（SFT）测量，然后将其与NIR预测值进行比较。还获得了人体测量。我们使用NIR开发了一个模型来预测脂肪量，并针对多室模型进行了验证。其中包括164名婴幼儿。对于3-24个月的年龄组（所有受试者），NIR模型相对于多隔室参考方法的评估达到了0.885、0.904和0.818的r值，0-6个月,7-24个月,分别。与SFT等常规方法相比，身体质量指数和人体测量学，NIR的性能最好。NIR提供了一种经济实惠且便携的方法来测量南非婴儿的脂肪量，以便在中低收入环境中进行生长监测。
