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3 至 24 月龄婴儿身体成分的人体测量学预测模型:一项多中心国际研究。

Anthropometric prediction models of body composition in 3 to 24month old infants: a multicenter international study.

机构信息

University of Colombo, Colombo, Sri Lanka.

The Aga Khan University, Karachi, Pakistan.

出版信息

Eur J Clin Nutr. 2024 Nov;78(11):943-951. doi: 10.1038/s41430-024-01501-0. Epub 2024 Sep 20.

Abstract

BACKGROUND

Accurate assessment of body composition during infancy is an important marker of early growth. This study aimed to develop anthropometric models to predict body composition in 3-24-month-old infants from diverse socioeconomic settings and ethnic groups.

METHODS

An observational, longitudinal, prospective, multi-country study of infants from 3 to 24 months with body composition assessed at three monthly intervals using deuterium dilution (DD) and anthropometry. Linear mixed modelling was utilized to generate sex-specific fat mass (FM) and fat-free mass (FFM) prediction equations, using length(m), weight-for-length (kg/m), triceps and subscapular skinfolds and South Asian ethnicity as variables. The study sample consisted of 1896 (942 measurements from 310 girls) training data sets, 941 (441 measurements from 154 girls) validation data sets of 3-24 months from Brazil, Pakistan, South Africa and Sri Lanka. The external validation group (test) comprised 349 measurements from 250 (185 from 124 girls) infants 3-6 months of age from South Africa, Australia and India.

RESULTS

Sex-specific equations for three age categories (3-9 months; 10-18 months; 19-24 months) were developed, validated on same population and externally validated. Root mean squared error (RMSE) was similar between training, validation and test data for assessment of FM and FFM in boys and in girls. RMSPE and mean absolute percentage error (MAPE) were higher in validation compared to test data for predicting FM, however, in the assessment of FFM, both measures were lower in validation data. RMSE for test data from South Africa (M/F-0.46/0.45 kg) showed good agreement with validation data for assessment of FFM compared to Australia (M/F-0.51/0.33 kg) and India(M/F-0.77/0.80 kg).

CONCLUSIONS

Anthropometry-based FFM prediction equations provide acceptable results. Assessments based on equations developed on similar populations are more applicable than those developed from a different population.

摘要

背景

准确评估婴儿期的身体成分是早期生长的重要指标。本研究旨在为来自不同社会经济背景和种族群体的 3 至 24 个月大的婴儿建立预测身体成分的人体测量学模型。

方法

这是一项观察性、纵向、前瞻性、多国研究,对 3 至 24 个月大的婴儿进行了为期三个月一次的身体成分评估,使用氘稀释(DD)和人体测量法。线性混合模型用于生成性别特异性脂肪量(FM)和去脂体重(FFM)预测方程,使用长度(m)、体重/长度(kg/m)、三头肌和肩胛下皮褶以及南亚种族作为变量。研究样本包括 1896 名(来自 310 名女孩的 942 次测量)训练数据集,来自巴西、巴基斯坦、南非和斯里兰卡的 941 名(来自 154 名女孩的 441 次测量)3 至 24 个月的验证数据集。外部验证组(测试)由来自南非、澳大利亚和印度的 250 名婴儿(来自 124 名女孩的 185 名)3 至 6 个月大的 349 次测量组成。

结果

为三个年龄组(3-9 个月;10-18 个月;19-24 个月)开发了性别特异性方程,在同一人群中进行了验证,并进行了外部验证。男孩和女孩的 FM 和 FFM 评估中,训练、验证和测试数据的均方根误差(RMSE)相似。与测试数据相比,预测 FM 时验证数据的 RMSPE 和平均绝对百分比误差(MAPE)更高,但评估 FFM 时,验证数据的这两个指标都较低。与澳大利亚(M/F-0.51/0.33kg)和印度(M/F-0.77/0.80kg)相比,南非(M/F-0.46/0.45kg)测试数据的 RMSE 对 FFM 评估显示出与验证数据的良好一致性。

结论

基于人体测量的 FFM 预测方程提供了可接受的结果。基于类似人群开发的评估比基于不同人群开发的评估更适用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5f06/11537960/8d5757d32571/41430_2024_1501_Fig1_HTML.jpg

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